diff --git a/qasper-0013/instruction.md b/qasper-0013/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..ab37dde78ef4f50bf25df309d3767eca4bdf315a --- /dev/null +++ b/qasper-0013/instruction.md @@ -0,0 +1,134 @@ +Name of Paper: Community Identity and User Engagement in a Multi-Community Landscape + +Question: How do the various social phenomena examined manifest in different types of communities? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "A typology of community identity", + "Overview and intuition", + "Language-based formalization", + "Community-level measures", + "Applying the typology to Reddit", + "Community identity and user retention", + "Community-type and monthly retention", + "Community-type and user tenure", + "Community identity and acculturation", + "Community identity and content affinity", + "Further related work", + "Conclusion and future work", + "Acknowledgements" + ], + "paragraphs": [ + [ + "\u201cIf each city is like a game of chess, the day when I have learned the rules, I shall finally possess my empire, even if I shall never succeed in knowing all the cities it contains.\u201d", + "", + "\u2014 Italo Calvino, Invisible Cities", + "A community's identity\u2014defined through the common interests and shared experiences of its users\u2014shapes various facets of the social dynamics within it BIBREF0 , BIBREF1 , BIBREF2 . Numerous instances of this interplay between a community's identity and social dynamics have been extensively studied in the context of individual online communities BIBREF3 , BIBREF4 , BIBREF5 . However, the sheer variety of online platforms complicates the task of generalizing insights beyond these isolated, single-community glimpses. A new way to reason about the variation across multiple communities is needed in order to systematically characterize the relationship between properties of a community and the dynamics taking place within.", + "One especially important component of community dynamics is user engagement. We can aim to understand why users join certain communities BIBREF6 , what factors influence user retention BIBREF7 , and how users react to innovation BIBREF5 . While striking patterns of user engagement have been uncovered in prior case studies of individual communities BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , we do not know whether these observations hold beyond these cases, or when we can draw analogies between different communities. Are there certain types of communities where we can expect similar or contrasting engagement patterns?", + "To address such questions quantitatively we need to provide structure to the diverse and complex space of online communities. Organizing the multi-community landscape would allow us to both characterize individual points within this space, and reason about systematic variations in patterns of user engagement across the space.", + "Present work: Structuring the multi-community space. In order to systematically understand the relationship between community identityand user engagement we introduce a quantitative typology of online communities. Our typology is based on two key aspects of community identity: how distinctive\u2014or niche\u2014a community's interests are relative to other communities, and how dynamic\u2014or volatile\u2014these interests are over time. These axes aim to capture the salience of a community's identity and dynamics of its temporal evolution.", + "Our main insight in implementing this typology automatically and at scale is that the language used within a community can simultaneously capture how distinctive and dynamic its interests are. This language-based approach draws on a wealth of literature characterizing linguistic variation in online communities and its relationship to community and user identity BIBREF16 , BIBREF5 , BIBREF17 , BIBREF18 , BIBREF19 . Basing our typology on language is also convenient since it renders our framework immediately applicable to a wide variety of online communities, where communication is primarily recorded in a textual format.", + "Using our framework, we map almost 300 Reddit communities onto the landscape defined by the two axes of our typology (Section SECREF2 ). We find that this mapping induces conceptually sound categorizations that effectively capture key aspects of community-level social dynamics. In particular, we quantitatively validate the effectiveness of our mapping by showing that our two-dimensional typology encodes signals that are predictive of community-level rates of user retention, complementing strong activity-based features.", + "Engagement and community identity. We apply our framework to understand how two important aspects of user engagement in a community\u2014the community's propensity to retain its users (Section SECREF3 ), and its permeability to new members (Section SECREF4 )\u2014vary according to the type of collective identity it fosters. We find that communities that are characterized by specialized, constantly-updating content have higher user retention rates, but also exhibit larger linguistic gaps that separate newcomers from established members.", + "More closely examining factors that could contribute to this linguistic gap, we find that especially within distinctive communities, established users have an increased propensity to engage with the community's specialized content, compared to newcomers (Section SECREF5 ). Interestingly, while established members of distinctive communities more avidly respond to temporal updates than newcomers, in more generic communities it is the outsiders who engage more with volatile content, perhaps suggesting that such content may serve as an entry-point to the community (but not necessarily a reason to stay). Such insights into the relation between collective identity and user engagement can be informative to community maintainers seeking to better understand growth patterns within their online communities.", + "More generally, our methodology stands as an example of how sociological questions can be addressed in a multi-community setting. In performing our analyses across a rich variety of communities, we reveal both the diversity of phenomena that can occur, as well as the systematic nature of this diversity." + ], + [ + "A community's identity derives from its members' common interests and shared experiences BIBREF15 , BIBREF20 . In this work, we structure the multi-community landscape along these two key dimensions of community identity: how distinctive a community's interests are, and how dynamic the community is over time.", + "We now proceed to outline our quantitative typology, which maps communities along these two dimensions. We start by providing an intuition through inspecting a few example communities. We then introduce a generalizable language-based methodology and use it to map a large set of Reddit communities onto the landscape defined by our typology of community identity." + ], + [ + "In order to illustrate the diversity within the multi-community space, and to provide an intuition for the underlying structure captured by the proposed typology, we first examine a few example communities and draw attention to some key social dynamics that occur within them.", + "We consider four communities from Reddit: in Seahawks, fans of the Seahawks football team gather to discuss games and players; in BabyBumps, expecting mothers trade advice and updates on their pregnancy; Cooking consists of recipe ideas and general discussion about cooking; while in pics, users share various images of random things (like eels and hornets). We note that these communities are topically contrasting and foster fairly disjoint user bases. Additionally, these communities exhibit varied patterns of user engagement. While Seahawks maintains a devoted set of users from month to month, pics is dominated by transient users who post a few times and then depart.", + "Discussions within these communities also span varied sets of interests. Some of these interests are more specific to the community than others: risotto, for example, is seldom a discussion point beyond Cooking. Additionally, some interests consistently recur, while others are specific to a particular time: kitchens are a consistent focus point for cooking, but mint is only in season during spring. Coupling specificity and consistency we find interests such as easter, which isn't particularly specific to BabyBumps but gains prominence in that community around Easter (see Figure FIGREF3 .A for further examples).", + "These specific interests provide a window into the nature of the communities' interests as a whole, and by extension their community identities. Overall, discussions in Cooking focus on topics which are highly distinctive and consistently recur (like risotto). In contrast, discussions in Seahawks are highly dynamic, rapidly shifting over time as new games occur and players are traded in and out. In the remainder of this section we formally introduce a methodology for mapping communities in this space defined by their distinctiveness and dynamicity (examples in Figure FIGREF3 .B)." + ], + [ + "Our approach follows the intuition that a distinctive community will use language that is particularly specific, or unique, to that community. Similarly, a dynamic community will use volatile language that rapidly changes across successive windows of time. To capture this intuition automatically, we start by defining word-level measures of specificity and volatility. We then extend these word-level primitives to characterize entire comments, and the community itself.", + "Our characterizations of words in a community are motivated by methodology from prior literature that compares the frequency of a word in a particular setting to its frequency in some background distribution, in order to identify instances of linguistic variation BIBREF21 , BIBREF19 . Our particular framework makes this comparison by way of pointwise mutual information (PMI).", + "In the following, we use INLINEFORM0 to denote one community within a set INLINEFORM1 of communities, and INLINEFORM2 to denote one time period within the entire history INLINEFORM3 of INLINEFORM4 . We account for temporal as well as inter-community variation by computing word-level measures for each time period of each community's history, INLINEFORM5 . Given a word INLINEFORM6 used within a particular community INLINEFORM7 at time INLINEFORM8 , we define two word-level measures:", + "Specificity. We quantify the specificity INLINEFORM0 of INLINEFORM1 to INLINEFORM2 by calculating the PMI of INLINEFORM3 and INLINEFORM4 , relative to INLINEFORM5 , INLINEFORM6 ", + "where INLINEFORM0 is INLINEFORM1 's frequency in INLINEFORM2 . INLINEFORM3 is specific to INLINEFORM4 if it occurs more frequently in INLINEFORM5 than in the entire set INLINEFORM6 , hence distinguishing this community from the rest. A word INLINEFORM7 whose occurrence is decoupled from INLINEFORM8 , and thus has INLINEFORM9 close to 0, is said to be generic.", + "We compute values of INLINEFORM0 for each time period INLINEFORM1 in INLINEFORM2 ; in the above description we drop the time-based subscripts for clarity.", + "Volatility. We quantify the volatility INLINEFORM0 of INLINEFORM1 to INLINEFORM2 as the PMI of INLINEFORM3 and INLINEFORM4 relative to INLINEFORM5 , the entire history of INLINEFORM6 : INLINEFORM7 ", + "A word INLINEFORM0 is volatile at time INLINEFORM1 in INLINEFORM2 if it occurs more frequently at INLINEFORM3 than in the entire history INLINEFORM4 , behaving as a fad within a small window of time. A word that occurs with similar frequency across time, and hence has INLINEFORM5 close to 0, is said to be stable.", + "Extending to utterances. Using our word-level primitives, we define the specificity of an utterance INLINEFORM0 in INLINEFORM1 , INLINEFORM2 as the average specificity of each word in the utterance. The volatility of utterances is defined analogously.", + "" + ], + [ + "Having described these word-level measures, we now proceed to establish the primary axes of our typology:", + "Distinctiveness. A community with a very distinctive identity will tend to have distinctive interests, expressed through specialized language. Formally, we define the distinctiveness of a community INLINEFORM0 as the average specificity of all utterances in INLINEFORM1 . We refer to a community with a less distinctive identity as being generic.", + "Dynamicity. A highly dynamic community constantly shifts interests from one time window to another, and these temporal variations are reflected in its use of volatile language. Formally, we define the dynamicity of a community INLINEFORM0 as the average volatility of all utterances in INLINEFORM1 . We refer to a community whose language is relatively consistent throughout time as being stable.", + "In our subsequent analyses, we focus mostly on examing the average distinctiveness and dynamicity of a community over time, denoted INLINEFORM0 and INLINEFORM1 ." + ], + [ + "We now explain how our typology can be applied to the particular setting of Reddit, and describe the overall behaviour of our linguistic axes in this context.", + "Dataset description. Reddit is a popular website where users form and participate in discussion-based communities called subreddits. Within these communities, users post content\u2014such as images, URLs, or questions\u2014which often spark vibrant lengthy discussions in thread-based comment sections.", + "The website contains many highly active subreddits with thousands of active subscribers. These communities span an extremely rich variety of topical interests, as represented by the examples described earlier. They also vary along a rich multitude of structural dimensions, such as the number of users, the amount of conversation and social interaction, and the social norms determining which types of content become popular. The diversity and scope of Reddit's multicommunity ecosystem make it an ideal landscape in which to closely examine the relation between varying community identities and social dynamics.", + "Our full dataset consists of all subreddits on Reddit from January 2013 to December 2014, for which there are at least 500 words in the vocabulary used to estimate our measures, in at least 4 months of the subreddit's history. We compute our measures over the comments written by users in a community in time windows of months, for each sufficiently active month, and manually remove communities where the bulk of the contributions are in a foreign language. This results in 283 communities ( INLINEFORM0 ), for a total of 4,872 community-months ( INLINEFORM1 ).", + "Estimating linguistic measures. We estimate word frequencies INLINEFORM0 , and by extension each downstream measure, in a carefully controlled manner in order to ensure we capture robust and meaningful linguistic behaviour. First, we only consider top-level comments which are initial responses to a post, as the content of lower-level responses might reflect conventions of dialogue more than a community's high-level interests. Next, in order to prevent a few highly active users from dominating our frequency estimates, we count each unique word once per user, ignoring successive uses of the same word by the same user. This ensures that our word-level characterizations are not skewed by a small subset of highly active contributors.", + "In our subsequent analyses, we will only look at these measures computed over the nouns used in comments. In principle, our framework can be applied to any choice of vocabulary. However, in the case of Reddit using nouns provides a convenient degree of interpretability. We can easily understand the implication of a community preferentially mentioning a noun such as gamer or feminist, but interpreting the overuse of verbs or function words such as take or of is less straightforward. Additionally, in focusing on nouns we adopt the view emphasized in modern \u201cthird wave\u201d accounts of sociolinguistic variation, that stylistic variation is inseparable from topical content BIBREF23 . In the case of online communities, the choice of what people choose to talk about serves as a primary signal of social identity. That said, a typology based on more purely stylistic differences is an interesting avenue for future work.", + "Accounting for rare words. One complication when using measures such as PMI, which are based off of ratios of frequencies, is that estimates for very infrequent words could be overemphasized BIBREF24 . Words that only appear a few times in a community tend to score at the extreme ends of our measures (e.g. as highly specific or highly generic), obfuscating the impact of more frequent words in the community. To address this issue, we discard the long tail of infrequent words in our analyses, using only the top 5th percentile of words, by frequency within each INLINEFORM0 , to score comments and communities.", + "Typology output on Reddit. The distribution of INLINEFORM0 and INLINEFORM1 across Reddit communities is shown in Figure FIGREF3 .B, along with examples of communities at the extremes of our typology. We find that interpretable groupings of communities emerge at various points within our axes. For instance, highly distinctive and dynamic communities tend to focus on rapidly-updating interests like sports teams and games, while generic and consistent communities tend to be large \u201clink-sharing\u201d hubs where users generally post content with no clear dominating themes. More examples of communities at the extremes of our typology are shown in Table TABREF9 .", + "We note that these groupings capture abstract properties of a community's content that go beyond its topic. For instance, our typology relates topically contrasting communities such as yugioh (which is about a popular trading card game) and Seahawks through the shared trait that their content is particularly distinctive. Additionally, the axes can clarify differences between topically similar communities: while startrek and thewalkingdead both focus on TV shows, startrek is less dynamic than the median community, while thewalkingdead is among the most dynamic communities, as the show was still airing during the years considered." + ], + [ + "We have seen that our typology produces qualitatively satisfying groupings of communities according to the nature of their collective identity. This section shows that there is an informative and highly predictive relationship between a community's position in this typology and its user engagement patterns. We find that communities with distinctive and dynamic identities have higher rates of user engagement, and further show that a community's position in our identity-based landscape holds important predictive information that is complementary to a strong activity baseline.", + "In particular user retention is one of the most crucial aspects of engagement and is critical to community maintenance BIBREF2 . We quantify how successful communities are at retaining users in terms of both short and long-term commitment. Our results indicate that rates of user retention vary drastically, yet systematically according to how distinctive and dynamic a community is (Figure FIGREF3 ).", + "We find a strong, explanatory relationship between the temporal consistency of a community's identity and rates of user engagement: dynamic communities that continually update and renew their discussion content tend to have far higher rates of user engagement. The relationship between distinctiveness and engagement is less universal, but still highly informative: niche communities tend to engender strong, focused interest from users at one particular point in time, though this does not necessarily translate into long-term retention." + ], + [ + "We find that dynamic communities, such as Seahawks or starcraft, have substantially higher rates of monthly user retention than more stable communities (Spearman's INLINEFORM0 = 0.70, INLINEFORM1 0.001, computed with community points averaged over months; Figure FIGREF11 .A, left). Similarly, more distinctive communities, like Cooking and Naruto, exhibit moderately higher monthly retention rates than more generic communities (Spearman's INLINEFORM2 = 0.33, INLINEFORM3 0.001; Figure FIGREF11 .A, right).", + "Monthly retention is formally defined as the proportion of users who contribute in month INLINEFORM0 and then return to contribute again in month INLINEFORM1 . Each monthly datapoint is treated as unique and the trends in Figure FIGREF11 show 95% bootstrapped confidence intervals, cluster-resampled at the level of subreddit BIBREF25 , to account for differences in the number of months each subreddit contributes to the data.", + "Importantly, we find that in the task of predicting community-level user retention our identity-based typology holds additional predictive value on top of strong baseline features based on community-size (# contributing users) and activity levels (mean # contributions per user), which are commonly used for churn prediction BIBREF7 . We compared out-of-sample predictive performance via leave-one-community-out cross validation using random forest regressors with ensembles of size 100, and otherwise default hyperparameters BIBREF26 . A model predicting average monthly retention based on a community's average distinctiveness and dynamicity achieves an average mean squared error ( INLINEFORM0 ) of INLINEFORM1 and INLINEFORM2 , while an analogous model predicting based on a community's size and average activity level (both log-transformed) achieves INLINEFORM4 and INLINEFORM5 . The difference between the two models is not statistically significant ( INLINEFORM6 , Wilcoxon signed-rank test). However, combining features from both models results in a large and statistically significant improvement over each independent model ( INLINEFORM7 , INLINEFORM8 , INLINEFORM9 Bonferroni-corrected pairwise Wilcoxon tests). These results indicate that our typology can explain variance in community-level retention rates, and provides information beyond what is present in standard activity-based features." + ], + [ + "As with monthly retention, we find a strong positive relationship between a community's dynamicity and the average number of months that a user will stay in that community (Spearman's INLINEFORM0 = 0.41, INLINEFORM1 0.001, computed over all community points; Figure FIGREF11 .B, left). This verifies that the short-term trend observed for monthly retention translates into longer-term engagement and suggests that long-term user retention might be strongly driven by the extent to which a community continually provides novel content. Interestingly, there is no significant relationship between distinctiveness and long-term engagement (Spearman's INLINEFORM2 = 0.03, INLINEFORM3 0.77; Figure FIGREF11 .B, right). Thus, while highly distinctive communities like RandomActsOfMakeup may generate focused commitment from users over a short period of time, such communities are unlikely to retain long-term users unless they also have sufficiently dynamic content.", + "To measure user tenures we focused on one slice of data (May, 2013) and measured how many months a user spends in each community, on average\u2014the average number of months between a user's first and last comment in each community. We have activity data up until May 2015, so the maximum tenure is 24 months in this set-up, which is exceptionally long relative to the average community member (throughout our entire data less than INLINEFORM0 of users have tenures of more than 24 months in any community)." + ], + [ + "The previous section shows that there is a strong connection between the nature of a community's identity and its basic user engagement patterns. In this section, we probe the relationship between a community's identity and how permeable, or accessible, it is to outsiders.", + "We measure this phenomenon using what we call the acculturation gap, which compares the extent to which engaged vs. non-engaged users employ community-specific language. While previous work has found this gap to be large and predictive of future user engagement in two beer-review communities BIBREF5 , we find that the size of the acculturation gap depends strongly on the nature of a community's identity, with the gap being most pronounced in stable, highly distinctive communities (Figure FIGREF13 ).", + "This finding has important implications for our understanding of online communities. Though many works have analyzed the dynamics of \u201clinguistic belonging\u201d in online communities BIBREF16 , BIBREF28 , BIBREF5 , BIBREF17 , our results suggest that the process of linguistically fitting in is highly contingent on the nature of a community's identity. At one extreme, in generic communities like pics or worldnews there is no distinctive, linguistic identity for users to adopt.", + "To measure the acculturation gap for a community, we follow Danescu-Niculescu-Mizil et al danescu-niculescu-mizilno2013 and build \u201csnapshot language models\u201d (SLMs) for each community, which capture the linguistic state of a community at one point of time. Using these language models we can capture how linguistically close a particular utterance is to the community by measuring the cross-entropy of this utterance relative to the SLM: DISPLAYFORM0 ", + "where INLINEFORM0 is the probability assigned to bigram INLINEFORM1 from comment INLINEFORM2 in community-month INLINEFORM3 . We build the SLMs by randomly sampling 200 active users\u2014defined as users with at least 5 comments in the respective community and month. For each of these 200 active users we select 5 random 10-word spans from 5 unique comments. To ensure robustness and maximize data efficiency, we construct 100 SLMs for each community-month pair that has enough data, bootstrap-resampling from the set of active users.", + "We compute a basic measure of the acculturation gap for a community-month INLINEFORM0 as the relative difference of the cross-entropy of comments by users active in INLINEFORM1 with that of singleton comments by outsiders\u2014i.e., users who only ever commented once in INLINEFORM2 , but who are still active in Reddit in general: DISPLAYFORM0 ", + " INLINEFORM0 denotes the distribution over singleton comments, INLINEFORM1 denotes the distribution over comments from users active in INLINEFORM2 , and INLINEFORM3 the expected values of the cross-entropy over these respective distributions. For each bootstrap-sampled SLM we compute the cross-entropy of 50 comments by active users (10 comments from 5 randomly sampled active users, who were not used to construct the SLM) and 50 comments from randomly-sampled outsiders.", + "Figure FIGREF13 .A shows that the acculturation gap varies substantially with how distinctive and dynamic a community is. Highly distinctive communities have far higher acculturation gaps, while dynamicity exhibits a non-linear relationship: relatively stable communities have a higher linguistic `entry barrier', as do very dynamic ones. Thus, in communities like IAmA (a general Q&A forum) that are very generic, with content that is highly, but not extremely dynamic, outsiders are at no disadvantage in matching the community's language. In contrast, the acculturation gap is large in stable, distinctive communities like Cooking that have consistent community-specific language. The gap is also large in extremely dynamic communities like Seahawks, which perhaps require more attention or interest on the part of active users to keep up-to-date with recent trends in content.", + "These results show that phenomena like the acculturation gap, which were previously observed in individual communities BIBREF28 , BIBREF5 , cannot be easily generalized to a larger, heterogeneous set of communities. At the same time, we see that structuring the space of possible communities enables us to observe systematic patterns in how such phenomena vary." + ], + [ + "Through the acculturation gap, we have shown that communities exhibit large yet systematic variations in their permeability to outsiders. We now turn to understanding the divide in commenting behaviour between outsiders and active community members at a finer granularity, by focusing on two particular ways in which such gaps might manifest among users: through different levels of engagement with specific content and with temporally volatile content.", + "Echoing previous results, we find that community type mediates the extent and nature of the divide in content affinity. While in distinctive communities active members have a higher affinity for both community-specific content and for highly volatile content, the opposite is true for generic communities, where it is the outsiders who engage more with volatile content.", + "We quantify these divides in content affinity by measuring differences in the language of the comments written by active users and outsiders. Concretely, for each community INLINEFORM0 , we define the specificity gap INLINEFORM1 as the relative difference between the average specificity of comments authored by active members, and by outsiders, where these measures are macroaveraged over users. Large, positive INLINEFORM2 then occur in communities where active users tend to engage with substantially more community-specific content than outsiders.", + "We analogously define the volatility gap INLINEFORM0 as the relative difference in volatilities of active member and outsider comments. Large, positive values of INLINEFORM1 characterize communities where active users tend to have more volatile interests than outsiders, while negative values indicate communities where active users tend to have more stable interests.", + "We find that in 94% of communities, INLINEFORM0 , indicating (somewhat unsurprisingly) that in almost all communities, active users tend to engage with more community-specific content than outsiders. However, the magnitude of this divide can vary greatly: for instance, in Homebrewing, which is dedicated to brewing beer, the divide is very pronounced ( INLINEFORM1 0.33) compared to funny, a large hub where users share humorous content ( INLINEFORM2 0.011).", + "The nature of the volatility gap is comparatively more varied. In Homebrewing ( INLINEFORM0 0.16), as in 68% of communities, active users tend to write more volatile comments than outsiders ( INLINEFORM1 0). However, communities like funny ( INLINEFORM2 -0.16), where active users contribute relatively stable comments compared to outsiders ( INLINEFORM3 0), are also well-represented on Reddit.", + "To understand whether these variations manifest systematically across communities, we examine the relationship between divides in content affinity and community type. In particular, following the intuition that active users have a relatively high affinity for a community's niche, we expect that the distinctiveness of a community will be a salient mediator of specificity and volatility gaps. Indeed, we find a strong correlation between a community's distinctiveness and its specificity gap (Spearman's INLINEFORM0 0.34, INLINEFORM1 0.001).", + "We also find a strong correlation between distinctiveness and community volatility gaps (Spearman's INLINEFORM0 0.53, INLINEFORM1 0.001). In particular, we see that among the most distinctive communities (i.e., the top third of communities by distinctiveness), active users tend to write more volatile comments than outsiders (mean INLINEFORM2 0.098), while across the most generic communities (i.e., the bottom third), active users tend to write more stable comments (mean INLINEFORM3 -0.047, Mann-Whitney U test INLINEFORM4 0.001). The relative affinity of outsiders for volatile content in these communities indicates that temporally ephemeral content might serve as an entry point into such a community, without necessarily engaging users in the long term." + ], + [ + "Our language-based typology and analysis of user engagement draws on and contributes to several distinct research threads, in addition to the many foundational studies cited in the previous sections.", + "Multicommunity studies. Our investigation of user engagement in multicommunity settings follows prior literature which has examined differences in user and community dynamics across various online groups, such as email listservs. Such studies have primarily related variations in user behaviour to structural features such as group size and volume of content BIBREF30 , BIBREF31 , BIBREF32 , BIBREF33 . In focusing on the linguistic content of communities, we extend this research by providing a content-based framework through which user engagement can be examined.", + "Reddit has been a particularly useful setting for studying multiple communities in prior work. Such studies have mostly focused on characterizing how individual users engage across a multi-community platform BIBREF34 , BIBREF35 , or on specific user engagement patterns such as loyalty to particular communities BIBREF22 . We complement these studies by seeking to understand how features of communities can mediate a broad array of user engagement patterns within them.", + "Typologies of online communities. Prior attempts to typologize online communities have primarily been qualitative and based on hand-designed categories, making them difficult to apply at scale. These typologies often hinge on having some well-defined function the community serves, such as supporting a business or non-profit cause BIBREF36 , which can be difficult or impossible to identify in massive, anonymous multi-community settings. Other typologies emphasize differences in communication platforms and other functional requirements BIBREF37 , BIBREF38 , which are important but preclude analyzing differences between communities within the same multi-community platform. Similarly, previous computational methods of characterizing multiple communities have relied on the presence of markers such as affixes in community names BIBREF35 , or platform-specific affordances such as evaluation mechanisms BIBREF39 .", + "Our typology is also distinguished from community detection techniques that rely on structural or functional categorizations BIBREF40 , BIBREF41 . While the focus of those studies is to identify and characterize sub-communities within a larger social network, our typology provides a characterization of pre-defined communities based on the nature of their identity.", + "Broader work on collective identity. Our focus on community identity dovetails with a long line of research on collective identity and user engagement, in both online and offline communities BIBREF42 , BIBREF1 , BIBREF2 . These studies focus on individual-level psychological manifestations of collective (or social) identity, and their relationship to user engagement BIBREF42 , BIBREF43 , BIBREF44 , BIBREF0 .", + "In contrast, we seek to characterize community identities at an aggregate level and in an interpretable manner, with the goal of systematically organizing the diverse space of online communities. Typologies of this kind are critical to these broader, social-psychological studies of collective identity: they allow researchers to systematically analyze how the psychological manifestations and implications of collective identity vary across diverse sets of communities." + ], + [ + "Our current understanding of engagement patterns in online communities is patched up from glimpses offered by several disparate studies focusing on a few individual communities. This work calls into attention the need for a method to systematically reason about similarities and differences across communities. By proposing a way to structure the multi-community space, we find not only that radically contrasting engagement patterns emerge in different parts of this space, but also that this variation can be at least partly explained by the type of identity each community fosters.", + "Our choice in this work is to structure the multi-community space according to a typology based on community identity, as reflected in language use. We show that this effectively explains cross-community variation of three different user engagement measures\u2014retention, acculturation and content affinity\u2014and complements measures based on activity and size with additional interpretable information. For example, we find that in niche communities established members are more likely to engage with volatile content than outsiders, while the opposite is true in generic communities. Such insights can be useful for community maintainers seeking to understand engagement patterns in their own communities.", + "One main area of future research is to examine the temporal dynamics in the multi-community landscape. By averaging our measures of distinctiveness and dynamicity across time, our present study treated community identity as a static property. However, as communities experience internal changes and respond to external events, we can expect the nature of their identity to shift as well. For instance, the relative consistency of harrypotter may be disrupted by the release of a new novel, while Seahawks may foster different identities during and between football seasons. Conversely, a community's type may also mediate the impact of new events. Moving beyond a static view of community identity could enable us to better understand how temporal phenomena such as linguistic change manifest across different communities, and also provide a more nuanced view of user engagement\u2014for instance, are communities more welcoming to newcomers at certain points in their lifecycle?", + "Another important avenue of future work is to explore other ways of mapping the landscape of online communities. For example, combining structural properties of communities BIBREF40 with topical information BIBREF35 and with our identity-based measures could further characterize and explain variations in user engagement patterns. Furthermore, extending the present analyses to even more diverse communities supported by different platforms (e.g., GitHub, StackExchange, Wikipedia) could enable the characterization of more complex behavioral patterns such as collaboration and altruism, which become salient in different multicommunity landscapes." + ], + [ + "The authors thank Liye Fu, Jack Hessel, David Jurgens and Lillian Lee for their helpful comments. This research has been supported in part by a Discovery and Innovation Research Seed Award from the Office of the Vice Provost for Research at Cornell, NSF CNS-1010921, IIS-1149837, IIS-1514268 NIH BD2K, ARO MURI, DARPA XDATA, DARPA SIMPLEX, DARPA NGS2, Stanford Data Science Initiative, SAP Stanford Graduate Fellowship, NSERC PGS-D, Boeing, Lightspeed, and Volkswagen. " + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0014/instruction.md b/qasper-0014/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..7d2e725d00b4002e28d34def783e09d3e36085a0 --- /dev/null +++ b/qasper-0014/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Community Identity and User Engagement in a Multi-Community Landscape + +Question: What patterns do they observe about how user engagement varies with the characteristics of a community? \ No newline at end of file diff --git a/qasper-0022/instruction.md b/qasper-0022/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..330d41457f8309da55fc7dd146dfa0fb9b5e9f06 --- /dev/null +++ b/qasper-0022/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Question Answering based Clinical Text Structuring Using Pre-trained Language Model + +Question: Is all text in this dataset a question, or are there unrelated sentences in between questions? \ No newline at end of file diff --git a/qasper-0025/instruction.md b/qasper-0025/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..a49243fcc1ef56238108b211bc619c0556ae9f31 --- /dev/null +++ b/qasper-0025/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Question Answering based Clinical Text Structuring Using Pre-trained Language Model + +Question: Are there privacy concerns with clinical data? \ No newline at end of file diff --git a/qasper-0040/instruction.md b/qasper-0040/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..8efe4997ff4e8c14d66f6587d13c9746d36920b5 --- /dev/null +++ b/qasper-0040/instruction.md @@ -0,0 +1,84 @@ +Name of Paper: Saliency Maps Generation for Automatic Text Summarization + +Question: How many attention layers are there in their model? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "The Task and the Model", + "Dataset and Training Task", + "The Model", + "Obtained Summaries", + "Layer-Wise Relevance Propagation", + "Mathematical Description", + "Generation of the Saliency Maps", + "Experimental results", + "First Observations", + "Validating the Attributions", + "Conclusion" + ], + "paragraphs": [ + [ + "Ever since the LIME algorithm BIBREF0 , \"explanation\" techniques focusing on finding the importance of input features in regard of a specific prediction have soared and we now have many ways of finding saliency maps (also called heat-maps because of the way we like to visualize them). We are interested in this paper by the use of such a technique in an extreme task that highlights questions about the validity and evaluation of the approach. We would like to first set the vocabulary we will use. We agree that saliency maps are not explanations in themselves and that they are more similar to attribution, which is only one part of the human explanation process BIBREF1 . We will prefer to call this importance mapping of the input an attribution rather than an explanation. We will talk about the importance of the input relevance score in regard to the model's computation and not make allusion to any human understanding of the model as a result.", + "There exist multiple ways to generate saliency maps over the input for non-linear classifiers BIBREF2 , BIBREF3 , BIBREF4 . We refer the reader to BIBREF5 for a survey of explainable AI in general. We use in this paper Layer-Wise Relevance Propagation (LRP) BIBREF2 which aims at redistributing the value of the classifying function on the input to obtain the importance attribution. It was first created to \u201cexplain\" the classification of neural networks on image recognition tasks. It was later successfully applied to text using convolutional neural networks (CNN) BIBREF6 and then Long-Short Term Memory (LSTM) networks for sentiment analysis BIBREF7 .", + "Our goal in this paper is to test the limits of the use of such a technique for more complex tasks, where the notion of input importance might not be as simple as in topic classification or sentiment analysis. We changed from a classification task to a generative task and chose a more complex one than text translation (in which we can easily find a word to word correspondence/importance between input and output). We chose text summarization. We consider abstractive and informative text summarization, meaning that we write a summary \u201cin our own words\" and retain the important information of the original text. We refer the reader to BIBREF8 for more details on the task and the different variants that exist. Since the success of deep sequence-to-sequence models for text translation BIBREF9 , the same approaches have been applied to text summarization tasks BIBREF10 , BIBREF11 , BIBREF12 which use architectures on which we can apply LRP.", + "We obtain one saliency map for each word in the generated summaries, supposed to represent the use of the input features for each element of the output sequence. We observe that all the saliency maps for a text are nearly identical and decorrelated with the attention distribution. We propose a way to check their validity by creating what could be seen as a counterfactual experiment from a synthesis of the saliency maps, using the same technique as in Arras et al. Arras2017. We show that in some but not all cases they help identify the important input features and that we need to rigorously check importance attributions before trusting them, regardless of whether or not the mapping \u201cmakes sense\" to us. We finally argue that in the process of identifying the important input features, verifying the saliency maps is as important as the generation step, if not more." + ], + [ + "We present in this section the baseline model from See et al. See2017 trained on the CNN/Daily Mail dataset. We reproduce the results from See et al. See2017 to then apply LRP on it." + ], + [ + "The CNN/Daily mail dataset BIBREF12 is a text summarization dataset adapted from the Deepmind question-answering dataset BIBREF13 . It contains around three hundred thousand news articles coupled with summaries of about three sentences. These summaries are in fact \u201chighlights\" of the articles provided by the media themselves. Articles have an average length of 780 words and the summaries of 50 words. We had 287 000 training pairs and 11 500 test pairs. Similarly to See et al. See2017, we limit during training and prediction the input text to 400 words and generate summaries of 200 words. We pad the shorter texts using an UNKNOWN token and truncate the longer texts. We embed the texts and summaries using a vocabulary of size 50 000, thus recreating the same parameters as See et al. See2017." + ], + [ + "The baseline model is a deep sequence-to-sequence encoder/decoder model with attention. The encoder is a bidirectional Long-Short Term Memory(LSTM) cell BIBREF14 and the decoder a single LSTM cell with attention mechanism. The attention mechanism is computed as in BIBREF9 and we use a greedy search for decoding. We train end-to-end including the words embeddings. The embedding size used is of 128 and the hidden state size of the LSTM cells is of 254." + ], + [ + "We train the 21 350 992 parameters of the network for about 60 epochs until we achieve results that are qualitatively equivalent to the results of See et al. See2017. We obtain summaries that are broadly relevant to the text but do not match the target summaries very well. We observe the same problems such as wrong reproduction of factual details, replacing rare words with more common alternatives or repeating non-sense after the third sentence. We can see in Figure 1 an example of summary obtained compared to the target one.", + "The \u201csummaries\" we generate are far from being valid summaries of the information in the texts but are sufficient to look at the attribution that LRP will give us. They pick up the general subject of the original text." + ], + [ + "We present in this section the Layer-Wise Relevance Propagation (LRP) BIBREF2 technique that we used to attribute importance to the input features, together with how we adapted it to our model and how we generated the saliency maps. LRP redistributes the output of the model from the output layer to the input by transmitting information backwards through the layers. We call this propagated backwards importance the relevance. LRP has the particularity to attribute negative and positive relevance: a positive relevance is supposed to represent evidence that led to the classifier's result while negative relevance represents evidence that participated negatively in the prediction." + ], + [ + "We initialize the relevance of the output layer to the value of the predicted class before softmax and we then describe locally the propagation backwards of the relevance from layer to layer. For normal neural network layers we use the form of LRP with epsilon stabilizer BIBREF2 . We write down $R_{i\\leftarrow j}^{(l, l+1)}$ the relevance received by the neuron $i$ of layer $l$ from the neuron $j$ of layer $l+1$ : ", + "$$\\begin{split}\n\nR_{i\\leftarrow j}^{(l, l+1)} &= \\dfrac{w_{i\\rightarrow j}^{l,l+1}\\textbf {z}^l_i + \\dfrac{\\epsilon \\textrm { sign}(\\textbf {z}^{l+1}_j) + \\textbf {b}^{l+1}_j}{D_l}}{\\textbf {z}^{l+1}_j + \\epsilon * \\textrm { sign}(\\textbf {z}^{l+1}_j)} * R_j^{l+1} \\\\\n\\end{split}$$ (Eq. 7) ", + "where $w_{i\\rightarrow j}^{l,l+1}$ is the network's weight parameter set during training, $\\textbf {b}^{l+1}_j$ is the bias for neuron $j$ of layer $l+1$ , $\\textbf {z}^{l}_i$ is the activation of neuron $i$ on layer $l$ , $\\epsilon $ is the stabilizing term set to 0.00001 and $D_l$ is the dimension of the $l$ -th layer.", + "The relevance of a neuron is then computed as the sum of the relevance he received from the above layer(s).", + "For LSTM cells we use the method from Arras et al.Arras2017 to solve the problem posed by the element-wise multiplications of vectors. Arras et al. noted that when such computation happened inside an LSTM cell, it always involved a \u201cgate\" vector and another vector containing information. The gate vector containing only value between 0 and 1 is essentially filtering the second vector to allow the passing of \u201crelevant\" information. Considering this, when we propagate relevance through an element-wise multiplication operation, we give all the upper-layer's relevance to the \u201cinformation\" vector and none to the \u201cgate\" vector." + ], + [ + "We use the same method to transmit relevance through the attention mechanism back to the encoder because Bahdanau's attention BIBREF9 uses element-wise multiplications as well. We depict in Figure 2 the transmission end-to-end from the output layer to the input through the decoder, attention mechanism and then the bidirectional encoder. We then sum up the relevance on the word embedding to get the token's relevance as Arras et al. Arras2017.", + "The way we generate saliency maps differs a bit from the usual context in which LRP is used as we essentially don't have one classification, but 200 (one for each word in the summary). We generate a relevance attribution for the 50 first words of the generated summary as after this point they often repeat themselves.", + "This means that for each text we obtain 50 different saliency maps, each one supposed to represent the relevance of the input for a specific generated word in the summary." + ], + [ + "In this section, we present our results from extracting attributions from the sequence-to-sequence model trained for abstractive text summarization. We first have to discuss the difference between the 50 different saliency maps we obtain and then we propose a protocol to validate the mappings." + ], + [ + "The first observation that is made is that for one text, the 50 saliency maps are almost identical. Indeed each mapping highlights mainly the same input words with only slight variations of importance. We can see in Figure 3 an example of two nearly identical attributions for two distant and unrelated words of the summary. The saliency map generated using LRP is also uncorrelated with the attention distribution that participated in the generation of the output word. The attention distribution changes drastically between the words in the generated summary while not impacting significantly the attribution over the input text. We deleted in an experiment the relevance propagated through the attention mechanism to the encoder and didn't observe much changes in the saliency map.", + "It can be seen as evidence that using the attention distribution as an \u201cexplanation\" of the prediction can be misleading. It is not the only information received by the decoder and the importance it \u201callocates\" to this attention state might be very low. What seems to happen in this application is that most of the information used is transmitted from the encoder to the decoder and the attention mechanism at each decoding step just changes marginally how it is used. Quantifying the difference between attention distribution and saliency map across multiple tasks is a possible future work.", + "The second observation we can make is that the saliency map doesn't seem to highlight the right things in the input for the summary it generates. The saliency maps on Figure 3 correspond to the summary from Figure 1 , and we don't see the word \u201cvideo\" highlighted in the input text, which seems to be important for the output.", + "This allows us to question how good the saliency maps are in the sense that we question how well they actually represent the network's use of the input features. We will call that truthfulness of the attribution in regard to the computation, meaning that an attribution is truthful in regard to the computation if it actually highlights the important input features that the network attended to during prediction. We proceed to measure the truthfulness of the attributions by validating them quantitatively." + ], + [ + "We propose to validate the saliency maps in a similar way as Arras et al. Arras2017 by incrementally deleting \u201cimportant\" words from the input text and observe the change in the resulting generated summaries.", + "We first define what \u201cimportant\" (and \u201cunimportant\") input words mean across the 50 saliency maps per texts. Relevance transmitted by LRP being positive or negative, we average the absolute value of the relevance across the saliency maps to obtain one ranking of the most \u201crelevant\" words. The idea is that input words with negative relevance have an impact on the resulting generated word, even if it is not participating positively, while a word with a relevance close to zero should not be important at all. We did however also try with different methods, like averaging the raw relevance or averaging a scaled absolute value where negative relevance is scaled down by a constant factor. The absolute value average seemed to deliver the best results.", + "We delete incrementally the important words (words with the highest average) in the input and compared it to the control experiment that consists of deleting the least important word and compare the degradation of the resulting summaries. We obtain mitigated results: for some texts, we observe a quick degradation when deleting important words which are not observed when deleting unimportant words (see Figure 4 ), but for other test examples we don't observe a significant difference between the two settings (see Figure 5 ).", + "One might argue that the second summary in Figure 5 is better than the first one as it makes better sentences but as the model generates inaccurate summaries, we do not wish to make such a statement.", + "This however allows us to say that the attribution generated for the text at the origin of the summaries in Figure 4 are truthful in regard to the network's computation and we may use it for further studies of the example, whereas for the text at the origin of Figure 5 we shouldn't draw any further conclusions from the attribution generated.", + "One interesting point is that one saliency map didn't look \u201cbetter\" than the other, meaning that there is no apparent way of determining their truthfulness in regard of the computation without doing a quantitative validation. This brings us to believe that even in simpler tasks, the saliency maps might make sense to us (for example highlighting the animal in an image classification task), without actually representing what the network really attended too, or in what way.", + "We defined without saying it the counterfactual case in our experiment: \u201cWould the important words in the input be deleted, we would have a different summary\". Such counterfactuals are however more difficult to define for image classification for example, where it could be applying a mask over an image, or just filtering a colour or a pattern. We believe that defining a counterfactual and testing it allows us to measure and evaluate the truthfulness of the attributions and thus weight how much we can trust them." + ], + [ + "In this work, we have implemented and applied LRP to a sequence-to-sequence model trained on a more complex task than usual: text summarization. We used previous work to solve the difficulties posed by LRP in LSTM cells and adapted the same technique for Bahdanau et al. Bahdanau2014 attention mechanism.", + "We observed a peculiar behaviour of the saliency maps for the words in the output summary: they are almost all identical and seem uncorrelated with the attention distribution. We then proceeded to validate our attributions by averaging the absolute value of the relevance across the saliency maps. We obtain a ranking of the word from the most important to the least important and proceeded to delete one or another.", + "We showed that in some cases the saliency maps are truthful to the network's computation, meaning that they do highlight the input features that the network focused on. But we also showed that in some cases the saliency maps seem to not capture the important input features. This brought us to discuss the fact that these attributions are not sufficient by themselves, and that we need to define the counter-factual case and test it to measure how truthful the saliency maps are.", + "Future work would look into the saliency maps generated by applying LRP to pointer-generator networks and compare to our current results as well as mathematically justifying the average that we did when validating our saliency maps. Some additional work is also needed on the validation of the saliency maps with counterfactual tests. The exploitation and evaluation of saliency map are a very important step and should not be overlooked." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0047/instruction.md b/qasper-0047/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..3d5516ac28963ee311a728431fac7165b87ed276 --- /dev/null +++ b/qasper-0047/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yor\`ub\'a and Twi + +Question: What two architectures are used? \ No newline at end of file diff --git a/qasper-0049/instruction.md b/qasper-0049/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..a5e4d4376095ceeb58fca09644c53c6622211026 --- /dev/null +++ b/qasper-0049/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Is there Gender bias and stereotype in Portuguese Word Embeddings? + +Question: What were the word embeddings trained on? \ No newline at end of file diff --git a/qasper-0071/instruction.md b/qasper-0071/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..22eb97e07ca2c5696add7df2ebb5e78597bd491a --- /dev/null +++ b/qasper-0071/instruction.md @@ -0,0 +1,121 @@ +Name of Paper: Spoken Language Identification using ConvNets + +Question: What is the accuracy reported by state-of-the-art methods? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work", + "Proposed Method ::: Motivations", + "Proposed Method ::: Description of Features", + "Proposed Method ::: Model Description", + "Proposed Method ::: Model Details: 1D ConvNet", + "Proposed Method ::: Model Details: 1D ConvNet ::: Hyperparameter Optimization:", + "Proposed Method ::: Model Details: 2D ConvNet with Attention and bi-directional GRU", + "Proposed Method ::: Model Details: 2D ConvNet with Attention and bi-directional GRU ::: ", + "Proposed Method ::: Model Details: 2D ConvNet with Attention and bi-directional GRU ::: Hyperparameter Optimization:", + "Proposed Method ::: Model details: 2D-ConvNet", + "Proposed Method ::: Dataset", + "Results and Discussion", + "Results and Discussion ::: Misclassification", + "Results and Discussion ::: Future Scope", + "Conclusion" + ], + "paragraphs": [ + [ + "Language Identification (LI) is a problem which involves classifying the language being spoken by a speaker. LI systems can be used in call centers to route international calls to an operator who is fluent in that identified language BIBREF0. In speech-based assistants, LI acts as the first step which chooses the corresponding grammar from a list of available languages for its further semantic analysis BIBREF1. It can also be used in multi-lingual voice-controlled information retrieval systems, for example, Apple Siri and Amazon Alexa.", + "Over the years, studies have utilized many prosodic and acoustic features to construct machine learning models for LI systems BIBREF2. Every language is composed of phonemes, which are distinct unit of sounds in that language, such as b of black and g of green. Several prosodic and acoustic features are based on phonemes, which become the underlying features on whom the performance of the statistical model depends BIBREF3, BIBREF4. If two languages have many overlapping phonemes, then identifying them becomes a challenging task for a classifier. For example, the word cat in English, kat in Dutch, katze in German have different consonants but when used in a speech they all would sound quite similar.", + "Due to such drawbacks several studies have switched over to using Deep Neural Networks (DNNs) to harness their novel auto-extraction techniques BIBREF1, BIBREF5. This work follows an implicit approach for identifying six languages with overlapping phonemes on the VoxForge BIBREF6 dataset and achieves 95.4% overall accuracy.", + "In previous studies BIBREF1, BIBREF7, BIBREF5, authors use log-Mel spectrum of a raw audio as inputs to their models. One of our contributions is to enhance the performance of this approach by utilising recent techniques like Mixup augmentation of inputs and exploring the effectiveness of Attention mechanism in enhancing performance of neural network. As log-Mel spectrum needs to be computed for each raw audio input and processing time for generating log-Mel spectrum increases linearly with length of audio, this acts as a bottleneck for these models. Hence, we propose the use of raw audio waveforms as inputs to deep neural network which boosts performance by avoiding additional overhead of computing log-Mel spectrum for each audio. Our 1D-ConvNet architecture auto-extracts and classifies features from this raw audio input.", + "The structure of the work is as follows. In Section 2 we discuss about the previous related studies in this field. The model architecture for both the raw waveforms and log-Mel spectrogram images is discussed in Section 3 along with the a discussion on hyperparameter space exploration. In Section 4 we present the experimental results. Finally, in Section 5 we discuss the conclusions drawn from the experiment and future work." + ], + [ + "Extraction of language dependent features like prosody and phonemes was a popular approach to classify spoken languages BIBREF8, BIBREF9, BIBREF10. Following their success in speaker verification systems, i-vectors have also been used as features in various classification networks. These approaches required significant domain knowledge BIBREF11, BIBREF9. Nowadays most of the attempts on spoken language identification rely on neural networks for meaningful feature extraction and classification BIBREF12, BIBREF13.", + "Revay et al. BIBREF5 used the ResNet50 BIBREF14 architecture for classifying languages by generating the log-Mel spectra of each raw audio. The model uses a cyclic learning rate where learning rate increases and then decreases linearly. Maximum learning rate for a cycle is set by finding the optimal learning rate using fastai BIBREF15 library. The model classified six languages \u2013 English, French, Spanish, Russian, Italian and German \u2013 and achieving an accuracy of 89.0%.", + "Gazeau et al. BIBREF16 in his research showed how Neural Networks, Support Vector Machine and Hidden Markov Model (HMM) can be used to identify French, English, Spanish and German. Dataset was prepared using voice samples from Youtube News BIBREF17and VoxForge BIBREF6 datasets. Hidden Markov models convert speech into a sequence of vectors, was used to capture temporal features in speech. HMMs trained on VoxForge BIBREF6 dataset performed best in comparison to other models proposed by him on same VoxForge dataset. They reported an accuracy of 70.0%.", + "Bartz et al. BIBREF1 proposed two different hybrid Convolutional Recurrent Neural Networks for language identification. They proposed a new architecture for extracting spatial features from log-Mel spectra of raw audio using CNNs and then using RNNs for capturing temporal features to identify the language. This model achieved an accuracy of 91.0% on Youtube News Dataset BIBREF17. In their second architecture they used the Inception-v3 BIBREF18 architecture to extract spatial features which were then used as input for bi-directional LSTMs to predict the language accurately. This model achieved an accuracy of 96.0% on four languages which were English, German, French and Spanish. They also trained their CNN model (obtained after removing RNN from CRNN model) and the Inception-v3 on their dataset. However they were not able to achieve better results achieving and reported 90% and 95% accuracies, respectively.", + "Kumar et al. BIBREF0 used Mel-frequency cepstral coefficients (MFCC), Perceptual linear prediction coefficients (PLP), Bark Frequency Cepstral Coefficients (BFCC) and Revised Perceptual Linear Prediction Coefficients (RPLP) as features for language identification. BFCC and RPLP are hybrid features derived using MFCC and PLP. They used two different models based on Vector Quantization (VQ) with Dynamic Time Warping (DTW) and Gaussian Mixture Model (GMM) for classification. These classification models were trained with different features. The authors were able to show that these models worked better with hybrid features (BFCC and RPLP) as compared to conventional features (MFCC and PLP). GMM combined with RPLP features gave the most promising results and achieved an accuracy of 88.8% on ten languages. They designed their own dataset comprising of ten languages being Dutch, English, French, German, Italian, Russian, Spanish, Hindi, Telegu, and Bengali.", + "Montavon BIBREF7 generated Mel spectrogram as features for a time-delay neural network (TDNN). This network had two-dimensional convolutional layers for feature extraction. An elaborate analysis of how deep architectures outperform their shallow counterparts is presented in this reseacrch. The difficulties in classifying perceptually similar languages like German and English were also put forward in this work. It is mentioned that the proposed approach is less robust to new speakers present in the test dataset. This method was able to achieve an accuracy of 91.2% on dataset comprising of 3 languages \u2013 English, French and German.", + "In Table TABREF1, we summarize the quantitative results of the above previous studies. It includes the model basis, feature description, languages classified and the used dataset along with accuracy obtained. The table also lists the overall results of our proposed models (at the top). The languages used by various authors along with their acronyms are English (En), Spanish (Es), French (Fr), German (De), Russian (Ru), Italian (It), Bengali (Ben), Hindi (Hi) and Telegu (Tel)." + ], + [ + "Several state-of-the-art results on various audio classification tasks have been obtained by using log-Mel spectrograms of raw audio, as features BIBREF19. Convolutional Neural Networks have demonstrated an excellent performance gain in classification of these features BIBREF20, BIBREF21 against other machine learning techniques. It has been shown that using attention layers with ConvNets further enhanced their performance BIBREF22. This motivated us to develop a CNN-based architecture with attention since this approach hasn\u2019t been applied to the task of language identification before.", + "Recently, using raw audio waveform as features to neural networks has become a popular approach in audio classification BIBREF23, BIBREF22. Raw waveforms have several artifacts which are not effectively captured by various conventional feature extraction techniques like Mel Frequency Cepstral Coefficients (MFCC), Constant Q Transform (CQT), Fast Fourier Transform (FFT), etc.", + "Audio files are a sequence of spoken words, hence they have temporal features too.A CNN is better at capturing spatial features only and RNNs are better at capturing temporal features as demonstrated by Bartz et al. BIBREF1 using audio files. Therefore, we combined both of these to make a CRNN model.", + "We propose three types of models to tackle the problem with different approaches, discussed as follows." + ], + [ + "As an average human's voice is around 300 Hz and according to Nyquist-Shannon sampling theorem all the useful frequencies (0-300 Hz) are preserved with sampling at 8 kHz, therefore, we sampled raw audio files from all six languages at 8 kHz", + "The average length of audio files in this dataset was about 10.4 seconds and standard deviation was 2.3 seconds. For our experiments, the audio length was set to 10 seconds. If the audio files were shorter than 10 second, then the data was repeated and concatenated. If audio files were longer, then the data was truncated." + ], + [ + "We applied the following design principles to all our models:", + "Every convolutional layer is always followed by an appropriate max pooling layer. This helps in containing the explosion of parameters and keeps the model small and nimble.", + "Convolutional blocks are defined as an individual block with multiple pairs of one convolutional layer and one max pooling layer. Each convolutional block is preceded or succeded by a convolutional layer.", + "Batch Normalization and Rectified linear unit activations were applied after each convolutional layer. Batch Normalization helps speed up convergence during training of a neural network.", + "Model ends with a dense layer which acts the final output layer." + ], + [ + "As the sampling rate is 8 kHz and audio length is 10 s, hence the input is raw audio to the models with input size of (batch size, 1, 80000). In Table TABREF10, we present a detailed layer-by-layer illustration of the model along with its hyperparameter.", + "-10pt" + ], + [ + "Tuning hyperparameters is a cumbersome process as the hyperparamter space expands exponentially with the number of parameters, therefore efficient exploration is needed for any feasible study. We used the random search algorithm supported by Hyperopt BIBREF24 library to randomly search for an optimal set of hyperparameters from a given parameter space. In Fig. FIGREF12, various hyperparameters we considered are plotted against the validation accuracy as violin plots. Our observations for each hyperparameter are summarized below:", + "Number of filters in first layer: We observe that having 128 filters gives better results as compared to other filter values of 32 and 64 in the first layer. A higher number of filters in the first layer of network is able to preserve most of the characteristics of input.", + "Kernel Size: We varied the receptive fields of convolutional layers by choosing the kernel size from among the set of {3, 5, 7, 9}. We observe that a kernel size of 9 gives better accuracy at the cost of increased computation time and larger number of parameters. A large kernel size is able to capture longer patterns in its input due to bigger receptive power which results in an improved accuracy.", + "Dropout: Dropout randomly turns-off (sets to 0) various individual nodes during training of the network. In a deep CNN it is important that nodes do not develop a co-dependency amongst each other during training in order to prevent overfitting on training data BIBREF25. Dropout rate of $0.1$ works well for our model. When using a higher dropout rate the network is not able to capture the patterns in training dataset.", + "Batch Size: We chose batch sizes from amongst the set {32, 64, 128}. There is more noise while calculating error in a smaller batch size as compared to a larger one. This tends to have a regularizing effect during training of the network and hence gives better results. Thus, batch size of 32 works best for the model.", + "Layers in Convolutional block 1 and 2: We varied the number of layers in both the convolutional blocks. If the number of layers is low, then the network does not have enough depth to capture patterns in the data whereas having large number of layers leads to overfitting on the data. In our network, two layers in the first block and one layer in the second block give optimal results." + ], + [ + "Log-Mel spectrogram is the most commonly used method for converting audio into the image domain. The audio data was again sampled at 8 kHz. The input to this model was the log-Mel spectra. We generated log-Mel spectrogram using the LibROSA BIBREF26 library. In Table TABREF16, we present a detailed layer-by-layer illustration of the model along with its hyperparameter." + ], + [ + "We took some specific design choices for this model, which are as follows:", + "We added residual connections with each convolutional layer. Residual connections in a way makes the model selective of the contributing layers, determines the optimal number of layers required for training and solves the problem of vanishing gradients. Residual connections or skip connections skip training of those layers that do not contribute much in the overall outcome of model.", + "We added spatial attention BIBREF27 networks to help the model in focusing on specific regions or areas in an image. Spatial attention aids learning irrespective of transformations, scaling and rotation done on the input images making the model more robust and helping it to achieve better results.", + "We added Channel Attention networks so as to help the model to find interdependencies among color channels of log-Mel spectra. It adaptively assigns importance to each color channel in a deep convolutional multi-channel network. In our model we apply channel and spatial attention just before feeding the input into bi-directional GRU. This helps the model to focus on selected regions and at the same time find patterns among channels to better determine the language." + ], + [ + "We used the random search algorithm supported by Hyperopt BIBREF24 library to randomly search for an optimal set of hyperparameters from a given parameter space. In Fig. FIGREF19 ,various hyperparameters we tuned are plotted against the validation accuracy. Our observations for each hyperparameter are summarized below:", + "Filter Size: 64 filters in the first layer of network can preserve most of the characteristics of input, but increasing it to 128 is inefficient as overfitting occurs.", + "Kernel Size: There is a trade-off between kernel size and capturing complex non-linear features. Using a small kernel size will require more layers to capture features whereas using a large kernel size will require less layers. Large kernels capture simple non-linear features whereas using a smaller kernel will help us capture more complex non-linear features. However, with more layers, backpropagation necessitates the need for a large memory. We experimented with large kernel size and gradually increased the layers in order to capture more complex features. The results are not conclusive and thus we chose kernel size of 7 against 3.", + "Dropout: Dropout rate of 0.1 works well for our data. When using a higher dropout rate the network is not able to capture the patterns in training dataset.", + "Batch Size: There is always a trade-off between batch size and getting accurate gradients. Using a large batch size helps the model to get more accurate gradients since the model tries to optimize gradients over a large set of images. We found that using a batch size of 128 helped the model to train faster and get better results than using a batch size less than 128.", + "Number of hidden units in bi-directional GRU: Varying the number of hidden units and layers in GRU helps the model to capture temporal features which can play a significant role in identifying the language correctly. The optimal number of hidden units and layers depends on the complexity of the dataset. Using less number of hidden units may capture less features whereas using large number of hidden units may be computationally expensive. In our case we found that using 1536 hidden units in a single bi-directional GRU layer leads to the best result.", + "Image Size: We experimented with log-Mel spectra images of sizes $64 \\times 64$ and $128 \\times 128$ pixels and found that our model worked best with images of size of $128 \\times 128$ pixels.", + "We also evaluated our model on data with mixup augmentation BIBREF28. It is a data augmentation technique that also acts as a regularization technique and prevents overfitting. Instead of directly taking images from the training dataset as input, mixup takes a linear combination of any two random images and feeds it as input. The following equations were used to prepared a mixed-up dataset:", + "and", + "where $\\alpha \\in [0, 1]$ is a random variable from a $\\beta $-distribution, $I_1$." + ], + [ + "This model is a similar model to 2D-ConvNet with Attention and bi-directional GRU described in section SECREF13 except that it lacks skip connections, attention layers, bi-directional GRU and the embedding layer incorporated in the previous model." + ], + [ + "We classified six languages (English, French, German, Spanish, Russian and Italian) from the VoxForge BIBREF6 dataset. VoxForge is an open-source speech corpus which primarily consists of samples recorded and submitted by users using their own microphone. This results in significant variation of speech quality between samples making it more representative of real world scenarios.", + "Our dataset consists of 1,500 samples for each of six languages. Out of 1,500 samples for each language, 1,200 were randomly selected as training dataset for that language and rest 300 as validation dataset using k-fold cross-validation. To sum up, we trained our model on 7,200 samples and validated it on 1800 samples comprising six languages. The results are discussed in next section." + ], + [ + "This paper discusses two end-to-end approaches which achieve state-of-the-art results in both the image as well as audio domain on the VoxForge dataset BIBREF6. In Table TABREF25, we present all the classification accuracies of the two models of the cases with and without mixup for six and four languages.", + "In the audio domain (using raw audio waveform as input), 1D-ConvNet achieved a mean accuracy of 93.7% with a standard deviation of 0.3% on running k-fold cross validation. In Fig FIGREF27 (a) we present the confusion matrix for the 1D-ConvNet model.", + "In the image domain (obtained by taking log-Mel spectra of raw audio), 2D-ConvNet with 2D attention (channel and spatial attention) and bi-directional GRU achieved a mean accuracy of 95.0% with a standard deviation of 1.2% for six languages. This model performed better when mixup regularization was applied. 2D-ConvNet achieved a mean accuracy of 95.4% with standard deviation of 0.6% on running k-fold cross validation for six languages when mixup was applied. In Fig FIGREF27 (b) we present the confusion matrix for the 2D-ConvNet model. 2D attention models focused on the important features extracted by convolutional layers and bi-directional GRU captured the temporal features." + ], + [ + "Several of the spoken languages in Europe belong to the Indo-European family. Within this family, the languages are divided into three phyla which are Romance, Germanic and Slavic. Of the 6 languages that we selected Spanish (Es), French (Fr) and Italian (It) belong to the Romance phyla, English and German belong to Germanic phyla and Russian in Slavic phyla. Our model also confuses between languages belonging to the similar phyla which acts as an insanity check since languages in same phyla have many similar pronounced words such as cat in English becomes Katze in German and Ciao in Italian becomes Chao in Spanish.", + "Our model confuses between French (Fr) and Russian (Ru) while these languages belong to different phyla, many words from French were adopted into Russian such as automate (oot-oo-mate) in French becomes ABTOMaT (aff-taa-maat) in Russian which have similar pronunciation.", + "" + ], + [ + "The performance of raw audio waveforms as input features to ConvNet can be further improved by applying silence removal in the audio. Also, there is scope for improvement by augmenting available data through various conventional techniques like pitch shifting, adding random noise and changing speed of audio. These help in making neural networks more robust to variations which might be present in real world scenarios. There can be further exploration of various feature extraction techniques like Constant-Q transform and Fast Fourier Transform and assessment of their impact on Language Identification.", + "There can be further improvements in neural network architectures like concatenating the high level features obtained from 1D-ConvNet and 2D-ConvNet, before performing classification. There can be experiments using deeper networks with skip connections and Inception modules. These are known to have positively impacted the performance of Convolutional Neural Networks." + ], + [ + "There are two main contributions of this paper in the domain of spoken language identification. Firstly, we presented an extensive analysis of raw audio waveforms as input features to 1D-ConvNet. We experimented with various hyperparameters in our 1D-ConvNet and evaluated their effect on validation accuracy. This method is able to bypass the computational overhead of conventional approaches which depend on generation of spectrograms as a necessary pre-procesing step. We were able to achieve an accauracy of 93.7% using this technique.", + "Next, we discussed the enhancement in performance of 2D-ConvNet using mixup augmentation, which is a recently developed technique to prevent over\ufb01tting on test data.This approach achieved an accuracy of 95.4%. We also analysed how attention mechanism and recurrent layers impact the performance of networks. This approach achieved an accuracy of 95.0%." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0076/instruction.md b/qasper-0076/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..dc6f556220cac21203bc98d1195a58e66bf06265 --- /dev/null +++ b/qasper-0076/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: AraNet: A Deep Learning Toolkit for Arabic Social Media + +Question: What models did they compare to? \ No newline at end of file diff --git a/qasper-0078/instruction.md b/qasper-0078/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..493d14d59e32209783545d75bee3799e2a99654f --- /dev/null +++ b/qasper-0078/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Generative Adversarial Nets for Multiple Text Corpora + +Question: Which GAN do they use? \ No newline at end of file diff --git a/qasper-0082/instruction.md b/qasper-0082/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..31b607c2d4d35dc415eb7eda9deb4d7c6a7f35e4 --- /dev/null +++ b/qasper-0082/instruction.md @@ -0,0 +1,104 @@ +Name of Paper: Stacked DeBERT: All Attention in Incomplete Data for Text Classification + +Question: How do the authors define or exemplify 'incorrect words'? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Proposed model", + "Dataset ::: Twitter Sentiment Classification", + "Dataset ::: Intent Classification from Text with STT Error", + "Experiments ::: Baseline models", + "Experiments ::: Baseline models ::: NLU service platforms", + "Experiments ::: Baseline models ::: Semantic hashing with classifier", + "Experiments ::: Training specifications", + "Experiments ::: Training specifications ::: NLU service platforms", + "Experiments ::: Training specifications ::: Semantic hashing with classifier", + "Experiments ::: Training specifications ::: BERT", + "Experiments ::: Training specifications ::: Stacked DeBERT", + "Experiments ::: Results on Sentiment Classification from Incorrect Text", + "Experiments ::: Results on Intent Classification from Text with STT Error", + "Conclusion", + "Acknowledgments" + ], + "paragraphs": [ + [ + "Understanding a user's intent and sentiment is of utmost importance for current intelligent chatbots to respond appropriately to human requests. However, current systems are not able to perform to their best capacity when presented with incomplete data, meaning sentences with missing or incorrect words. This scenario is likely to happen when one considers human error done in writing. In fact, it is rather naive to assume that users will always type fully grammatically correct sentences. Panko BIBREF0 goes as far as claiming that human accuracy regarding research paper writing is none when considering the entire document. This has been aggravated with the advent of internet and social networks, which allowed language and modern communication to be been rapidly transformed BIBREF1, BIBREF2. Take Twitter for instance, where information is expected to be readily communicated in short and concise sentences with little to no regard to correct sentence grammar or word spelling BIBREF3.", + "Further motivation can be found in Automatic Speech Recognition (ASR) applications, where high error rates prevail and pose an enormous hurdle in the broad adoption of speech technology by users worldwide BIBREF4. This is an important issue to tackle because, in addition to more widespread user adoption, improving Speech-to-Text (STT) accuracy diminishes error propagation to modules using the recognized text. With that in mind, in order for current systems to improve the quality of their services, there is a need for development of robust intelligent systems that are able to understand a user even when faced with incomplete representation in language.", + "The advancement of deep neural networks have immensely aided in the development of the Natural Language Processing (NLP) domain. Tasks such as text generation, sentence correction, image captioning and text classification, have been possible via models such as Convolutional Neural Networks and Recurrent Neural Networks BIBREF5, BIBREF6, BIBREF7. More recently, state-of-the-art results have been achieved with attention models, more specifically Transformers BIBREF8. Surprisingly, however, there is currently no research on incomplete text classification in the NLP community. Realizing the need of research in that area, we make it the focus of this paper. In this novel task, the model aims to identify the user's intent or sentiment by analyzing a sentence with missing and/or incorrect words. In the sentiment classification task, the model aims to identify the user's sentiment given a tweet, written in informal language and without regards for sentence correctness.", + "Current approaches for Text Classification tasks focus on efficient embedding representations. Kim et al. BIBREF9 use semantically enriched word embeddings to make synonym and antonym word vectors respectively more and less similar in order to improve intent classification performance. Devlin et al. BIBREF10 propose Bidirectional Encoder Representations from Transformers (BERT), a powerful bidirectional language representation model based on Transformers, achieving state-of-the-art results on eleven NLP tasks BIBREF11, including sentiment text classification. Concurrently, Shridhar et al. BIBREF12 also reach state of the art in the intent recognition task using Semantic Hashing for feature representation followed by a neural classifier. All aforementioned approaches are, however, applied to datasets based solely on complete data.", + "The incomplete data problem is usually approached as a reconstruction or imputation task and is most often related to missing numbers imputation BIBREF13. Vincent et al. BIBREF14, BIBREF15 propose to reconstruct clean data from its noisy version by mapping the input to meaningful representations. This approach has also been shown to outperform other models, such as predictive mean matching, random forest, Support Vector Machine (SVM) and Multiple imputation by Chained Equations (MICE), at missing data imputation tasks BIBREF16, BIBREF17. Researchers in those two areas have shown that meaningful feature representation of data is of utter importance for high performance achieving methods. We propose a model that combines the power of BERT in the NLP domain and the strength of denoising strategies in incomplete data reconstruction to tackle the tasks of incomplete intent and sentiment classification. This enables the implementation of a novel encoding scheme, more robust to incomplete data, called Stacked Denoising BERT or Stacked DeBERT. Our approach consists of obtaining richer input representations from input tokens by stacking denoising transformers on an embedding layer with vanilla transformers. The embedding layer and vanilla transformers extract intermediate input features from the input tokens, and the denoising transformers are responsible for obtaining richer input representations from them. By improving BERT with stronger denoising abilities, we are able to reconstruct missing and incorrect words' embeddings and improve classification accuracy. To summarize, our contribution is two-fold:", + "Novel model architecture that is more robust to incomplete data, including missing or incorrect words in text.", + "Proposal of the novel tasks of incomplete intent and sentiment classification from incorrect sentences, and release of corpora related with these tasks.", + "The remainder of this paper is organized in four sections, with Section SECREF2 explaining the proposed model. This is followed by Section SECREF3 which includes a detailed description of the dataset used for training and evaluation purposes and how it was obtained. Section SECREF4 covers the baseline models used for comparison, training specifications and experimental results. Finally, Section SECREF5 wraps up this paper with conclusion and future works." + ], + [ + "We propose Stacked Denoising BERT (DeBERT) as a novel encoding scheming for the task of incomplete intent classification and sentiment classification from incorrect sentences, such as tweets and text with STT error. The proposed model, illustrated in Fig. FIGREF4, is structured as a stacking of embedding layers and vanilla transformer layers, similarly to the conventional BERT BIBREF10, followed by layers of novel denoising transformers. The main purpose of this model is to improve the robustness and efficiency of BERT when applied to incomplete data by reconstructing hidden embeddings from sentences with missing words. By reconstructing these hidden embeddings, we are able to improve the encoding scheme in BERT.", + "The initial part of the model is the conventional BERT, a multi-layer bidirectional Transformer encoder and a powerful language model. During training, BERT is fine-tuned on the incomplete text classification corpus (see Section SECREF3). The first layer pre-processes the input sentence by making it lower-case and by tokenizing it. It also prefixes the sequence of tokens with a special character `[CLS]' and sufixes each sentence with a `[SEP]' character. It is followed by an embedding layer used for input representation, with the final input embedding being a sum of token embedddings, segmentation embeddings and position embeddings. The first one, token embedding layer, uses a vocabulary dictionary to convert each token into a more representative embedding. The segmentation embedding layer indicates which tokens constitute a sentence by signaling either 1 or 0. In our case, since our data are formed of single sentences, the segment is 1 until the first `[SEP]' character appears (indicating segment A) and then it becomes 0 (segment B). The position embedding layer, as the name indicates, adds information related to the token's position in the sentence. This prepares the data to be considered by the layers of vanilla bidirectional transformers, which outputs a hidden embedding that can be used by our novel layers of denoising transformers.", + "Although BERT has shown to perform better than other baseline models when handling incomplete data, it is still not enough to completely and efficiently handle such data. Because of that, there is a need for further improvement of the hidden feature vectors obtained from sentences with missing words. With this purpose in mind, we implement a novel encoding scheme consisting of denoising transformers, which is composed of stacks of multilayer perceptrons for the reconstruction of missing words\u2019 embeddings by extracting more abstract and meaningful hidden feature vectors, and bidirectional transformers for improved embedding representation. The embedding reconstruction step is trained on sentence embeddings extracted from incomplete data $h_{inc}$ as input and embeddings corresponding to its complete version $h_{comp}$ as target. Both input and target are obtained after applying the embedding layers and the vanilla transformers, as indicated in Fig. FIGREF4, and have shape $(N_{bs}, 768, 128)$, where $N_{bs}$ is the batch size, 768 is the original BERT embedding size for a single token, and 128 is the maximum sequence length in a sentence.", + "The stacks of multilayer perceptrons are structured as two sets of three layers with two hidden layers each. The first set is responsible for compressing the $h_{inc}$ into a latent-space representation, extracting more abstract features into lower dimension vectors $z_1$, $z_2$ and $\\mathbf {z}$ with shape $(N_{bs}, 128, 128)$, $(N_{bs}, 32, 128)$, and $(N_{bs}, 12, 128)$, respectively. This process is shown in Eq. (DISPLAY_FORM5):", + "where $f(\\cdot )$ is the parameterized function mapping $h_{inc}$ to the hidden state $\\mathbf {z}$. The second set then respectively reconstructs $z_1$, $z_2$ and $\\mathbf {z}$ into $h_{rec_1}$, $h_{rec_2}$ and $h_{rec}$. This process is shown in Eq. (DISPLAY_FORM6):", + "where $g(\\cdot )$ is the parameterized function that reconstructs $\\mathbf {z}$ as $h_{rec}$.", + "The reconstructed hidden sentence embedding $h_{rec}$ is compared with the complete hidden sentence embedding $h_{comp}$ through a mean square error loss function, as shown in Eq. (DISPLAY_FORM7):", + "After reconstructing the correct hidden embeddings from the incomplete sentences, the correct hidden embeddings are given to bidirectional transformers to generate input representations. The model is then fine-tuned in an end-to-end manner on the incomplete text classification corpus.", + "Classification is done with a feedforward network and softmax activation function. Softmax $\\sigma $ is a discrete probability distribution function for $N_C$ classes, with the sum of the classes probability being 1 and the maximum value being the predicted class. The predicted class can be mathematically calculated as in Eq. (DISPLAY_FORM8):", + "where $o = W t + b$, the output of the feedforward layer used for classification." + ], + [ + "In order to evaluate the performance of our model, we need access to a naturally noisy dataset with real human errors. Poor quality texts obtained from Twitter, called tweets, are then ideal for our task. For this reason, we choose Kaggle's two-class Sentiment140 dataset BIBREF18, which consists of spoken text being used in writing and without strong consideration for grammar or sentence correctness. Thus, it has many mistakes, as specified in Table TABREF11.", + "Even though this corpus has incorrect sentences and their emotional labels, they lack their respective corrected sentences, necessary for the training of our model. In order to obtain this missing information, we outsource native English speakers from an unbiased and anonymous platform, called Amazon Mechanical Turk (MTurk) BIBREF19, which is a paid marketplace for Human Intelligence Tasks (HITs). We use this platform to create tasks for native English speakers to format the original incorrect tweets into correct sentences. Some examples are shown in Table TABREF12.", + "After obtaining the correct sentences, our two-class dataset has class distribution as shown in Table TABREF14. There are 200 sentences used in the training stage, with 100 belonging to the positive sentiment class and 100 to the negative class, and 50 samples being used in the evaluation stage, with 25 negative and 25 positive. This totals in 300 samples, with incorrect and correct sentences combined. Since our goal is to evaluate the model's performance and robustness in the presence of noise, we only consider incorrect data in the testing phase. Note that BERT is a pre-trained model, meaning that small amounts of data are enough for appropriate fine-tuning." + ], + [ + "In the intent classification task, we are presented with a corpus that suffers from the opposite problem of the Twitter sentiment classification corpus. In the intent classification corpus, we have the complete sentences and intent labels but lack their corresponding incomplete sentences, and since our task revolves around text classification in incomplete or incorrect data, it is essential that we obtain this information. To remedy this issue, we apply a Text-to-Speech (TTS) module followed by a Speech-to-Text (STT) module to the complete sentences in order to obtain incomplete sentences with STT error. Due to TTS and STT modules available being imperfect, the resulting sentences have a reasonable level of noise in the form of missing or incorrectly transcribed words. Analysis on this dataset adds value to our work by enabling evaluation of our model's robustness to different rates of data incompleteness.", + "The dataset used to evaluate the models' performance is the Chatbot Natural Language Unerstanding (NLU) Evaluation Corpus, introduced by Braun et al. BIBREF20 to test NLU services. It is a publicly available benchmark and is composed of sentences obtained from a German Telegram chatbot used to answer questions about public transport connections. The dataset has two intents, namely Departure Time and Find Connection with 100 train and 106 test samples, shown in Table TABREF18. Even though English is the main language of the benchmark, this dataset contains a few German station and street names.", + "The incomplete dataset used for training is composed of lower-cased incomplete data obtained by manipulating the original corpora. The incomplete sentences with STT error are obtained in a 2-step process shown in Fig. FIGREF22. The first step is to apply a TTS module to the available complete sentence. Here, we apply gtts , a Google Text-to-Speech python library, and macsay , a terminal command available in Mac OS as say. The second step consists of applying an STT module to the obtained audio files in order to obtain text containing STT errors. The STT module used here was witai , freely available and maintained by Wit.ai. The mentioned TTS and STT modules were chosen according to code availability and whether it's freely available or has high daily usage limitations.", + "Table TABREF24 exemplifies a complete and its respective incomplete sentences with different TTS-STT combinations, thus varying rates of missing and incorrect words. The level of noise in the STT imbued sentences is denoted by a inverted BLEU (iBLEU) score ranging from 0 to 1. The inverted BLEU score is denoted in Eq. (DISPLAY_FORM23):", + "where BLEU is a common metric usually used in machine translation tasks BIBREF21. We decide to showcase that instead of regular BLEU because it is more indicative to the amount of noise in the incomplete text, where the higher the iBLEU, the higher the noise." + ], + [ + "Besides the already mentioned BERT, the following baseline models are also used for comparison." + ], + [ + "We focus on the three following services, where the first two are commercial services and last one is open source with two separate backends: Google Dialogflow (formerly Api.ai) , SAP Conversational AI (formerly Recast.ai) and Rasa (spacy and tensorflow backend) ." + ], + [ + "Shridhar et al. BIBREF12 proposed a word embedding method that doesn't suffer from out-of-vocabulary issues. The authors achieve this by using hash tokens in the alphabet instead of a single word, making it vocabulary independent. For classification, classifiers such as Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Random Forest are used. A complete list of classifiers and training specifications are given in Section SECREF31." + ], + [ + "The baseline and proposed models are each trained 3 separate times for the incomplete intent classification task: complete data and one for each of the TTS-STT combinations (gtts-witai and macsay-witai). Regarding the sentiment classification from incorrect sentences task, the baseline and proposed models are each trained 3 times: original text, corrected text and incorrect with correct texts. The reported F1 scores are the best accuracies obtained from 10 runs." + ], + [ + "No settable training configurations available in the online platforms." + ], + [ + "Trained on 3-gram, feature vector size of 768 as to match the BERT embedding size, and 13 classifiers with parameters set as specified in the authors' paper so as to allow comparison: MLP with 3 hidden layers of sizes $[300, 100, 50]$ respectively; Random Forest with 50 estimators or trees; 5-fold Grid Search with Random Forest classifier and estimator $([50, 60, 70]$; Linear Support Vector Classifier with L1 and L2 penalty and tolerance of $10^{-3}$; Regularized linear classifier with Stochastic Gradient Descent (SGD) learning with regularization term $alpha=10^{-4}$ and L1, L2 and Elastic-Net penalty; Nearest Centroid with Euclidian metric, where classification is done by representing each class with a centroid; Bernoulli Naive Bayes with smoothing parameter $alpha=10^{-2}$; K-means clustering with 2 clusters and L2 penalty; and Logistic Regression classifier with L2 penalty, tolerance of $10^{-4}$ and regularization term of $1.0$. Most often, the best performing classifier was MLP." + ], + [ + "Conventional BERT is a BERT-base-uncased model, meaning that it has 12 transformer blocks $L$, hidden size $H$ of 768, and 12 self-attention heads $A$. The model is fine-tuned with our dataset on 2 Titan X GPUs for 3 epochs with Adam Optimizer, learning rate of $2*10^{-5}$, maximum sequence length of 128, and warm up proportion of $0.1$. The train batch size is 4 for the Twitter Sentiment Corpus and 8 for the Chatbot Intent Classification Corpus." + ], + [ + "Our proposed model is trained in end-to-end manner on 2 Titan X GPUs, with training time depending on the size of the dataset and train batch size. The stack of multilayer perceptrons are trained for 100 and 1,000 epochs with Adam Optimizer, learning rate of $10^{-3}$, weight decay of $10^{-5}$, MSE loss criterion and batch size the same as BERT (4 for the Twitter Sentiment Corpus and 8 for the Chatbot Intent Classification Corpus)." + ], + [ + "Experimental results for the Twitter Sentiment Classification task on Kaggle's Sentiment140 Corpus dataset, displayed in Table TABREF37, show that our model has better F1-micros scores, outperforming the baseline models by 6$\\%$ to 8$\\%$. We evaluate our model and baseline models on three versions of the dataset. The first one (Inc) only considers the original data, containing naturally incorrect tweets, and achieves accuracy of 80$\\%$ against BERT's 72$\\%$. The second version (Corr) considers the corrected tweets, and shows higher accuracy given that it is less noisy. In that version, Stacked DeBERT achieves 82$\\%$ accuracy against BERT's 76$\\%$, an improvement of 6$\\%$. In the last case (Inc+Corr), we consider both incorrect and correct tweets as input to the models in hopes of improving performance. However, the accuracy was similar to the first aforementioned version, 80$\\%$ for our model and 74$\\%$ for the second highest performing model. Since the first and last corpus gave similar performances with our model, we conclude that the Twitter dataset does not require complete sentences to be given as training input, in addition to the original naturally incorrect tweets, in order to better model the noisy sentences.", + "In addition to the overall F1-score, we also present a confusion matrix, in Fig. FIGREF38, with the per-class F1-scores for BERT and Stacked DeBERT. The normalized confusion matrix plots the predicted labels versus the target/target labels. Similarly to Table TABREF37, we evaluate our model with the original Twitter dataset, the corrected version and both original and corrected tweets. It can be seen that our model is able to improve the overall performance by improving the accuracy of the lower performing classes. In the Inc dataset, the true class 1 in BERT performs with approximately 50%. However, Stacked DeBERT is able to improve that to 72%, although to a cost of a small decrease in performance of class 0. A similar situation happens in the remaining two datasets, with improved accuracy in class 0 from 64% to 84% and 60% to 76% respectively." + ], + [ + "Experimental results for the Intent Classification task on the Chatbot NLU Corpus with STT error can be seen in Table TABREF40. When presented with data containing STT error, our model outperforms all baseline models in both combinations of TTS-STT: gtts-witai outperforms the second placing baseline model by 0.94% with F1-score of 97.17%, and macsay-witai outperforms the next highest achieving model by 1.89% with F1-score of 96.23%.", + "The table also indicates the level of noise in each dataset with the already mentioned iBLEU score, where 0 means no noise and higher values mean higher quantity of noise. As expected, the models' accuracy degrade with the increase in noise, thus F1-scores of gtts-witai are higher than macsay-witai. However, while the other models decay rapidly in the presence of noise, our model does not only outperform them but does so with a wider margin. This is shown with the increasing robustness curve in Fig. FIGREF41 and can be demonstrated by macsay-witai outperforming the baseline models by twice the gap achieved by gtts-witai.", + "Further analysis of the results in Table TABREF40 show that, BERT decay is almost constant with the addition of noise, with the difference between the complete data and gtts-witai being 1.88 and gtts-witai and macsay-witai being 1.89. Whereas in Stacked DeBERT, that difference is 1.89 and 0.94 respectively. This is stronger indication of our model's robustness in the presence of noise.", + "Additionally, we also present Fig. FIGREF42 with the normalized confusion matrices for BERT and Stacked DeBERT for sentences containing STT error. Analogously to the Twitter Sentiment Classification task, the per-class F1-scores show that our model is able to improve the overall performance by improving the accuracy of one class while maintaining the high-achieving accuracy of the second one." + ], + [ + "In this work, we proposed a novel deep neural network, robust to noisy text in the form of sentences with missing and/or incorrect words, called Stacked DeBERT. The idea was to improve the accuracy performance by improving the representation ability of the model with the implementation of novel denoising transformers. More specifically, our model was able to reconstruct hidden embeddings from their respective incomplete hidden embeddings. Stacked DeBERT was compared against three NLU service platforms and two other machine learning methods, namely BERT and Semantic Hashing with neural classifier. Our model showed better performance when evaluated on F1 scores in both Twitter sentiment and intent text with STT error classification tasks. The per-class F1 score was also evaluated in the form of normalized confusion matrices, showing that our model was able to improve the overall performance by better balancing the accuracy of each class, trading-off small decreases in high achieving class for significant improvements in lower performing ones. In the Chatbot dataset, accuracy improvement was achieved even without trade-off, with the highest achieving classes maintaining their accuracy while the lower achieving class saw improvement. Further evaluation on the F1-scores decay in the presence of noise demonstrated that our model is more robust than the baseline models when considering noisy data, be that in the form of incorrect sentences or sentences with STT error. Not only that, experiments on the Twitter dataset also showed improved accuracy in clean data, with complete sentences. We infer that this is due to our model being able to extract richer data representations from the input data regardless of the completeness of the sentence. For future works, we plan on evaluating the robustness of our model against other types of noise, such as word reordering, word insertion, and spelling mistakes in sentences. In order to improve the performance of our model, further experiments will be done in search for more appropriate hyperparameters and more complex neural classifiers to substitute the last feedforward network layer." + ], + [ + "This work was partly supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (2016-0-00564, Development of Intelligent Interaction Technology Based on Context Awareness and Human Intention Understanding) and Korea Evaluation Institute of Industrial Technology (KEIT) grant funded by the Korea government (MOTIE) (50%) and the Technology Innovation Program: Industrial Strategic Technology Development Program (No: 10073162) funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea) (50%)." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0085/instruction.md b/qasper-0085/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..9ad5c396df1239f3b64de8ade807a3e0ddbb20f8 --- /dev/null +++ b/qasper-0085/instruction.md @@ -0,0 +1,104 @@ +Name of Paper: Stacked DeBERT: All Attention in Incomplete Data for Text Classification + +Question: Should their approach be applied only when dealing with incomplete data? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Proposed model", + "Dataset ::: Twitter Sentiment Classification", + "Dataset ::: Intent Classification from Text with STT Error", + "Experiments ::: Baseline models", + "Experiments ::: Baseline models ::: NLU service platforms", + "Experiments ::: Baseline models ::: Semantic hashing with classifier", + "Experiments ::: Training specifications", + "Experiments ::: Training specifications ::: NLU service platforms", + "Experiments ::: Training specifications ::: Semantic hashing with classifier", + "Experiments ::: Training specifications ::: BERT", + "Experiments ::: Training specifications ::: Stacked DeBERT", + "Experiments ::: Results on Sentiment Classification from Incorrect Text", + "Experiments ::: Results on Intent Classification from Text with STT Error", + "Conclusion", + "Acknowledgments" + ], + "paragraphs": [ + [ + "Understanding a user's intent and sentiment is of utmost importance for current intelligent chatbots to respond appropriately to human requests. However, current systems are not able to perform to their best capacity when presented with incomplete data, meaning sentences with missing or incorrect words. This scenario is likely to happen when one considers human error done in writing. In fact, it is rather naive to assume that users will always type fully grammatically correct sentences. Panko BIBREF0 goes as far as claiming that human accuracy regarding research paper writing is none when considering the entire document. This has been aggravated with the advent of internet and social networks, which allowed language and modern communication to be been rapidly transformed BIBREF1, BIBREF2. Take Twitter for instance, where information is expected to be readily communicated in short and concise sentences with little to no regard to correct sentence grammar or word spelling BIBREF3.", + "Further motivation can be found in Automatic Speech Recognition (ASR) applications, where high error rates prevail and pose an enormous hurdle in the broad adoption of speech technology by users worldwide BIBREF4. This is an important issue to tackle because, in addition to more widespread user adoption, improving Speech-to-Text (STT) accuracy diminishes error propagation to modules using the recognized text. With that in mind, in order for current systems to improve the quality of their services, there is a need for development of robust intelligent systems that are able to understand a user even when faced with incomplete representation in language.", + "The advancement of deep neural networks have immensely aided in the development of the Natural Language Processing (NLP) domain. Tasks such as text generation, sentence correction, image captioning and text classification, have been possible via models such as Convolutional Neural Networks and Recurrent Neural Networks BIBREF5, BIBREF6, BIBREF7. More recently, state-of-the-art results have been achieved with attention models, more specifically Transformers BIBREF8. Surprisingly, however, there is currently no research on incomplete text classification in the NLP community. Realizing the need of research in that area, we make it the focus of this paper. In this novel task, the model aims to identify the user's intent or sentiment by analyzing a sentence with missing and/or incorrect words. In the sentiment classification task, the model aims to identify the user's sentiment given a tweet, written in informal language and without regards for sentence correctness.", + "Current approaches for Text Classification tasks focus on efficient embedding representations. Kim et al. BIBREF9 use semantically enriched word embeddings to make synonym and antonym word vectors respectively more and less similar in order to improve intent classification performance. Devlin et al. BIBREF10 propose Bidirectional Encoder Representations from Transformers (BERT), a powerful bidirectional language representation model based on Transformers, achieving state-of-the-art results on eleven NLP tasks BIBREF11, including sentiment text classification. Concurrently, Shridhar et al. BIBREF12 also reach state of the art in the intent recognition task using Semantic Hashing for feature representation followed by a neural classifier. All aforementioned approaches are, however, applied to datasets based solely on complete data.", + "The incomplete data problem is usually approached as a reconstruction or imputation task and is most often related to missing numbers imputation BIBREF13. Vincent et al. BIBREF14, BIBREF15 propose to reconstruct clean data from its noisy version by mapping the input to meaningful representations. This approach has also been shown to outperform other models, such as predictive mean matching, random forest, Support Vector Machine (SVM) and Multiple imputation by Chained Equations (MICE), at missing data imputation tasks BIBREF16, BIBREF17. Researchers in those two areas have shown that meaningful feature representation of data is of utter importance for high performance achieving methods. We propose a model that combines the power of BERT in the NLP domain and the strength of denoising strategies in incomplete data reconstruction to tackle the tasks of incomplete intent and sentiment classification. This enables the implementation of a novel encoding scheme, more robust to incomplete data, called Stacked Denoising BERT or Stacked DeBERT. Our approach consists of obtaining richer input representations from input tokens by stacking denoising transformers on an embedding layer with vanilla transformers. The embedding layer and vanilla transformers extract intermediate input features from the input tokens, and the denoising transformers are responsible for obtaining richer input representations from them. By improving BERT with stronger denoising abilities, we are able to reconstruct missing and incorrect words' embeddings and improve classification accuracy. To summarize, our contribution is two-fold:", + "Novel model architecture that is more robust to incomplete data, including missing or incorrect words in text.", + "Proposal of the novel tasks of incomplete intent and sentiment classification from incorrect sentences, and release of corpora related with these tasks.", + "The remainder of this paper is organized in four sections, with Section SECREF2 explaining the proposed model. This is followed by Section SECREF3 which includes a detailed description of the dataset used for training and evaluation purposes and how it was obtained. Section SECREF4 covers the baseline models used for comparison, training specifications and experimental results. Finally, Section SECREF5 wraps up this paper with conclusion and future works." + ], + [ + "We propose Stacked Denoising BERT (DeBERT) as a novel encoding scheming for the task of incomplete intent classification and sentiment classification from incorrect sentences, such as tweets and text with STT error. The proposed model, illustrated in Fig. FIGREF4, is structured as a stacking of embedding layers and vanilla transformer layers, similarly to the conventional BERT BIBREF10, followed by layers of novel denoising transformers. The main purpose of this model is to improve the robustness and efficiency of BERT when applied to incomplete data by reconstructing hidden embeddings from sentences with missing words. By reconstructing these hidden embeddings, we are able to improve the encoding scheme in BERT.", + "The initial part of the model is the conventional BERT, a multi-layer bidirectional Transformer encoder and a powerful language model. During training, BERT is fine-tuned on the incomplete text classification corpus (see Section SECREF3). The first layer pre-processes the input sentence by making it lower-case and by tokenizing it. It also prefixes the sequence of tokens with a special character `[CLS]' and sufixes each sentence with a `[SEP]' character. It is followed by an embedding layer used for input representation, with the final input embedding being a sum of token embedddings, segmentation embeddings and position embeddings. The first one, token embedding layer, uses a vocabulary dictionary to convert each token into a more representative embedding. The segmentation embedding layer indicates which tokens constitute a sentence by signaling either 1 or 0. In our case, since our data are formed of single sentences, the segment is 1 until the first `[SEP]' character appears (indicating segment A) and then it becomes 0 (segment B). The position embedding layer, as the name indicates, adds information related to the token's position in the sentence. This prepares the data to be considered by the layers of vanilla bidirectional transformers, which outputs a hidden embedding that can be used by our novel layers of denoising transformers.", + "Although BERT has shown to perform better than other baseline models when handling incomplete data, it is still not enough to completely and efficiently handle such data. Because of that, there is a need for further improvement of the hidden feature vectors obtained from sentences with missing words. With this purpose in mind, we implement a novel encoding scheme consisting of denoising transformers, which is composed of stacks of multilayer perceptrons for the reconstruction of missing words\u2019 embeddings by extracting more abstract and meaningful hidden feature vectors, and bidirectional transformers for improved embedding representation. The embedding reconstruction step is trained on sentence embeddings extracted from incomplete data $h_{inc}$ as input and embeddings corresponding to its complete version $h_{comp}$ as target. Both input and target are obtained after applying the embedding layers and the vanilla transformers, as indicated in Fig. FIGREF4, and have shape $(N_{bs}, 768, 128)$, where $N_{bs}$ is the batch size, 768 is the original BERT embedding size for a single token, and 128 is the maximum sequence length in a sentence.", + "The stacks of multilayer perceptrons are structured as two sets of three layers with two hidden layers each. The first set is responsible for compressing the $h_{inc}$ into a latent-space representation, extracting more abstract features into lower dimension vectors $z_1$, $z_2$ and $\\mathbf {z}$ with shape $(N_{bs}, 128, 128)$, $(N_{bs}, 32, 128)$, and $(N_{bs}, 12, 128)$, respectively. This process is shown in Eq. (DISPLAY_FORM5):", + "where $f(\\cdot )$ is the parameterized function mapping $h_{inc}$ to the hidden state $\\mathbf {z}$. The second set then respectively reconstructs $z_1$, $z_2$ and $\\mathbf {z}$ into $h_{rec_1}$, $h_{rec_2}$ and $h_{rec}$. This process is shown in Eq. (DISPLAY_FORM6):", + "where $g(\\cdot )$ is the parameterized function that reconstructs $\\mathbf {z}$ as $h_{rec}$.", + "The reconstructed hidden sentence embedding $h_{rec}$ is compared with the complete hidden sentence embedding $h_{comp}$ through a mean square error loss function, as shown in Eq. (DISPLAY_FORM7):", + "After reconstructing the correct hidden embeddings from the incomplete sentences, the correct hidden embeddings are given to bidirectional transformers to generate input representations. The model is then fine-tuned in an end-to-end manner on the incomplete text classification corpus.", + "Classification is done with a feedforward network and softmax activation function. Softmax $\\sigma $ is a discrete probability distribution function for $N_C$ classes, with the sum of the classes probability being 1 and the maximum value being the predicted class. The predicted class can be mathematically calculated as in Eq. (DISPLAY_FORM8):", + "where $o = W t + b$, the output of the feedforward layer used for classification." + ], + [ + "In order to evaluate the performance of our model, we need access to a naturally noisy dataset with real human errors. Poor quality texts obtained from Twitter, called tweets, are then ideal for our task. For this reason, we choose Kaggle's two-class Sentiment140 dataset BIBREF18, which consists of spoken text being used in writing and without strong consideration for grammar or sentence correctness. Thus, it has many mistakes, as specified in Table TABREF11.", + "Even though this corpus has incorrect sentences and their emotional labels, they lack their respective corrected sentences, necessary for the training of our model. In order to obtain this missing information, we outsource native English speakers from an unbiased and anonymous platform, called Amazon Mechanical Turk (MTurk) BIBREF19, which is a paid marketplace for Human Intelligence Tasks (HITs). We use this platform to create tasks for native English speakers to format the original incorrect tweets into correct sentences. Some examples are shown in Table TABREF12.", + "After obtaining the correct sentences, our two-class dataset has class distribution as shown in Table TABREF14. There are 200 sentences used in the training stage, with 100 belonging to the positive sentiment class and 100 to the negative class, and 50 samples being used in the evaluation stage, with 25 negative and 25 positive. This totals in 300 samples, with incorrect and correct sentences combined. Since our goal is to evaluate the model's performance and robustness in the presence of noise, we only consider incorrect data in the testing phase. Note that BERT is a pre-trained model, meaning that small amounts of data are enough for appropriate fine-tuning." + ], + [ + "In the intent classification task, we are presented with a corpus that suffers from the opposite problem of the Twitter sentiment classification corpus. In the intent classification corpus, we have the complete sentences and intent labels but lack their corresponding incomplete sentences, and since our task revolves around text classification in incomplete or incorrect data, it is essential that we obtain this information. To remedy this issue, we apply a Text-to-Speech (TTS) module followed by a Speech-to-Text (STT) module to the complete sentences in order to obtain incomplete sentences with STT error. Due to TTS and STT modules available being imperfect, the resulting sentences have a reasonable level of noise in the form of missing or incorrectly transcribed words. Analysis on this dataset adds value to our work by enabling evaluation of our model's robustness to different rates of data incompleteness.", + "The dataset used to evaluate the models' performance is the Chatbot Natural Language Unerstanding (NLU) Evaluation Corpus, introduced by Braun et al. BIBREF20 to test NLU services. It is a publicly available benchmark and is composed of sentences obtained from a German Telegram chatbot used to answer questions about public transport connections. The dataset has two intents, namely Departure Time and Find Connection with 100 train and 106 test samples, shown in Table TABREF18. Even though English is the main language of the benchmark, this dataset contains a few German station and street names.", + "The incomplete dataset used for training is composed of lower-cased incomplete data obtained by manipulating the original corpora. The incomplete sentences with STT error are obtained in a 2-step process shown in Fig. FIGREF22. The first step is to apply a TTS module to the available complete sentence. Here, we apply gtts , a Google Text-to-Speech python library, and macsay , a terminal command available in Mac OS as say. The second step consists of applying an STT module to the obtained audio files in order to obtain text containing STT errors. The STT module used here was witai , freely available and maintained by Wit.ai. The mentioned TTS and STT modules were chosen according to code availability and whether it's freely available or has high daily usage limitations.", + "Table TABREF24 exemplifies a complete and its respective incomplete sentences with different TTS-STT combinations, thus varying rates of missing and incorrect words. The level of noise in the STT imbued sentences is denoted by a inverted BLEU (iBLEU) score ranging from 0 to 1. The inverted BLEU score is denoted in Eq. (DISPLAY_FORM23):", + "where BLEU is a common metric usually used in machine translation tasks BIBREF21. We decide to showcase that instead of regular BLEU because it is more indicative to the amount of noise in the incomplete text, where the higher the iBLEU, the higher the noise." + ], + [ + "Besides the already mentioned BERT, the following baseline models are also used for comparison." + ], + [ + "We focus on the three following services, where the first two are commercial services and last one is open source with two separate backends: Google Dialogflow (formerly Api.ai) , SAP Conversational AI (formerly Recast.ai) and Rasa (spacy and tensorflow backend) ." + ], + [ + "Shridhar et al. BIBREF12 proposed a word embedding method that doesn't suffer from out-of-vocabulary issues. The authors achieve this by using hash tokens in the alphabet instead of a single word, making it vocabulary independent. For classification, classifiers such as Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Random Forest are used. A complete list of classifiers and training specifications are given in Section SECREF31." + ], + [ + "The baseline and proposed models are each trained 3 separate times for the incomplete intent classification task: complete data and one for each of the TTS-STT combinations (gtts-witai and macsay-witai). Regarding the sentiment classification from incorrect sentences task, the baseline and proposed models are each trained 3 times: original text, corrected text and incorrect with correct texts. The reported F1 scores are the best accuracies obtained from 10 runs." + ], + [ + "No settable training configurations available in the online platforms." + ], + [ + "Trained on 3-gram, feature vector size of 768 as to match the BERT embedding size, and 13 classifiers with parameters set as specified in the authors' paper so as to allow comparison: MLP with 3 hidden layers of sizes $[300, 100, 50]$ respectively; Random Forest with 50 estimators or trees; 5-fold Grid Search with Random Forest classifier and estimator $([50, 60, 70]$; Linear Support Vector Classifier with L1 and L2 penalty and tolerance of $10^{-3}$; Regularized linear classifier with Stochastic Gradient Descent (SGD) learning with regularization term $alpha=10^{-4}$ and L1, L2 and Elastic-Net penalty; Nearest Centroid with Euclidian metric, where classification is done by representing each class with a centroid; Bernoulli Naive Bayes with smoothing parameter $alpha=10^{-2}$; K-means clustering with 2 clusters and L2 penalty; and Logistic Regression classifier with L2 penalty, tolerance of $10^{-4}$ and regularization term of $1.0$. Most often, the best performing classifier was MLP." + ], + [ + "Conventional BERT is a BERT-base-uncased model, meaning that it has 12 transformer blocks $L$, hidden size $H$ of 768, and 12 self-attention heads $A$. The model is fine-tuned with our dataset on 2 Titan X GPUs for 3 epochs with Adam Optimizer, learning rate of $2*10^{-5}$, maximum sequence length of 128, and warm up proportion of $0.1$. The train batch size is 4 for the Twitter Sentiment Corpus and 8 for the Chatbot Intent Classification Corpus." + ], + [ + "Our proposed model is trained in end-to-end manner on 2 Titan X GPUs, with training time depending on the size of the dataset and train batch size. The stack of multilayer perceptrons are trained for 100 and 1,000 epochs with Adam Optimizer, learning rate of $10^{-3}$, weight decay of $10^{-5}$, MSE loss criterion and batch size the same as BERT (4 for the Twitter Sentiment Corpus and 8 for the Chatbot Intent Classification Corpus)." + ], + [ + "Experimental results for the Twitter Sentiment Classification task on Kaggle's Sentiment140 Corpus dataset, displayed in Table TABREF37, show that our model has better F1-micros scores, outperforming the baseline models by 6$\\%$ to 8$\\%$. We evaluate our model and baseline models on three versions of the dataset. The first one (Inc) only considers the original data, containing naturally incorrect tweets, and achieves accuracy of 80$\\%$ against BERT's 72$\\%$. The second version (Corr) considers the corrected tweets, and shows higher accuracy given that it is less noisy. In that version, Stacked DeBERT achieves 82$\\%$ accuracy against BERT's 76$\\%$, an improvement of 6$\\%$. In the last case (Inc+Corr), we consider both incorrect and correct tweets as input to the models in hopes of improving performance. However, the accuracy was similar to the first aforementioned version, 80$\\%$ for our model and 74$\\%$ for the second highest performing model. Since the first and last corpus gave similar performances with our model, we conclude that the Twitter dataset does not require complete sentences to be given as training input, in addition to the original naturally incorrect tweets, in order to better model the noisy sentences.", + "In addition to the overall F1-score, we also present a confusion matrix, in Fig. FIGREF38, with the per-class F1-scores for BERT and Stacked DeBERT. The normalized confusion matrix plots the predicted labels versus the target/target labels. Similarly to Table TABREF37, we evaluate our model with the original Twitter dataset, the corrected version and both original and corrected tweets. It can be seen that our model is able to improve the overall performance by improving the accuracy of the lower performing classes. In the Inc dataset, the true class 1 in BERT performs with approximately 50%. However, Stacked DeBERT is able to improve that to 72%, although to a cost of a small decrease in performance of class 0. A similar situation happens in the remaining two datasets, with improved accuracy in class 0 from 64% to 84% and 60% to 76% respectively." + ], + [ + "Experimental results for the Intent Classification task on the Chatbot NLU Corpus with STT error can be seen in Table TABREF40. When presented with data containing STT error, our model outperforms all baseline models in both combinations of TTS-STT: gtts-witai outperforms the second placing baseline model by 0.94% with F1-score of 97.17%, and macsay-witai outperforms the next highest achieving model by 1.89% with F1-score of 96.23%.", + "The table also indicates the level of noise in each dataset with the already mentioned iBLEU score, where 0 means no noise and higher values mean higher quantity of noise. As expected, the models' accuracy degrade with the increase in noise, thus F1-scores of gtts-witai are higher than macsay-witai. However, while the other models decay rapidly in the presence of noise, our model does not only outperform them but does so with a wider margin. This is shown with the increasing robustness curve in Fig. FIGREF41 and can be demonstrated by macsay-witai outperforming the baseline models by twice the gap achieved by gtts-witai.", + "Further analysis of the results in Table TABREF40 show that, BERT decay is almost constant with the addition of noise, with the difference between the complete data and gtts-witai being 1.88 and gtts-witai and macsay-witai being 1.89. Whereas in Stacked DeBERT, that difference is 1.89 and 0.94 respectively. This is stronger indication of our model's robustness in the presence of noise.", + "Additionally, we also present Fig. FIGREF42 with the normalized confusion matrices for BERT and Stacked DeBERT for sentences containing STT error. Analogously to the Twitter Sentiment Classification task, the per-class F1-scores show that our model is able to improve the overall performance by improving the accuracy of one class while maintaining the high-achieving accuracy of the second one." + ], + [ + "In this work, we proposed a novel deep neural network, robust to noisy text in the form of sentences with missing and/or incorrect words, called Stacked DeBERT. The idea was to improve the accuracy performance by improving the representation ability of the model with the implementation of novel denoising transformers. More specifically, our model was able to reconstruct hidden embeddings from their respective incomplete hidden embeddings. Stacked DeBERT was compared against three NLU service platforms and two other machine learning methods, namely BERT and Semantic Hashing with neural classifier. Our model showed better performance when evaluated on F1 scores in both Twitter sentiment and intent text with STT error classification tasks. The per-class F1 score was also evaluated in the form of normalized confusion matrices, showing that our model was able to improve the overall performance by better balancing the accuracy of each class, trading-off small decreases in high achieving class for significant improvements in lower performing ones. In the Chatbot dataset, accuracy improvement was achieved even without trade-off, with the highest achieving classes maintaining their accuracy while the lower achieving class saw improvement. Further evaluation on the F1-scores decay in the presence of noise demonstrated that our model is more robust than the baseline models when considering noisy data, be that in the form of incorrect sentences or sentences with STT error. Not only that, experiments on the Twitter dataset also showed improved accuracy in clean data, with complete sentences. We infer that this is due to our model being able to extract richer data representations from the input data regardless of the completeness of the sentence. For future works, we plan on evaluating the robustness of our model against other types of noise, such as word reordering, word insertion, and spelling mistakes in sentences. In order to improve the performance of our model, further experiments will be done in search for more appropriate hyperparameters and more complex neural classifiers to substitute the last feedforward network layer." + ], + [ + "This work was partly supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (2016-0-00564, Development of Intelligent Interaction Technology Based on Context Awareness and Human Intention Understanding) and Korea Evaluation Institute of Industrial Technology (KEIT) grant funded by the Korea government (MOTIE) (50%) and the Technology Innovation Program: Industrial Strategic Technology Development Program (No: 10073162) funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea) (50%)." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0102/instruction.md b/qasper-0102/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..8a2af3f65ca472e61f9b4ae4e4bff6fb8dc49464 --- /dev/null +++ b/qasper-0102/instruction.md @@ -0,0 +1,127 @@ +Name of Paper: Unsupervised Machine Commenting with Neural Variational Topic Model + +Question: Which paired corpora did they use in the other experiment? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Machine Commenting", + "Challenges", + "Solutions", + "Proposed Approach", + "Retrieval-based Commenting", + "Neural Variational Topic Model", + "Training", + "Datasets", + "Implementation Details", + "Baselines", + "Retrieval Evaluation", + "Generative Evaluation", + "Analysis and Discussion", + "Article Comment", + "Topic Model and Variational Auto-Encoder", + "Conclusion" + ], + "paragraphs": [ + [ + "Making article comments is a fundamental ability for an intelligent machine to understand the article and interact with humans. It provides more challenges because commenting requires the abilities of comprehending the article, summarizing the main ideas, mining the opinions, and generating the natural language. Therefore, machine commenting is an important problem faced in building an intelligent and interactive agent. Machine commenting is also useful in improving the activeness of communities, including online forums and news websites. Article comments can provide extended information and external opinions for the readers to have a more comprehensive understanding of the article. Therefore, an article with more informative and interesting comments will attract more attention from readers. Moreover, machine commenting can kick off the discussion about an article or a topic, which helps increase user engagement and interaction between the readers and authors.", + "Because of the advantage and importance described above, more recent studies have focused on building a machine commenting system with neural models BIBREF0 . One bottleneck of neural machine commenting models is the requirement of a large parallel dataset. However, the naturally paired commenting dataset is loosely paired. Qin et al. QinEA2018 were the first to propose the article commenting task and an article-comment dataset. The dataset is crawled from a news website, and they sample 1,610 article-comment pairs to annotate the relevance score between articles and comments. The relevance score ranges from 1 to 5, and we find that only 6.8% of the pairs have an average score greater than 4. It indicates that the naturally paired article-comment dataset contains a lot of loose pairs, which is a potential harm to the supervised models. Besides, most articles and comments are unpaired on the Internet. For example, a lot of articles do not have the corresponding comments on the news websites, and the comments regarding the news are more likely to appear on social media like Twitter. Since comments on social media are more various and recent, it is important to exploit these unpaired data.", + "Another issue is that there is a semantic gap between articles and comments. In machine translation and text summarization, the target output mainly shares the same points with the source input. However, in article commenting, the comment does not always tell the same thing as the corresponding article. Table TABREF1 shows an example of an article and several corresponding comments. The comments do not directly tell what happened in the news, but talk about the underlying topics (e.g. NBA Christmas Day games, LeBron James). However, existing methods for machine commenting do not model the topics of articles, which is a potential harm to the generated comments.", + "To this end, we propose an unsupervised neural topic model to address both problems. For the first problem, we completely remove the need of parallel data and propose a novel unsupervised approach to train a machine commenting system, relying on nothing but unpaired articles and comments. For the second issue, we bridge the articles and comments with their topics. Our model is based on a retrieval-based commenting framework, which uses the news as the query to retrieve the comments by the similarity of their topics. The topic is represented with a variational topic, which is trained in an unsupervised manner.", + "The contributions of this work are as follows:" + ], + [ + "In this section, we highlight the research challenges of machine commenting, and provide some solutions to deal with these challenges." + ], + [ + "Here, we first introduce the challenges of building a well-performed machine commenting system.", + "The generative model, such as the popular sequence-to-sequence model, is a direct choice for supervised machine commenting. One can use the title or the content of the article as the encoder input, and the comments as the decoder output. However, we find that the mode collapse problem is severed with the sequence-to-sequence model. Despite the input articles being various, the outputs of the model are very similar. The reason mainly comes from the contradiction between the complex pattern of generating comments and the limited parallel data. In other natural language generation tasks, such as machine translation and text summarization, the target output of these tasks is strongly related to the input, and most of the required information is involved in the input text. However, the comments are often weakly related to the input articles, and part of the information in the comments is external. Therefore, it requires much more paired data for the supervised model to alleviate the mode collapse problem.", + "One article can have multiple correct comments, and these comments can be very semantically different from each other. However, in the training set, there is only a part of the correct comments, so the other correct comments will be falsely regarded as the negative samples by the supervised model. Therefore, many interesting and informative comments will be discouraged or neglected, because they are not paired with the articles in the training set.", + "There is a semantic gap between articles and comments. In machine translation and text summarization, the target output mainly shares the same points with the source input. However, in article commenting, the comments often have some external information, or even tell an opposite opinion from the articles. Therefore, it is difficult to automatically mine the relationship between articles and comments." + ], + [ + "Facing the above challenges, we provide three solutions to the problems.", + "Given a large set of candidate comments, the retrieval model can select some comments by matching articles with comments. Compared with the generative model, the retrieval model can achieve more promising performance. First, the retrieval model is less likely to suffer from the mode collapse problem. Second, the generated comments are more predictable and controllable (by changing the candidate set). Third, the retrieval model can be combined with the generative model to produce new comments (by adding the outputs of generative models to the candidate set).", + "The unsupervised learning method is also important for machine commenting to alleviate the problems descried above. Unsupervised learning allows the model to exploit more data, which helps the model to learn more complex patterns of commenting and improves the generalization of the model. Many comments provide some unique opinions, but they do not have paired articles. For example, many interesting comments on social media (e.g. Twitter) are about recent news, but require redundant work to match these comments with the corresponding news articles. With the help of the unsupervised learning method, the model can also learn to generate these interesting comments. Additionally, the unsupervised learning method does not require negative samples in the training stage, so that it can alleviate the negative sampling bias.", + "Although there is semantic gap between the articles and the comments, we find that most articles and comments share the same topics. Therefore, it is possible to bridge the semantic gap by modeling the topics of both articles and comments. It is also similar to how humans generate comments. Humans do not need to go through the whole article but are capable of making a comment after capturing the general topics." + ], + [ + "We now introduce our proposed approach as an implementation of the solutions above. We first give the definition and the denotation of the problem. Then, we introduce the retrieval-based commenting framework. After that, a neural variational topic model is introduced to model the topics of the comments and the articles. Finally, semi-supervised training is used to combine the advantage of both supervised and unsupervised learning." + ], + [ + "Given an article, the retrieval-based method aims to retrieve a comment from a large pool of candidate comments. The article consists of a title INLINEFORM0 and a body INLINEFORM1 . The comment pool is formed from a large scale of candidate comments INLINEFORM2 , where INLINEFORM3 is the number of the unique comments in the pool. In this work, we have 4.5 million human comments in the candidate set, and the comments are various, covering different topics from pets to sports.", + "The retrieval-based model should score the matching between the upcoming article and each comments, and return the comments which is matched with the articles the most. Therefore, there are two main challenges in retrieval-based commenting. One is how to evaluate the matching of the articles and comments. The other is how to efficiently compute the matching scores because the number of comments in the pool is large.", + "To address both problems, we select the \u201cdot-product\u201d operation to compute matching scores. More specifically, the model first computes the representations of the article INLINEFORM0 and the comments INLINEFORM1 . Then the score between article INLINEFORM2 and comment INLINEFORM3 is computed with the \u201cdot-product\u201d operation: DISPLAYFORM0 ", + "The dot-product scoring method has proven a successful in a matching model BIBREF1 . The problem of finding datapoints with the largest dot-product values is called Maximum Inner Product Search (MIPS), and there are lots of solutions to improve the efficiency of solving this problem. Therefore, even when the number of candidate comments is very large, the model can still find comments with the highest efficiency. However, the study of the MIPS is out of the discussion in this work. We refer the readers to relevant articles for more details about the MIPS BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 . Another advantage of the dot-product scoring method is that it does not require any extra parameters, so it is more suitable as a part of the unsupervised model." + ], + [ + "We obtain the representations of articles INLINEFORM0 and comments INLINEFORM1 with a neural variational topic model. The neural variational topic model is based on the variational autoencoder framework, so it can be trained in an unsupervised manner. The model encodes the source text into a representation, from which it reconstructs the text.", + "We concatenate the title and the body to represent the article. In our model, the representations of the article and the comment are obtained in the same way. For simplicity, we denote both the article and the comment as \u201cdocument\u201d. Since the articles are often very long (more than 200 words), we represent the documents into bag-of-words, for saving both the time and memory cost. We denote the bag-of-words representation as INLINEFORM0 , where INLINEFORM1 is the one-hot representation of the word at INLINEFORM2 position, and INLINEFORM3 is the number of words in the vocabulary. The encoder INLINEFORM4 compresses the bag-of-words representations INLINEFORM5 into topic representations INLINEFORM6 : DISPLAYFORM0 DISPLAYFORM1 ", + "where INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , and INLINEFORM3 are the trainable parameters. Then the decoder INLINEFORM4 reconstructs the documents by independently generating each words in the bag-of-words: DISPLAYFORM0 DISPLAYFORM1 ", + "where INLINEFORM0 is the number of words in the bag-of-words, and INLINEFORM1 is a trainable matrix to map the topic representation into the word distribution.", + "In order to model the topic information, we use a Dirichlet prior rather than the standard Gaussian prior. However, it is difficult to develop an effective reparameterization function for the Dirichlet prior to train VAE. Therefore, following BIBREF6 , we use the Laplace approximation BIBREF7 to Dirichlet prior INLINEFORM0 : DISPLAYFORM0 DISPLAYFORM1 ", + "where INLINEFORM0 denotes the logistic normal distribution, INLINEFORM1 is the number of topics, and INLINEFORM2 is a parameter vector. Then, the variational lower bound is written as: DISPLAYFORM0 ", + "where the first term is the KL-divergence loss and the second term is the reconstruction loss. The mean INLINEFORM0 and the variance INLINEFORM1 are computed as follows: DISPLAYFORM0 DISPLAYFORM1 ", + "We use the INLINEFORM0 and INLINEFORM1 to generate the samples INLINEFORM2 by sampling INLINEFORM3 , from which we reconstruct the input INLINEFORM4 .", + "At the training stage, we train the neural variational topic model with the Eq. EQREF22 . At the testing stage, we use INLINEFORM0 to compute the topic representations of the article INLINEFORM1 and the comment INLINEFORM2 ." + ], + [ + "In addition to the unsupervised training, we explore a semi-supervised training framework to combine the proposed unsupervised model and the supervised model. In this scenario we have a paired dataset that contains article-comment parallel contents INLINEFORM0 , and an unpaired dataset that contains the documents (articles or comments) INLINEFORM1 . The supervised model is trained on INLINEFORM2 so that we can learn the matching or mapping between articles and comments. By sharing the encoder of the supervised model and the unsupervised model, we can jointly train both the models with a joint objective function: DISPLAYFORM0 ", + "where INLINEFORM0 is the loss function of the unsupervised learning (Eq. refloss), INLINEFORM1 is the loss function of the supervised learning (e.g. the cross-entropy loss of Seq2Seq model), and INLINEFORM2 is a hyper-parameter to balance two parts of the loss function. Hence, the model is trained on both unpaired data INLINEFORM3 , and paired data INLINEFORM4 ." + ], + [ + "We select a large-scale Chinese dataset BIBREF0 with millions of real comments and a human-annotated test set to evaluate our model. The dataset is collected from Tencent News, which is one of the most popular Chinese websites for news and opinion articles. The dataset consists of 198,112 news articles. Each piece of news contains a title, the content of the article, and a list of the users' comments. Following the previous work BIBREF0 , we tokenize all text with the popular python package Jieba, and filter out short articles with less than 30 words in content and those with less than 20 comments. The dataset is split into training/validation/test sets, and they contain 191,502/5,000/1,610 pieces of news, respectively. The whole dataset has a vocabulary size of 1,858,452. The average lengths of the article titles and content are 15 and 554 Chinese words. The average comment length is 17 words." + ], + [ + "The hidden size of the model is 512, and the batch size is 64. The number of topics INLINEFORM0 is 100. The weight INLINEFORM1 in Eq. EQREF26 is 1.0 under the semi-supervised setting. We prune the vocabulary, and only leave 30,000 most frequent words in the vocabulary. We train the model for 20 epochs with the Adam optimizing algorithms BIBREF8 . In order to alleviate the KL vanishing problem, we set the initial learning to INLINEFORM2 , and use batch normalization BIBREF9 in each layer. We also gradually increase the KL term from 0 to 1 after each epoch." + ], + [ + "We compare our model with several unsupervised models and supervised models.", + "Unsupervised baseline models are as follows:", + "TF-IDF (Lexical, Non-Neural) is an important unsupervised baseline. We use the concatenation of the title and the body as the query to retrieve the candidate comment set by means of the similarity of the tf-idf value. The model is trained on unpaired articles and comments, which is the same as our proposed model.", + "LDA (Topic, Non-Neural) is a popular unsupervised topic model, which discovers the abstract \"topics\" that occur in a collection of documents. We train the LDA with the articles and comments in the training set. The model retrieves the comments by the similarity of the topic representations.", + "NVDM (Lexical, Neural) is a VAE-based approach for document modeling BIBREF10 . We compare our model with this baseline to demonstrate the effect of modeling topic.", + "The supervised baseline models are:", + "S2S (Generative) BIBREF11 is a supervised generative model based on the sequence-to-sequence network with the attention mechanism BIBREF12 . The model uses the titles and the bodies of the articles as the encoder input, and generates the comments with the decoder.", + "IR (Retrieval) BIBREF0 is a supervised retrieval-based model, which trains a convolutional neural network (CNN) to take the articles and a comment as inputs, and output the relevance score. The positive instances for training are the pairs in the training set, and the negative instances are randomly sampled using the negative sampling technique BIBREF13 ." + ], + [ + "For text generation, automatically evaluate the quality of the generated text is an open problem. In particular, the comment of a piece of news can be various, so it is intractable to find out all the possible references to be compared with the model outputs. Inspired by the evaluation methods of dialogue models, we formulate the evaluation as a ranking problem. Given a piece of news and a set of candidate comments, the comment model should return the rank of the candidate comments. The candidate comment set consists of the following parts:", + "Correct: The ground-truth comments of the corresponding news provided by the human.", + "Plausible: The 50 most similar comments to the news. We use the news as the query to retrieve the comments that appear in the training set based on the cosine similarity of their tf-idf values. We select the top 50 comments that are not the correct comments as the plausible comments.", + "Popular: The 50 most popular comments from the dataset. We count the frequency of each comments in the training set, and select the 50 most frequent comments to form the popular comment set. The popular comments are the general and meaningless comments, such as \u201cYes\u201d, \u201cGreat\u201d, \u201cThat's right', and \u201cMake Sense\u201d. These comments are dull and do not carry any information, so they are regarded as incorrect comments.", + "Random: After selecting the correct, plausible, and popular comments, we fill the candidate set with randomly selected comments from the training set so that there are 200 unique comments in the candidate set.", + "Following previous work, we measure the rank in terms of the following metrics:", + "Recall@k: The proportion of human comments found in the top-k recommendations.", + "Mean Rank (MR): The mean rank of the human comments.", + "Mean Reciprocal Rank (MRR): The mean reciprocal rank of the human comments.", + "The evaluation protocol is compatible with both retrieval models and generative models. The retrieval model can directly rank the comments by assigning a score for each comment, while the generative model can rank the candidates by the model's log-likelihood score.", + "Table TABREF31 shows the performance of our models and the baselines in retrieval evaluation. We first compare our proposed model with other popular unsupervised methods, including TF-IDF, LDA, and NVDM. TF-IDF retrieves the comments by similarity of words rather than the semantic meaning, so it achieves low scores on all the retrieval metrics. The neural variational document model is based on the neural VAE framework. It can capture the semantic information, so it has better performance than the TF-IDF model. LDA models the topic information, and captures the deeper relationship between the article and comments, so it achieves improvement in all relevance metrics. Finally, our proposed model outperforms all these unsupervised methods, mainly because the proposed model learns both the semantics and the topic information.", + "We also evaluate two popular supervised models, i.e. seq2seq and IR. Since the articles are very long, we find either RNN-based or CNN-based encoders cannot hold all the words in the articles, so it requires limiting the length of the input articles. Therefore, we use an MLP-based encoder, which is the same as our model, to encode the full length of articles. In our preliminary experiments, the MLP-based encoder with full length articles achieves better scores than the RNN/CNN-based encoder with limited length articles. It shows that the seq2seq model gets low scores on all relevant metrics, mainly because of the mode collapse problem as described in Section Challenges. Unlike seq2seq, IR is based on a retrieval framework, so it achieves much better performance." + ], + [ + "Following previous work BIBREF0 , we evaluate the models under the generative evaluation setting. The retrieval-based models generate the comments by selecting a comment from the candidate set. The candidate set contains the comments in the training set. Unlike the retrieval evaluation, the reference comments may not appear in the candidate set, which is closer to real-world settings. Generative-based models directly generate comments without a candidate set. We compare the generated comments of either the retrieval-based models or the generative models with the five reference comments. We select four popular metrics in text generation to compare the model outputs with the references: BLEU BIBREF14 , METEOR BIBREF15 , ROUGE BIBREF16 , CIDEr BIBREF17 .", + "Table TABREF32 shows the performance for our models and the baselines in generative evaluation. Similar to the retrieval evaluation, our proposed model outperforms the other unsupervised methods, which are TF-IDF, NVDM, and LDA, in generative evaluation. Still, the supervised IR achieves better scores than the seq2seq model. With the help of our proposed model, both IR and S2S achieve an improvement under the semi-supervised scenarios." + ], + [ + "We analyze the performance of the proposed method under the semi-supervised setting. We train the supervised IR model with different numbers of paired data. Figure FIGREF39 shows the curve (blue) of the recall1 score. As expected, the performance grows as the paired dataset becomes larger. We further combine the supervised IR with our unsupervised model, which is trained with full unpaired data (4.8M) and different number of paired data (from 50K to 4.8M). It shows that IR+Proposed can outperform the supervised IR model given the same paired dataset. It concludes that the proposed model can exploit the unpaired data to further improve the performance of the supervised model.", + "Although our proposed model can achieve better performance than previous models, there are still remaining two questions: why our model can outperform them, and how to further improve the performance. To address these queries, we perform error analysis to analyze the error types of our model and the baseline models. We select TF-IDF, S2S, and IR as the representative baseline models. We provide 200 unique comments as the candidate sets, which consists of four types of comments as described in the above retrieval evaluation setting: Correct, Plausible, Popular, and Random. We rank the candidate comment set with four models (TF-IDF, S2S, IR, and Proposed+IR), and record the types of top-1 comments.", + "Figure FIGREF40 shows the percentage of different types of top-1 comments generated by each model. It shows that TF-IDF prefers to rank the plausible comments as the top-1 comments, mainly because it matches articles with the comments based on the similarity of the lexicon. Therefore, the plausible comments, which are more similar in the lexicon, are more likely to achieve higher scores than the correct comments. It also shows that the S2S model is more likely to rank popular comments as the top-1 comments. The reason is the S2S model suffers from the mode collapse problem and data sparsity, so it prefers short and general comments like \u201cGreat\u201d or \u201cThat's right\u201d, which appear frequently in the training set. The correct comments often contain new information and different language models from the training set, so they do not obtain a high score from S2S.", + "IR achieves better performance than TF-IDF and S2S. However, it still suffers from the discrimination between the plausible comments and correct comments. This is mainly because IR does not explicitly model the underlying topics. Therefore, the correct comments which are more relevant in topic with the articles get lower scores than the plausible comments which are more literally relevant with the articles. With the help of our proposed model, proposed+IR achieves the best performance, and achieves a better accuracy to discriminate the plausible comments and the correct comments. Our proposed model incorporates the topic information, so the correct comments which are more similar to the articles in topic obtain higher scores than the other types of comments. According to the analysis of the error types of our model, we still need to focus on avoiding predicting the plausible comments." + ], + [ + "There are few studies regarding machine commenting. Qin et al. QinEA2018 is the first to propose the article commenting task and a dataset, which is used to evaluate our model in this work. More studies about the comments aim to automatically evaluate the quality of the comments. Park et al. ParkSDE16 propose a system called CommentIQ, which assist the comment moderators in identifying high quality comments. Napoles et al. NapolesTPRP17 propose to discriminating engaging, respectful, and informative conversations. They present a Yahoo news comment threads dataset and annotation scheme for the new task of identifying \u201cgood\u201d online conversations. More recently, Kolhaatkar and Taboada KolhatkarT17 propose a model to classify the comments into constructive comments and non-constructive comments. In this work, we are also inspired by the recent related work of natural language generation models BIBREF18 , BIBREF19 ." + ], + [ + "Topic models BIBREF20 are among the most widely used models for learning unsupervised representations of text. One of the most popular approaches for modeling the topics of the documents is the Latent Dirichlet Allocation BIBREF21 , which assumes a discrete mixture distribution over topics is sampled from a Dirichlet prior shared by all documents. In order to explore the space of different modeling assumptions, some black-box inference methods BIBREF22 , BIBREF23 are proposed and applied to the topic models.", + "Kingma and Welling vae propose the Variational Auto-Encoder (VAE) where the generative model and the variational posterior are based on neural networks. VAE has recently been applied to modeling the representation and the topic of the documents. Miao et al. NVDM model the representation of the document with a VAE-based approach called the Neural Variational Document Model (NVDM). However, the representation of NVDM is a vector generated from a Gaussian distribution, so it is not very interpretable unlike the multinomial mixture in the standard LDA model. To address this issue, Srivastava and Sutton nvlda propose the NVLDA model that replaces the Gaussian prior with the Logistic Normal distribution to approximate the Dirichlet prior and bring the document vector into the multinomial space. More recently, Nallapati et al. sengen present a variational auto-encoder approach which models the posterior over the topic assignments to sentences using an RNN." + ], + [ + "We explore a novel way to train a machine commenting model in an unsupervised manner. According to the properties of the task, we propose using the topics to bridge the semantic gap between articles and comments. We introduce a variation topic model to represent the topics, and match the articles and comments by the similarity of their topics. Experiments show that our topic-based approach significantly outperforms previous lexicon-based models. The model can also profit from paired corpora and achieves state-of-the-art performance under semi-supervised scenarios." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0103/instruction.md b/qasper-0103/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..e2f0c28cc9def6d80f0a908d08e871a19d0691cb --- /dev/null +++ b/qasper-0103/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Unsupervised Machine Commenting with Neural Variational Topic Model + +Question: By how much does their system outperform the lexicon-based models? \ No newline at end of file diff --git a/qasper-0105/instruction.md b/qasper-0105/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..191c7746e2b6362b53d022617d2b9f99ac8ebf0a --- /dev/null +++ b/qasper-0105/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Unsupervised Machine Commenting with Neural Variational Topic Model + +Question: How many comments were used? \ No newline at end of file diff --git a/qasper-0132/instruction.md b/qasper-0132/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..b2f79d050f786ed6a6a8a2fc8ceea48b4879f174 --- /dev/null +++ b/qasper-0132/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: DisSim: A Discourse-Aware Syntactic Text Simplification Frameworkfor English and German + +Question: Is the semantic hierarchy representation used for any task? \ No newline at end of file diff --git a/qasper-0133/instruction.md b/qasper-0133/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..8163ebb3a035ac1851eeab9af11675a306791d59 --- /dev/null +++ b/qasper-0133/instruction.md @@ -0,0 +1,56 @@ +Name of Paper: DisSim: A Discourse-Aware Syntactic Text Simplification Frameworkfor English and German + +Question: What are the corpora used for the task? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "System Description", + "System Description ::: Split into Minimal Propositions", + "System Description ::: Establish a Semantic Hierarchy", + "System Description ::: Establish a Semantic Hierarchy ::: Constituency Type Classification.", + "System Description ::: Establish a Semantic Hierarchy ::: Rhetorical Relation Identification.", + "Usage", + "Experiments", + "Application in Downstream Tasks", + "Conclusion" + ], + "paragraphs": [ + [ + "We developed a syntactic text simplification (TS) approach that can be used as a preprocessing step to facilitate and improve the performance of a wide range of artificial intelligence (AI) tasks, such as Machine Translation, Information Extraction (IE) or Text Summarization. Since shorter sentences are generally better processed by natural language processing (NLP) systems BIBREF0, the goal of our approach is to break down a complex source sentence into a set of minimal propositions, i.e. a sequence of sound, self-contained utterances, with each of them presenting a minimal semantic unit that cannot be further decomposed into meaningful propositions BIBREF1.", + "However, any sound and coherent text is not simply a loose arrangement of self-contained units, but rather a logical structure of utterances that are semantically connected BIBREF2. Consequently, when carrying out syntactic simplification operations without considering discourse implications, the rewriting may easily result in a disconnected sequence of simplified sentences that lack important contextual information, making the text harder to interpret. Thus, in order to preserve the coherence structure and, hence, the interpretability of the input, we developed a discourse-aware TS approach based on Rhetorical Structure Theory (RST) BIBREF3. It establishes a contextual hierarchy between the split components, and identifies and classifies the semantic relationship that holds between them. In that way, a complex source sentence is turned into a so-called discourse tree, consisting of a set of hierarchically ordered and semantically interconnected sentences that present a simplified syntax which is easier to process for downstream semantic applications and may support a faster generalization in machine learning tasks." + ], + [ + "We present DisSim, a discourse-aware sentence splitting approach for English and German that creates a semantic hierarchy of simplified sentences. It takes a sentence as input and performs a recursive transformation process that is based upon a small set of 35 hand-crafted grammar rules for the English version and 29 rules for the German approach. These patterns were heuristically determined in a comprehensive linguistic analysis and encode syntactic and lexical features that can be derived from a sentence's parse tree. Each rule specifies (1) how to split up and rephrase the input into structurally simplified sentences and (2) how to set up a semantic hierarchy between them. They are recursively applied on a given source sentence in a top-down fashion. When no more rule matches, the algorithm stops and returns the generated discourse tree." + ], + [ + "In a first step, source sentences that present a complex linguistic form are turned into clean, compact structures by decomposing clausal and phrasal components. For this purpose, the transformation rules encode both the splitting points and rephrasing procedure for reconstructing proper sentences." + ], + [ + "Each split will create two or more sentences with a simplified syntax. To establish a semantic hierarchy between them, two subtasks are carried out:" + ], + [ + "First, we set up a contextual hierarchy between the split sentences by connecting them with information about their hierarchical level, similar to the concept of nuclearity in RST. For this purpose, we distinguish core sentences (nuclei), which carry the key information of the input, from accompanying contextual sentences (satellites) that disclose additional information about it. To differentiate between those two types of constituents, the transformation patterns encode a simple syntax-based approach where subordinate clauses/phrases are classified as context sentences, while superordinate as well as coordinate clauses/phrases are labelled as core." + ], + [ + "Second, we aim to restore the semantic relationship between the disembedded components. For this purpose, we identify and classify the rhetorical relations that hold between the simplified sentences, making use of both syntactic features, which are derived from the input's parse tree structure, and lexical features in the form of cue phrases. Following the work of Taboada13, they are mapped to a predefined list of rhetorical cue words to infer the type of rhetorical relation." + ], + [ + "DisSim can be either used as a Java API, imported as a Maven dependency, or as a service which we provide through a command line interface or a REST-like web service that can be deployed via docker. It takes as input NL text in the form of a single sentence. Alternatively, a file containing a sequence of sentences can be loaded. The result of the transformation process is either written to the console or stored in a specified output file in JSON format. We also provide a browser-based user interface, where the user can directly type in sentences to be processed (see Figure FIGREF1)." + ], + [ + "For the English version, we performed both a thorough manual analysis and automatic evaluation across three commonly used TS datasets from two different domains in order to assess the performance of our framework with regard to the sentence splitting subtask. The results show that our proposed sentence splitting approach outperforms the state of the art in structural TS, returning fine-grained simplified sentences that achieve a high level of grammaticality and preserve the meaning of the input. The full evaluation methodology and detailed results are reported in niklaus-etal-2019-transforming. In addition, a comparative analysis with the annotations contained in the RST Discourse Treebank BIBREF6 demonstrates that we are able to capture the contextual hierarchy between the split sentences with a precision of almost 90% and reach an average precision of approximately 70% for the classification of the rhetorical relations that hold between them. The evaluation of the German version is in progress." + ], + [ + "An extrinsic evaluation was carried out on the task of Open IE BIBREF7. It revealed that when applying DisSim as a preprocessing step, the performance of state-of-the-art Open IE systems can be improved by up to 346% in precision and 52% in recall, i.e. leading to a lower information loss and a higher accuracy of the extracted relations. For details, the interested reader may refer to niklaus-etal-2019-transforming.", + "Moreover, most current Open IE approaches output only a loose arrangement of extracted tuples that are hard to interpret as they ignore the context under which a proposition is complete and correct and thus lack the expressiveness needed for a proper interpretation of complex assertions BIBREF8. As illustrated in Figure FIGREF9, with the help of the semantic hierarchy generated by our discourse-aware sentence splitting approach the output of Open IE systems can be easily enriched with contextual information that allows to restore the semantic relationship between a set of propositions and, hence, preserve their interpretability in downstream tasks." + ], + [ + "We developed and implemented a discourse-aware syntactic TS approach that recursively splits and rephrases complex English or German sentences into a semantic hierarchy of simplified sentences. The resulting lightweight semantic representation can be used to facilitate and improve a variety of AI tasks." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0134/instruction.md b/qasper-0134/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..53a438912df3299b25aff57932006003a8f1d2b2 --- /dev/null +++ b/qasper-0134/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: DisSim: A Discourse-Aware Syntactic Text Simplification Frameworkfor English and German + +Question: Is the model evaluated? \ No newline at end of file diff --git a/qasper-0135/instruction.md b/qasper-0135/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..06fc82506536c9bf55c821c1d1e0cdef3e3cef1d --- /dev/null +++ b/qasper-0135/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Learning Word Embeddings from the Portuguese Twitter Stream: A Study of some Practical Aspects + +Question: What new metrics are suggested to track progress? \ No newline at end of file diff --git a/qasper-0150/instruction.md b/qasper-0150/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..f0ea7617dd7ed9da8b7b39270f16ec9adf0aceef --- /dev/null +++ b/qasper-0150/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Learning Supervised Topic Models for Classification and Regression from Crowds + +Question: what are the advantages of the proposed model? \ No newline at end of file diff --git a/qasper-0151/instruction.md b/qasper-0151/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..21e6a425b3e59b85d141701bb2500e8d79d9016c --- /dev/null +++ b/qasper-0151/instruction.md @@ -0,0 +1,201 @@ +Name of Paper: Learning Supervised Topic Models for Classification and Regression from Crowds + +Question: what are the state of the art approaches? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Supervised topic models", + "Learning from multiple annotators", + "Classification model", + "Proposed model", + "Approximate inference", + "Parameter estimation", + "Stochastic variational inference", + "Document classification", + "Regression model", + "Experiments", + "Classification", + "Regression", + "Conclusion", + "Acknowledgment" + ], + "paragraphs": [ + [ + "Topic models, such as latent Dirichlet allocation (LDA), allow us to analyze large collections of documents by revealing their underlying themes, or topics, and how each document exhibits them BIBREF0 . Therefore, it is not surprising that topic models have become a standard tool in data analysis, with many applications that go even beyond their original purpose of modeling textual data, such as analyzing images BIBREF1 , BIBREF2 , videos BIBREF3 , survey data BIBREF4 or social networks data BIBREF5 .", + "Since documents are frequently associated with other variables such as labels, tags or ratings, much interest has been placed on supervised topic models BIBREF6 , which allow the use of that extra information to \u201cguide\" the topics discovery. By jointly learning the topics distributions and a classification or regression model, supervised topic models have been shown to outperform the separate use of their unsupervised analogues together with an external regression/classification algorithm BIBREF2 , BIBREF7 .", + "Supervised topics models are then state-of-the-art approaches for predicting target variables associated with complex high-dimensional data, such as documents or images. Unfortunately, the size of modern datasets makes the use of a single annotator unrealistic and unpractical for the majority of the real-world applications that involve some form of human labeling. For instance, the popular Reuters-21578 benchmark corpus was categorized by a group of personnel from Reuters Ltd and Carnegie Group, Inc. Similarly, the LabelMe project asks volunteers to annotate images from a large collection using an online tool. Hence, it is seldom the case where a single oracle labels an entire collection.", + "Furthermore, the Web, through its social nature, also exploits the wisdom of crowds to annotate large collections of documents and images. By categorizing texts, tagging images or rating products and places, Web users are generating large volumes of labeled content. However, when learning supervised models from crowds, the quality of labels can vary significantly due to task subjectivity and differences in annotator reliability (or bias) BIBREF8 , BIBREF9 . If we consider a sentiment analysis task, it becomes clear that the subjectiveness of the exercise is prone to generate considerably distinct labels from different annotators. Similarly, online product reviews are known to vary considerably depending on the personal biases and volatility of the reviewer's opinions. It is therefore essential to account for these issues when learning from this increasingly common type of data. Hence, the interest of researchers on building models that take the reliabilities of different annotators into consideration and mitigate the effect of their biases has spiked during the last few years (e.g. BIBREF10 , BIBREF11 ).", + "The increasing popularity of crowdsourcing platforms like Amazon Mechanical Turk (AMT) has further contributed to the recent advances in learning from crowds. This kind of platforms offers a fast, scalable and inexpensive solution for labeling large amounts of data. However, their heterogeneous nature in terms of contributors makes their straightforward application prone to many sorts of labeling noise and bias. Hence, a careless use of crowdsourced data as training data risks generating flawed models.", + "In this article, we propose a fully generative supervised topic model that is able to account for the different reliabilities of multiple annotators and correct their biases. The proposed model is then capable of jointly modeling the words in documents as arising from a mixture of topics, the latent true target variables as a result of the empirical distribution over topics of the documents, and the labels of the multiple annotators as noisy versions of that latent ground truth. We propose two different models, one for classification BIBREF12 and another for regression problems, thus covering a very wide range of possible practical applications, as we empirically demonstrate. Since the majority of the tasks for which multiple annotators are used generally involve complex data such as text, images and video, by developing a multi-annotator supervised topic model we are contributing with a powerful tool for learning predictive models of complex high-dimensional data from crowds.", + "Given that the increasing sizes of modern datasets can pose a problem for obtaining human labels as well as for Bayesian inference, we propose an efficient stochastic variational inference algorithm BIBREF13 that is able to scale to very large datasets. We empirically show, using both simulated and real multiple-annotator labels obtained from AMT for popular text and image collections, that the proposed models are able to outperform other state-of-the-art approaches in both classification and regression tasks. We further show the computational and predictive advantages of the stochastic variational inference algorithm over its batch counterpart." + ], + [ + "Latent Dirichlet allocation (LDA) soon proved to be a powerful tool for modeling documents BIBREF0 and images BIBREF1 by extracting their underlying topics, where topics are probability distributions across words, and each document is characterized by a probability distribution across topics. However, the need to model the relationship between documents and labels quickly gave rise to many supervised variants of LDA. One of the first notable works was that of supervised LDA (sLDA) BIBREF6 . By extending LDA through the inclusion of a response variable that is linearly dependent on the mean topic-assignments of the words in a document, sLDA is able to jointly model the documents and their responses, in order to find latent topics that will best predict the response variables for future unlabeled documents. Although initially developed for general continuous response variables, sLDA was later extended to classification problems BIBREF2 , by modeling the relationship between topic-assignments and labels with a softmax function as in logistic regression.", + "From a classification perspective, there are several ways in which document classes can be included in LDA. The most natural one in this setting is probably the sLDA approach, since the classes are directly dependent on the empirical topic mixture distributions. This approach is coherent with the generative perspective of LDA but, nevertheless, several discriminative alternatives also exist. For example, DiscLDA BIBREF14 introduces a class-dependent linear transformation on the topic mixture proportions of each document, such that the per-word topic assignments are drawn from linearly transformed mixture proportions. The class-specific transformation matrices are then able to reposition the topic mixture proportions so that documents with the same class labels have similar topics mixture proportions. The transformation matrices can be estimated by maximizing the conditional likelihood of response variables as the authors propose BIBREF14 .", + "An alternative way of including classes in LDA for supervision is the one proposed in the Labeled-LDA model BIBREF15 . Labeled-LDA is a variant of LDA that incorporates supervision by constraining the topic model to assign to a document only topics that correspond to its label set. While this allows for multiple labels per document, it is restrictive in the sense that the number of topics needs to be the same as the number of possible labels.", + "From a regression perspective, other than sLDA, the most relevant approaches are the Dirichlet-multimonial regression BIBREF16 and the inverse regression topic models BIBREF17 . The Dirichlet-multimonial regression (DMR) topic model BIBREF16 includes a log-linear prior on the document's mixture proportions that is a function of a set of arbitrary features, such as author, date, publication venue or references in scientific articles. The inferred Dirichlet-multinomial distribution can then be used to make predictions about the values of theses features. The inverse regression topic model (IRTM) BIBREF17 is a mixed-membership extension of the multinomial inverse regression (MNIR) model proposed in BIBREF18 that exploits the topical structure of text corpora to improve its predictions and facilitate exploratory data analysis. However, this results in a rather complex and inefficient inference procedure. Furthermore, making predictions in the IRTM is not trivial. For example, MAP estimates of targets will be in a different scale than the original document's metadata. Hence, the authors propose the use of a linear model to regress metadata values onto their MAP predictions.", + "The approaches discussed so far rely on likelihood-based estimation procedures. The work in BIBREF7 contrasts with these approaches by proposing MedLDA, a supervised topic model that utilizes the max-margin principle for estimation. Despite its margin-based advantages, MedLDA looses the probabilistic interpretation of the document classes given the topic mixture distributions. On the contrary, in this article we propose a fully generative probabilistic model of the answers of multiple annotators and of the words of documents arising from a mixture of topics." + ], + [ + "Learning from multiple annotators is an increasingly important research topic. Since the early work of Dawid and Skeene BIBREF19 , who attempted to obtain point estimates of the error rates of patients given repeated but conflicting responses to various medical questions, many approaches have been proposed. These usually rely on latent variable models. For example, in BIBREF20 the authors propose a model to estimate the ground truth from the labels of multiple experts, which is then used to train a classifier.", + "While earlier works usually focused on estimating the ground truth and the error rates of different annotators, recent works are more focused on the problem of learning classifiers using multiple-annotator data. This idea was explored by Raykar et al. BIBREF21 , who proposed an approach for jointly learning the levels of expertise of different annotators and the parameters of a logistic regression classifier, by modeling the ground truth labels as latent variables. This work was later extended in BIBREF11 by considering the dependencies of the annotators' labels on the instances they are labeling, and also in BIBREF22 through the use of Gaussian process classifiers. The model proposed in this article for classification problems shares the same intuition with this line of work and models the true labels as latent variables. However, it differs significantly by using a fully Bayesian approach for estimating the reliabilities and biases of the different annotators. Furthermore, it considers the problems of learning a low-dimensional representation of the input data (through topic modeling) and modeling the answers of multiple annotators jointly, providing an efficient stochastic variational inference algorithm.", + "Despite the considerable amount of approaches for learning classifiers from the noisy answers of multiple annotators, for continuous response variables this problem has been approached in a much smaller extent. For example, Groot et al. BIBREF23 address this problem in the context of Gaussian processes. In their work, the authors assign a different variance to the likelihood of the data points provided by the different annotators, thereby allowing them to have different noise levels, which can be estimated by maximizing the marginal likelihood of the data. Similarly, the authors in BIBREF21 propose an extension of their own classification approach to regression problems by assigning different variances to the Gaussian noise models of the different annotators. In this article, we take this idea one step further by also considering a per-annotator bias parameter, which gives the proposed model the ability to overcome certain personal tendencies in the annotators labeling styles that are quite common, for example, in product ratings and document reviews. Furthermore, we empirically validate the proposed model using real multi-annotator data obtained from Amazon Mechanical Turk. This contrasts with the previously mentioned works, which rely only on simulated annotators." + ], + [ + "In this section, we develop a multi-annotator supervised topic model for classification problems. The model for regression settings will be presented in Section SECREF5 . We start by deriving a (batch) variational inference algorithm for approximating the posterior distribution over the latent variables and an algorithm to estimate the model parameters. We then develop a stochastic variational inference algorithm that gives the model the capability of handling large collections of documents. Finally, we show how to use the learned model to classify new documents." + ], + [ + "Let INLINEFORM0 be an annotated corpus of size INLINEFORM1 , where each document INLINEFORM2 is given a set of labels INLINEFORM3 from INLINEFORM4 distinct annotators. We can take advantage of the inherent topical structure of documents and model their words as arising from a mixture of topics, each being defined as a distribution over the words in a vocabulary, as in LDA. In LDA, the INLINEFORM5 word, INLINEFORM6 , in a document INLINEFORM7 is provided a discrete topic-assignment INLINEFORM8 , which is drawn from the documents' distribution over topics INLINEFORM9 . This allows us to build lower-dimensional representations of documents, which we can explore to build classification models by assigning coefficients INLINEFORM10 to the mean topic-assignment of the words in the document, INLINEFORM11 , and applying a softmax function in order to obtain a distribution over classes. Alternatively, one could consider more flexible models such as Gaussian processes, however that would considerably increase the complexity of inference.", + "Unfortunately, a direct mapping between document classes and the labels provided by the different annotators in a multiple-annotator setting would correspond to assuming that they are all equally reliable, an assumption that is violated in practice, as previous works clearly demonstrate (e.g. BIBREF8 , BIBREF9 ). Hence, we assume the existence of a latent ground truth class, and model the labels from the different annotators using a noise model that states that, given a true class INLINEFORM0 , each annotator INLINEFORM1 provides the label INLINEFORM2 with some probability INLINEFORM3 . Hence, by modeling the matrix INLINEFORM4 we are in fact modeling a per-annotator (normalized) confusion matrix, which allows us to account for their different levels of expertise and correct their potential biases.", + "The generative process of the proposed model for classification problems can then be summarized as follows:", + "For each annotator INLINEFORM0 ", + "For each class INLINEFORM0 ", + "Draw reliability parameter INLINEFORM0 ", + "For each topic INLINEFORM0 ", + "Draw topic distribution INLINEFORM0 ", + "For each document INLINEFORM0 ", + "Draw topic proportions INLINEFORM0 ", + "For the INLINEFORM0 word", + "Draw topic assignment INLINEFORM0 ", + "Draw word INLINEFORM0 ", + "Draw latent (true) class INLINEFORM0 ", + "For each annotator INLINEFORM0 ", + "Draw annotator's label INLINEFORM0 ", + "where INLINEFORM0 denotes the set of annotators that labeled the INLINEFORM1 document, INLINEFORM2 , and the softmax is given by DISPLAYFORM0 ", + "Fig. FIGREF20 shows a graphical model representation of the proposed model, where INLINEFORM0 denotes the number of topics, INLINEFORM1 is the number of classes, INLINEFORM2 is the total number of annotators and INLINEFORM3 is the number of words in the document INLINEFORM4 . Shaded nodes are used to distinguish latent variable from the observed ones and small solid circles are used to denote model parameters. Notice that we included a Dirichlet prior over the topics INLINEFORM5 to produce a smooth posterior and control sparsity. Similarly, instead of computing maximum likelihood or MAP estimates for the annotators reliability parameters INLINEFORM6 , we place a Dirichlet prior over these variables and perform approximate Bayesian inference. This contrasts with previous works on learning classification models from crowds BIBREF21 , BIBREF24 .", + "For developing a multi-annotator supervised topic model for regression, we shall follow a similar intuition as the one we considered for classification. Namely, we shall assume that, for a given document INLINEFORM0 , each annotator provides a noisy version, INLINEFORM1 , of the true (continuous) target variable, which we denote by INLINEFORM2 . This can be, for example, the true rating of a product or the true sentiment of a document. Assuming that each annotator INLINEFORM3 has its own personal bias INLINEFORM4 and precision INLINEFORM5 (inverse variance), and assuming a Gaussian noise model for the annotators' answers, we have that DISPLAYFORM0 ", + " This approach is therefore more powerful than previous works BIBREF21 , BIBREF23 , where a single precision parameter was used to model the annotators' expertise. Fig. FIGREF45 illustrates this intuition for 4 annotators, represented by different colors. The \u201cgreen annotator\" is the best one, since he is right on the target and his answers vary very little (low bias, high precision). The \u201cyellow annotator\" has a low bias, but his answers are very uncertain, as they can vary a lot. Contrarily, the \u201cblue annotator\" is very precise, but consistently over-estimates the true target (high bias, high precision). Finally, the \u201cred annotator\" corresponds to the worst kind of annotator (with high bias and low precision).", + "Having specified a model for annotators answers given the true targets, the only thing left is to do is to specify a model of the latent true targets INLINEFORM0 given the empirical topic mixture distributions INLINEFORM1 . For this, we shall keep things simple and assume a linear model as in sLDA BIBREF6 . The generative process of the proposed model for continuous target variables can then be summarized as follows:", + "For each annotator INLINEFORM0 ", + "For each class INLINEFORM0 ", + "Draw reliability parameter INLINEFORM0 ", + "For each topic INLINEFORM0 ", + "Draw topic distribution INLINEFORM0 ", + "For each document INLINEFORM0 ", + "Draw topic proportions INLINEFORM0 ", + "For the INLINEFORM0 word", + "Draw topic assignment INLINEFORM0 ", + "Draw word INLINEFORM0 ", + "Draw latent (true) target INLINEFORM0 ", + "For each annotator INLINEFORM0 ", + "Draw answer INLINEFORM0 ", + "Fig. FIGREF60 shows a graphical representation of the proposed model." + ], + [ + "Given a dataset INLINEFORM0 , the goal of inference is to compute the posterior distribution of the per-document topic proportions INLINEFORM1 , the per-word topic assignments INLINEFORM2 , the per-topic distribution over words INLINEFORM3 , the per-document latent true class INLINEFORM4 , and the per-annotator confusion parameters INLINEFORM5 . As with LDA, computing the exact posterior distribution of the latent variables is computationally intractable. Hence, we employ mean-field variational inference to perform approximate Bayesian inference.", + "Variational inference methods seek to minimize the KL divergence between the variational and the true posterior distribution. We assume a fully-factorized (mean-field) variational distribution of the form DISPLAYFORM0 ", + " where INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , INLINEFORM3 and INLINEFORM4 are variational parameters. Table TABREF23 shows the correspondence between variational parameters and the original parameters.", + "Let INLINEFORM0 denote the model parameters. Following BIBREF25 , the KL minimization can be equivalently formulated as maximizing the following lower bound on the log marginal likelihood DISPLAYFORM0 ", + " which we maximize using coordinate ascent.", + "Optimizing INLINEFORM0 w.r.t. INLINEFORM1 and INLINEFORM2 gives the same coordinate ascent updates as in LDA BIBREF0 DISPLAYFORM0 ", + "The variational Dirichlet parameters INLINEFORM0 can be optimized by collecting only the terms in INLINEFORM1 that contain INLINEFORM2 DISPLAYFORM0 ", + " where INLINEFORM0 denotes the documents labeled by the INLINEFORM1 annotator, INLINEFORM2 , and INLINEFORM3 and INLINEFORM4 are the gamma and digamma functions, respectively. Taking derivatives of INLINEFORM5 w.r.t. INLINEFORM6 and setting them to zero, yields the following update DISPLAYFORM0 ", + "Similarly, the coordinate ascent updates for the documents distribution over classes INLINEFORM0 can be found by considering the terms in INLINEFORM1 that contain INLINEFORM2 DISPLAYFORM0 ", + " where INLINEFORM0 . Adding the necessary Lagrange multipliers to ensure that INLINEFORM1 and setting the derivatives w.r.t. INLINEFORM2 to zero gives the following update DISPLAYFORM0 ", + " Observe how the variational distribution over the true classes results from a combination between the dot product of the inferred mean topic assignment INLINEFORM0 with the coefficients INLINEFORM1 and the labels INLINEFORM2 from the multiple annotators \u201cweighted\" by their expected log probability INLINEFORM3 .", + "The main difficulty of applying standard variational inference methods to the proposed model is the non-conjugacy between the distribution of the mean topic-assignment INLINEFORM0 and the softmax. Namely, in the expectation DISPLAYFORM0 ", + " the second term is intractable to compute. We can make progress by applying Jensen's inequality to bound it as follows DISPLAYFORM0 ", + " where INLINEFORM0 , which is constant w.r.t. INLINEFORM1 . This local variational bound can be made tight by noticing that INLINEFORM2 , where equality holds if and only if INLINEFORM3 . Hence, given the current parameter estimates INLINEFORM4 , if we set INLINEFORM5 and INLINEFORM6 then, for an individual parameter INLINEFORM7 , we have that DISPLAYFORM0 ", + " Using this local bound to approximate the expectation of the log-sum-exp term, and taking derivatives of the evidence lower bound w.r.t. INLINEFORM0 with the constraint that INLINEFORM1 , yields the following fix-point update DISPLAYFORM0 ", + " where INLINEFORM0 denotes the size of the vocabulary. Notice how the per-word variational distribution over topics INLINEFORM1 depends on the variational distribution over the true class label INLINEFORM2 .", + "The variational inference algorithm iterates between Eqs. EQREF25 - EQREF33 until the evidence lower bound, Eq. EQREF24 , converges. Additional details are provided as supplementary material.", + "", + "", + "The goal of inference is to compute the posterior distribution of the per-document topic proportions INLINEFORM0 , the per-word topic assignments INLINEFORM1 , the per-topic distribution over words INLINEFORM2 and the per-document latent true targets INLINEFORM3 . As we did for the classification model, we shall develop a variational inference algorithm using coordinate ascent. The lower-bound on the log marginal likelihood is now given by DISPLAYFORM0 ", + " where INLINEFORM0 are the model parameters. We assume a fully-factorized (mean-field) variational distribution INLINEFORM1 of the form DISPLAYFORM0 ", + " where INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , INLINEFORM3 and INLINEFORM4 are the variational parameters. Notice the new Gaussian term, INLINEFORM5 , corresponding to the approximate posterior distribution of the unobserved true targets.", + "Optimizing the variational objective INLINEFORM0 w.r.t. INLINEFORM1 and INLINEFORM2 yields the same updates from Eqs. EQREF25 and . Optimizing w.r.t. INLINEFORM3 gives a similar update to the one in sLDA BIBREF6 DISPLAYFORM0 ", + " where we defined INLINEFORM0 . Notice how this update differs only from the one in BIBREF6 by replacing the true target variable by its expected value under the variational distribution, which is given by INLINEFORM1 .", + "The only variables left for doing inference on are then the latent true targets INLINEFORM0 . The variational distribution of INLINEFORM1 is governed by two parameters: a mean INLINEFORM2 and a variance INLINEFORM3 . Collecting all the terms in INLINEFORM4 that contain INLINEFORM5 gives DISPLAYFORM0 ", + " Taking derivatives of INLINEFORM0 and setting them to zero gives the following update for INLINEFORM1 DISPLAYFORM0 ", + " Notice how the value of INLINEFORM0 is a weighted average of what the linear regression model on the empirical topic mixture believes the true target should be, and the bias-corrected answers of the different annotators weighted by their individual precisions.", + "As for INLINEFORM0 , we can optimize INLINEFORM1 w.r.t. INLINEFORM2 by collecting all terms that contain INLINEFORM3 DISPLAYFORM0 ", + " and taking derivatives, yielding the update DISPLAYFORM0 " + ], + [ + "The model parameters are INLINEFORM0 . The parameters INLINEFORM1 of the Dirichlet priors can be regarded as hyper-parameters of the proposed model. As with many works on topic models (e.g. BIBREF26 , BIBREF2 ), we assume hyper-parameters to be fixed, since they can be effectively selected by grid-search procedures which are able to explore well the parameter space without suffering from local optima. Our focus is then on estimating the coefficients INLINEFORM2 using a variational EM algorithm. Therefore, in the E-step we use the variational inference algorithm from section SECREF21 to estimate the posterior distribution of the latent variables, and in the M-step we find maximum likelihood estimates of INLINEFORM3 by maximizing the evidence lower bound INLINEFORM4 . Unfortunately, taking derivatives of INLINEFORM5 w.r.t. INLINEFORM6 does not yield a closed-form solution. Hence, we use a numerical method, namely L-BFGS BIBREF27 , to find an optimum. The objective function and gradients are given by DISPLAYFORM0 ", + " where, for convenience, we defined the following variable: INLINEFORM0 .", + "The parameters of the proposed regression model are INLINEFORM0 . As we did for the classification model, we shall assume the Dirichlet parameters, INLINEFORM1 and INLINEFORM2 , to be fixed. Similarly, we shall assume that the variance of the true targets, INLINEFORM3 , to be constant. The only parameters left to estimate are then the regression coefficients INLINEFORM4 and the annotators biases, INLINEFORM5 , and precisions, INLINEFORM6 , which we estimate using variational Bayesian EM.", + "Since the latent true targets are now linear functions of the documents' empirical topic mixtures (i.e. there is no softmax function), we can find a closed form solution for the regression coefficients INLINEFORM0 . Taking derivatives of INLINEFORM1 w.r.t. INLINEFORM2 and setting them to zero, gives the following solution for INLINEFORM3 DISPLAYFORM0 ", + " where DISPLAYFORM0 ", + "We can find maximum likelihood estimates for the annotator biases INLINEFORM0 by optimizing the lower bound on the marginal likelihood. The terms in INLINEFORM1 that involve INLINEFORM2 are DISPLAYFORM0 ", + " Taking derivatives w.r.t. INLINEFORM0 gives the following estimate for the bias of the INLINEFORM1 annotator DISPLAYFORM0 ", + "Similarly, we can find maximum likelihood estimates for the precisions INLINEFORM0 of the different annotators by considering the terms in INLINEFORM1 that contain INLINEFORM2 DISPLAYFORM0 ", + " The maximum likelihood estimate for the precision (inverse variance) of the INLINEFORM0 annotator is then given by DISPLAYFORM0 ", + "Given a set of fitted parameters, it is then straightforward to make predictions for new documents: it is just necessary to infer the (approximate) posterior distribution over the word-topic assignments INLINEFORM0 for all the words using the coordinates ascent updates of standard LDA (Eqs. EQREF25 and EQREF42 ), and then use the mean topic assignments INLINEFORM1 to make predictions INLINEFORM2 ." + ], + [ + "In Section SECREF21 , we proposed a batch coordinate ascent algorithm for doing variational inference in the proposed model. This algorithm iterates between analyzing every document in the corpus to infer the local hidden structure, and estimating the global hidden variables. However, this can be inefficient for large datasets, since it requires a full pass through the data at each iteration before updating the global variables. In this section, we develop a stochastic variational inference algorithm BIBREF13 , which follows noisy estimates of the gradients of the evidence lower bound INLINEFORM0 .", + "Based on the theory of stochastic optimization BIBREF28 , we can find unbiased estimates of the gradients by subsampling a document (or a mini-batch of documents) from the corpus, and using it to compute the gradients as if that document was observed INLINEFORM0 times. Hence, given an uniformly sampled document INLINEFORM1 , we use the current posterior distributions of the global latent variables, INLINEFORM2 and INLINEFORM3 , and the current coefficient estimates INLINEFORM4 , to compute the posterior distribution over the local hidden variables INLINEFORM5 , INLINEFORM6 and INLINEFORM7 using Eqs. EQREF25 , EQREF33 and EQREF29 respectively. These posteriors are then used to update the global variational parameters, INLINEFORM8 and INLINEFORM9 by taking a step of size INLINEFORM10 in the direction of the noisy estimates of the natural gradients.", + "Algorithm SECREF37 describes a stochastic variational inference algorithm for the proposed model. Given an appropriate schedule for the learning rates INLINEFORM0 , such that INLINEFORM1 and INLINEFORM2 , the stochastic optimization algorithm is guaranteed to converge to a local maximum of the evidence lower bound BIBREF28 .", + "[t] Stochastic variational inference for the proposed classification model [1] Initialize INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , INLINEFORM3 , INLINEFORM4 , INLINEFORM5 Set t = t + 1 Sample a document INLINEFORM6 uniformly from the corpus Compute INLINEFORM7 using Eq. EQREF33 , for INLINEFORM8 Compute INLINEFORM9 using Eq. EQREF25 Compute INLINEFORM10 using Eq. EQREF29 local parameters INLINEFORM11 , INLINEFORM12 and INLINEFORM13 converge Compute step-size INLINEFORM14 Update topics variational parameters DISPLAYFORM0 ", + " Update annotators confusion parameters DISPLAYFORM0 ", + " global convergence criterion is met", + "As we did for the classification model from Section SECREF4 , we can envision developing a stochastic variational inference for the proposed regression model. In this case, the only \u201cglobal\" latent variables are the per-topic distributions over words INLINEFORM0 . As for the \u201clocal\" latent variables, instead of a single variable INLINEFORM1 , we now have two variables per-document: INLINEFORM2 and INLINEFORM3 . The stochastic variational inference can then be summarized as shown in Algorithm SECREF76 . For added efficiency, one can also perform stochastic updates of the annotators biases INLINEFORM4 and precisions INLINEFORM5 , by taking a step in the direction of the gradient of the noisy evidence lower bound scaled by the step-size INLINEFORM6 .", + "[t] Stochastic variational inference for the proposed regression model [1] Initialize INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , INLINEFORM3 , INLINEFORM4 , INLINEFORM5 , INLINEFORM6 Set t = t + 1 Sample a document INLINEFORM7 uniformly from the corpus Compute INLINEFORM8 using Eq. EQREF64 , for INLINEFORM9 Compute INLINEFORM10 using Eq. EQREF25 Compute INLINEFORM11 using Eq. EQREF66 Compute INLINEFORM12 using Eq. EQREF68 local parameters INLINEFORM13 , INLINEFORM14 and INLINEFORM15 converge Compute step-size INLINEFORM16 Update topics variational parameters DISPLAYFORM0 ", + " global convergence criterion is met" + ], + [ + "In order to make predictions for a new (unlabeled) document INLINEFORM0 , we start by computing the approximate posterior distribution over the latent variables INLINEFORM1 and INLINEFORM2 . This can be achieved by dropping the terms that involve INLINEFORM3 , INLINEFORM4 and INLINEFORM5 from the model's joint distribution (since, at prediction time, the multi-annotator labels are no longer observed) and averaging over the estimated topics distributions. Letting the topics distribution over words inferred during training be INLINEFORM6 , the joint distribution for a single document is now simply given by DISPLAYFORM0 ", + " Deriving a mean-field variational inference algorithm for computing the posterior over INLINEFORM0 results in the same fixed-point updates as in LDA BIBREF0 for INLINEFORM1 (Eq. EQREF25 ) and INLINEFORM2 DISPLAYFORM0 ", + " Using the inferred posteriors and the coefficients INLINEFORM0 estimated during training, we can make predictions as follows DISPLAYFORM0 ", + " This is equivalent to making predictions in the classification version of sLDA BIBREF2 ." + ], + [ + "In this section, we develop a variant of the model proposed in Section SECREF4 for regression problems. We shall start by describing the proposed model with a special focus on the how to handle multiple annotators with different biases and reliabilities when the target variables are continuous variables. Next, we present a variational inference algorithm, highlighting the differences to the classification version. Finally, we show how to optimize the model parameters." + ], + [ + "In this section, the proposed multi-annotator supervised LDA models for classification and regression (MA-sLDAc and MA-sLDAr, respectively) are validated using both simulated annotators on popular corpora and using real multiple-annotator labels obtained from Amazon Mechanical Turk. Namely, we shall consider the following real-world problems: classifying posts and news stories; classifying images according to their content; predicting number of stars that a given user gave to a restaurant based on the review; predicting movie ratings using the text of the reviews." + ], + [ + "In order to first validate the proposed model for classification problems in a slightly more controlled environment, the well-known 20-Newsgroups benchmark corpus BIBREF29 was used by simulating multiple annotators with different levels of expertise. The 20-Newsgroups consists of twenty thousand messages taken from twenty newsgroups, and is divided in six super-classes, which are, in turn, partitioned in several sub-classes. For this first set of experiments, only the four most populated super-classes were used: \u201ccomputers\", \u201cscience\", \u201cpolitics\" and \u201crecreative\". The preprocessing of the documents consisted of stemming and stop-words removal. After that, 75% of the documents were randomly selected for training and the remaining 25% for testing.", + "The different annotators were simulated by sampling their answers from a multinomial distribution, where the parameters are given by the lines of the annotators' confusion matrices. Hence, for each annotator INLINEFORM0 , we start by pre-defining a confusion matrix INLINEFORM1 with elements INLINEFORM2 , which correspond to the probability that the annotators' answer is INLINEFORM3 given that the true label is INLINEFORM4 , INLINEFORM5 . Then, the answers are sampled i.i.d. from INLINEFORM6 . This procedure was used to simulate 5 different annotators with the following accuracies: 0.737, 0.468, 0.284, 0.278, 0.260. In this experiment, no repeated labelling was used. Hence, each annotator only labels roughly one-fifth of the data. When compared to the ground truth, the simulated answers revealed an accuracy of 0.405. See Table TABREF81 for an overview of the details of the classification datasets used.", + "Both the batch and the stochastic variational inference (svi) versions of the proposed model (MA-sLDAc) are compared with the following baselines:", + "[itemsep=0.02cm]", + "LDA + LogReg (mv): This baseline corresponds to applying unsupervised LDA to the data, and learning a logistic regression classifier on the inferred topics distributions of the documents. The labels from the different annotators were aggregated using majority voting (mv). Notice that, when there is a single annotator label per instance, majority voting is equivalent to using that label for training. This is the case of the 20-Newsgroups' simulated annotators, but the same does not apply for the experiments in Section UID89 .", + "LDA + Raykar: For this baseline, the model of BIBREF21 was applied using the documents' topic distributions inferred by LDA as features.", + "LDA + Rodrigues: This baseline is similar to the previous one, but uses the model of BIBREF9 instead.", + "Blei 2003 (mv): The idea of this baseline is to replicate a popular state-of-the-art approach for document classification. Hence, the approach of BIBREF0 was used. It consists of applying LDA to extract the documents' topics distributions, which are then used to train a SVM. Similarly to the previous approach, the labels from the different annotators were aggregated using majority voting (mv).", + "sLDA (mv): This corresponds to using the classification version of sLDA BIBREF2 with the labels obtained by performing majority voting (mv) on the annotators' answers.", + "For all the experiments the hyper-parameters INLINEFORM0 , INLINEFORM1 and INLINEFORM2 were set using a simple grid search in the collection INLINEFORM3 . The same approach was used to optimize the hyper-parameters of the all the baselines. For the svi algorithm, different mini-batch sizes and forgetting rates INLINEFORM4 were tested. For the 20-Newsgroup dataset, the best results were obtained with a mini-batch size of 500 and INLINEFORM5 . The INLINEFORM6 was kept at 1. The results are shown in Fig. FIGREF87 for different numbers of topics, where we can see that the proposed model outperforms all the baselines, being the svi version the one that performs best.", + "In order to assess the computational advantages of the stochastic variational inference (svi) over the batch algorithm, the log marginal likelihood (or log evidence) was plotted against the number of iterations. Fig. FIGREF88 shows this comparison. Not surprisingly, the svi version converges much faster to higher values of the log marginal likelihood when compared to the batch version, which reflects the efficiency of the svi algorithm.", + "In order to validate the proposed classification model in real crowdsourcing settings, Amazon Mechanical Turk (AMT) was used to obtain labels from multiple annotators for two popular datasets: Reuters-21578 BIBREF30 and LabelMe BIBREF31 .", + "The Reuters-21578 is a collection of manually categorized newswire stories with labels such as Acquisitions, Crude-oil, Earnings or Grain. For this experiment, only the documents belonging to the ModApte split were considered with the additional constraint that the documents should have no more than one label. This resulted in a total of 7016 documents distributed among 8 classes. Of these, 1800 documents were submitted to AMT for multiple annotators to label, giving an average of approximately 3 answers per document (see Table TABREF81 for further details). The remaining 5216 documents were used for testing. The collected answers yield an average worker accuracy of 56.8%. Applying majority voting to these answers reveals a ground truth accuracy of 71.0%. Fig. FIGREF90 shows the boxplots of the number of answers per worker and their accuracies. Observe how applying majority voting yields a higher accuracy than the median accuracy of the workers.", + "The results obtained by the different approaches are given in Fig. FIGREF91 , where it can be seen that the proposed model (MA-sLDAc) outperforms all the other approaches. For this dataset, the svi algorithm is using mini-batches of 300 documents.", + "The proposed model was also validated using a dataset from the computer vision domain: LabelMe BIBREF31 . In contrast to the Reuters and Newsgroups corpora, LabelMe is an open online tool to annotate images. Hence, this experiment allows us to see how the proposed model generalizes beyond non-textual data. Using the Matlab interface provided in the projects' website, we extracted a subset of the LabelMe data, consisting of all the 256 x 256 images with the categories: \u201chighway\", \u201cinside city\", \u201ctall building\", \u201cstreet\", \u201cforest\", \u201ccoast\", \u201cmountain\" or \u201copen country\". This allowed us to collect a total of 2688 labeled images. Of these, 1000 images were given to AMT workers to classify with one of the classes above. Each image was labeled by an average of 2.547 workers, with a mean accuracy of 69.2%. When majority voting is applied to the collected answers, a ground truth accuracy of 76.9% is obtained. Fig. FIGREF92 shows the boxplots of the number of answers per worker and their accuracies. Interestingly, the worker accuracies are much higher and their distribution is much more concentrated than on the Reuters-21578 data (see Fig. FIGREF90 ), which suggests that this is an easier task for the AMT workers.", + "The preprocessing of the images used is similar to the approach in BIBREF1 . It uses 128-dimensional SIFT BIBREF32 region descriptors selected by a sliding grid spaced at one pixel. This sliding grid extracts local regions of the image with sizes uniformly sampled between 16 x 16 and 32 x 32 pixels. The 128-dimensional SIFT descriptors produced by the sliding window are then fed to a k-means algorithm (with k=200) in order construct a vocabulary of 200 \u201cvisual words\". This allows us to represent the images with a bag of visual words model.", + "With the purpose of comparing the proposed model with a popular state-of-the-art approach for image classification, for the LabelMe dataset, the following baseline was introduced:", + "Bosch 2006 (mv): This baseline is similar to one in BIBREF33 . The authors propose the use of pLSA to extract the latent topics, and the use of k-nearest neighbor (kNN) classifier using the documents' topics distributions. For this baseline, unsupervised LDA is used instead of pLSA, and the labels from the different annotators for kNN (with INLINEFORM0 ) are aggregated using majority voting (mv).", + "The results obtained by the different approaches for the LabelMe data are shown in Fig. FIGREF94 , where the svi version is using mini-batches of 200 documents.", + "Analyzing the results for the Reuters-21578 and LabelMe data, we can observe that MA-sLDAc outperforms all the baselines, with slightly better accuracies for the batch version, especially in the Reuters data. Interestingly, the second best results are consistently obtained by the multi-annotator approaches, which highlights the need for accounting for the noise and biases of the answers of the different annotators.", + "In order to verify that the proposed model was estimating the (normalized) confusion matrices INLINEFORM0 of the different workers correctly, a random sample of them was plotted against the true confusion matrices (i.e. the normalized confusion matrices evaluated against the true labels). Figure FIGREF95 shows the results obtained with 60 topics on the Reuters-21578 dataset, where the color intensity of the cells increases with the magnitude of the value of INLINEFORM1 (the supplementary material provides a similar figure for the LabelMe dataset). Using this visualization we can verify that the AMT workers are quite heterogeneous in their labeling styles and in the kind of mistakes they make, with several workers showing clear biases (e.g. workers 3 and 4), while others made mistakes more randomly (e.g. worker 1). Nevertheless, the proposed is able to capture these patterns correctly and account for effect.", + "To gain further insights, Table TABREF96 shows 4 example images from the LabelMe dataset, along with their true labels, the answers provided by the different workers, the true label inferred by the proposed model and the likelihood of the different possible answers given the true label for each annotator ( INLINEFORM0 for INLINEFORM1 ) using a color-coding scheme similar to Fig. FIGREF95 . In the first example, although majority voting suggests \u201cinside city\" to be the correct label, we can see that the model has learned that annotators 32 and 43 are very likely to provide the label \u201cinside city\" when the true label is actually \u201cstreet\", and it is able to leverage that fact to infer that the correct label is \u201cstreet\". Similarly, in the second image the model is able to infer the correct true label from 3 conflicting labels. However, in the third image the model is not able to recover the correct true class, which can be explained by it not having enough evidence about the annotators and their reliabilities and biases (likelihood distribution for these cases is uniform). In fact, this raises interesting questions regarding requirements for the minimum number of labels per annotator, their reliabilities and their coherence. Finally, for the fourth image, somehow surprisingly, the model is able to infer the correct true class, even though all 3 annotators labeled it as \u201cinside city\"." + ], + [ + "As for proposed classification model, we start by validating MA-sLDAr using simulated annotators on a popular corpus where the documents have associated targets that we wish to predict. For this purpose, we shall consider a dataset of user-submitted restaurant reviews from the website we8there.com. This dataset was originally introduced in BIBREF34 and it consists of 6260 reviews. For each review, there is a five-star rating on four specific aspects of quality (food, service, value, and atmosphere) as well as the overall experience. Our goal is then to predict the overall experience of the user based on his comments in the review. We apply the same preprocessing as in BIBREF18 , which consists in tokenizing the text into bigrams and discarding those that appear in less than ten reviews. The preprocessing of the documents consisted of stemming and stop-words removal. After that, 75% of the documents were randomly selected for training and the remaining 25% for testing.", + "As with the classification model, we seek to simulate an heterogeneous set of annotators in terms of reliability and bias. Hence, in order to simulate an annotator INLINEFORM0 , we proceed as follows: let INLINEFORM1 be the true review of the restaurant; we start by assigning a given bias INLINEFORM2 and precision INLINEFORM3 to the reviewers, depending on what type of annotator we wish to simulate (see Fig. FIGREF45 ); we then sample a simulated answer as INLINEFORM4 . Using this procedure, we simulated 5 annotators with the following (bias, precision) pairs: (0.1, 10), (-0.3, 3), (-2.5, 10), (0.1, 0.5) and (1, 0.25). The goal is to have 2 good annotators (low bias, high precision), 1 highly biased annotator and 2 low precision annotators where one is unbiased and the other is reasonably biased. The coefficients of determination ( INLINEFORM5 ) of the simulated annotators are: [0.940, 0.785, -2.469, -0.131, -1.749]. Computing the mean of the answers of the different annotators yields a INLINEFORM6 of 0.798. Table TABREF99 gives an overview on the statistics of datasets used in the regression experiments.", + "We compare the proposed model (MA-sLDAr) with the two following baselines:", + "[itemsep=0.02cm]", + "LDA + LinReg (mean): This baseline corresponds to applying unsupervised LDA to the data, and learning a linear regression model on the inferred topics distributions of the documents. The answers from the different annotators were aggregated by computing the mean.", + "sLDA (mean): This corresponds to using the regression version of sLDA BIBREF6 with the target variables obtained by computing the mean of the annotators' answers.", + "Fig. FIGREF102 shows the results obtained for different numbers of topics. Do to the stochastic nature of both the annotators simulation procedure and the initialization of the variational Bayesian EM algorithm, we repeated each experiment 30 times and report the average INLINEFORM0 obtained with the corresponding standard deviation. Since the regression datasets that are considered in this article are not large enough to justify the use of a stochastic variational inference (svi) algorithm, we only made experiments using the batch algorithm developed in Section SECREF61 . The results obtained clearly show the improved performance of MA-sLDAr over the other methods.", + "The proposed multi-annotator regression model (MA-sLDAr) was also validated with real annotators by using AMT. For that purpose, the movie review dataset from BIBREF35 was used. This dataset consists of 5006 movie reviews along with their respective star rating (from 1 to 10). The goal of this experiment is then predict how much a person liked a movie based on what she says about it. We ask workers to guess how much they think the writer of the review liked the movie based on her comments. An average of 4.96 answers per-review was collected for a total of 1500 reviews. The remaining reviews were used for testing. In average, each worker rated approximately 55 reviews. Using the mean answer as an estimate of the true rating of the movie yields a INLINEFORM0 of 0.830. Table TABREF99 gives an overview of the statistics of this data. Fig. FIGREF104 shows boxplots of the number of answers per worker, as well as boxplots of their respective biases ( INLINEFORM1 ) and variances (inverse precisions, INLINEFORM2 ).", + "The preprocessing of the text consisted of stemming and stop-words removal. Using the preprocessed data, the proposed MA-sLDAr model was compared with the same baselines that were used with the we8there dataset in Section UID98 . Fig. FIGREF105 shows the results obtained for different numbers of topics. These results show that the proposed model outperforms all the other baselines.", + "With the purpose of verifying that the proposed model is indeed estimating the biases and precisions of the different workers correctly, we plotted the true values against the estimates of MA-sLDAr with 60 topics for a random subset of 10 workers. Fig. FIGREF106 shows the obtained results, where higher color intensities indicate higher values. Ideally, the colour of two horizontally-adjacent squares would then be of similar shades, and this is indeed what happens in practice for the majority of the workers, as Fig. FIGREF106 shows. Interestingly, the figure also shows that there are a couple of workers that are considerably biased (e.g. workers 6 and 8) and that those biases are being correctly estimated, thus justifying the inclusion of a bias parameter in the proposed model, which contrasts with previous works BIBREF21 , BIBREF23 ." + ], + [ + "This article proposed a supervised topic model that is able to learn from multiple annotators and crowds, by accounting for their biases and different levels of expertise. Given the large sizes of modern datasets, and considering that the majority of the tasks for which crowdsourcing and multiple annotators are desirable candidates, generally involve complex high-dimensional data such as text and images, the proposed model constitutes a strong contribution for the multi-annotator paradigm. This model is then capable of jointly modeling the words in documents as arising from a mixture of topics, as well as the latent true target variables and the (noisy) answers of the multiple annotators. We developed two distinct models, one for classification and another for regression, which share similar intuitions but that inevitably differ due to the nature of the target variables. We empirically showed, using both simulated and real annotators from Amazon Mechanical Turk that the proposed model is able to outperform state-of-the-art approaches in several real-world problems, such as classifying posts, news stories and images, or predicting the number of stars of restaurant and the rating of movie based on their reviews. For this, we use various popular datasets from the state-of-the-art, that are commonly used for benchmarking machine learning algorithms. Finally, an efficient stochastic variational inference algorithm was described, which gives the proposed models the ability to scale to large datasets." + ], + [ + "The Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia (FCT) is gratefully acknowledged for founding this work with the grants SFRH/BD/78396/2011 and PTDC/ECM-TRA/1898/2012 (InfoCROWDS).", + "[]Mariana Louren\u00e7o has a MSc degree in Informatics Engineering from University of Coimbra, Portugal. Her thesis presented a supervised topic model that is able to learn from crowds and she took part in a research project whose primary objective was to exploit online information about public events to build predictive models of flows of people in the city. Her main research interests are machine learning, pattern recognition and natural language processing.", + "[]Bernardete Ribeiro is Associate Professor at the Informatics Engineering Department, University of Coimbra in Portugal, from where she received a D.Sc. in Informatics Engineering, a Ph.D. in Electrical Engineering, speciality of Informatics, and a MSc in Computer Science. Her research interests are in the areas of Machine Learning, Pattern Recognition and Signal Processing and their applications to a broad range of fields. She was responsible/participated in several research projects in a wide range of application areas such as Text Classification, Financial, Biomedical and Bioinformatics. Bernardete Ribeiro is IEEE Senior Member, and member of IARP International Association of Pattern Recognition and ACM.", + "[]Francisco C. Pereira is Full Professor at the Technical University of Denmark (DTU), where he leads the Smart Mobility research group. His main research focus is on applying machine learning and pattern recognition to the context of transportation systems with the purpose of understanding and predicting mobility behavior, and modeling and optimizing the transportation system as a whole. He has Master\u20ac\u2122s (2000) and Ph.D. (2005) degrees in Computer Science from University of Coimbra, and has authored/co-authored over 70 journal and conference papers in areas such as pattern recognition, transportation, knowledge based systems and cognitive science. Francisco was previously Research Scientist at MIT and Assistant Professor in University of Coimbra. He was awarded several prestigious prizes, including an IEEE Achievements award, in 2009, the Singapore GYSS Challenge in 2013, and the Pyke Johnson award from Transportation Research Board, in 2015." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0157/instruction.md b/qasper-0157/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..edc36f39b27217e1d4ace34d21493f3d06b56850 --- /dev/null +++ b/qasper-0157/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model Performance + +Question: How much is representaton improved for rare/medum frequency words compared to standalone BERT and previous work? \ No newline at end of file diff --git a/qasper-0159/instruction.md b/qasper-0159/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..7c9d4ca5580968e6cda63f83c7ea45a556ed3d75 --- /dev/null +++ b/qasper-0159/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model Performance + +Question: What is dataset for word probing task? \ No newline at end of file diff --git a/qasper-0161/instruction.md b/qasper-0161/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..55d3a5d75bb1492bc70026713a190c4165f3e94c --- /dev/null +++ b/qasper-0161/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Joint Entity Linking with Deep Reinforcement Learning + +Question: How big is the performance difference between this method and the baseline? \ No newline at end of file diff --git a/qasper-0166/instruction.md b/qasper-0166/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..5d98e5ef301e23eb903f013f2be6ebc48e9f64ce --- /dev/null +++ b/qasper-0166/instruction.md @@ -0,0 +1,86 @@ +Name of Paper: Classification Betters Regression in Query-based Multi-document Summarisation Techniques for Question Answering: Macquarie University at BioASQ7b + +Question: What classification approaches were experimented for this task? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work", + "Classification vs. Regression Experiments", + "Deep Learning Models", + "Reinforcement Learning", + "Evaluation Correlation Analysis", + "Submitted Runs", + "Conclusions" + ], + "paragraphs": [ + [ + "The BioASQ Challenge includes a question answering task (Phase B, part B) where the aim is to find the \u201cideal answer\u201d \u2014 that is, an answer that would normally be given by a person BIBREF0. This is in contrast with most other question answering challenges where the aim is normally to give an exact answer, usually a fact-based answer or a list. Given that the answer is based on an input that consists of a biomedical question and several relevant PubMed abstracts, the task can be seen as an instance of query-based multi-document summarisation.", + "As in past participation BIBREF1, BIBREF2, we wanted to test the use of deep learning and reinforcement learning approaches for extractive summarisation. In contrast with past years where the training procedure was based on a regression set up, this year we experiment with various classification set ups. The main contributions of this paper are:", + "We compare classification and regression approaches and show that classification produces better results than regression but the quality of the results depends on the approach followed to annotate the data labels.", + "We conduct correlation analysis between various ROUGE evaluation metrics and the human evaluations conducted at BioASQ and show that Precision and F1 correlate better than Recall.", + "Section SECREF2 briefly introduces some related work for context. Section SECREF3 describes our classification and regression experiments. Section SECREF4 details our experiments using deep learning architectures. Section SECREF5 explains the reinforcement learning approaches. Section SECREF6 shows the results of our correlation analysis between ROUGE scores and human annotations. Section SECREF7 lists the specific runs submitted at BioASQ 7b. Finally, Section SECREF8 concludes the paper." + ], + [ + "The BioASQ challenge has organised annual challenges on biomedical semantic indexing and question answering since 2013 BIBREF0. Every year there has been a task about semantic indexing (task a) and another about question answering (task b), and occasionally there have been additional tasks. The tasks defined for 2019 are:", + "Large Scale Online Biomedical Semantic Indexing.", + "Biomedical Semantic QA involving Information Retrieval (IR), Question Answering (QA), and Summarisation.", + "Medical Semantic Indexing in Spanish.", + "BioASQ Task 7b consists of two phases. Phase A provides a biomedical question as an input, and participants are expected to find relevant concepts from designated terminologies and ontologies, relevant articles from PubMed, relevant snippets from the relevant articles, and relevant RDF triples from designated ontologies. Phase B provides a biomedical question and a list of relevant articles and snippets, and participant systems are expected to return the exact answers and the ideal answers. The training data is composed of the test data from all previous years, and amounts to 2,747 samples. There has been considerable research on the use of machine learning approaches for tasks related to text summarisation, especially on single-document summarisation. Abstractive approaches normally use an encoder-decoder architecture and variants of this architecture incorporate attention BIBREF3 and pointer-generator BIBREF4. Recent approaches leveraged the use of pre-trained models BIBREF5. Recent extractive approaches to summarisation incorporate recurrent neural networks that model sequences of sentence extractions BIBREF6 and may incorporate an abstractive component and reinforcement learning during the training stage BIBREF7. But relatively few approaches have been proposed for query-based multi-document summarisation. Table TABREF8 summarises the approaches presented in the proceedings of the 2018 BioASQ challenge." + ], + [ + "Our past participation in BioASQ BIBREF1, BIBREF2 and this paper focus on extractive approaches to summarisation. Our decision to focus on extractive approaches is based on the observation that a relatively large number of sentences from the input snippets has very high ROUGE scores, thus suggesting that human annotators had a general tendency to copy text from the input to generate the target summaries BIBREF1. Our past participating systems used regression approaches using the following framework:", + "Train the regressor to predict the ROUGE-SU4 F1 score of the input sentence.", + "Produce a summary by selecting the top $n$ input sentences.", + "A novelty in the current participation is the introduction of classification approaches using the following framework.", + "Train the classifier to predict the target label (\u201csummary\u201d or \u201cnot summary\u201d) of the input sentence.", + "Produce a summary by selecting all sentences predicted as \u201csummary\u201d.", + "If the total number of sentences selected is less than $n$, select $n$ sentences with higher probability of label \u201csummary\u201d.", + "Introducing a classifier makes labelling the training data not trivial, since the target summaries are human-generated and they do not have a perfect mapping to the input sentences. In addition, some samples have multiple reference summaries. BIBREF11 showed that different data labelling approaches influence the quality of the final summary, and some labelling approaches may lead to better results than using regression. In this paper we experiment with the following labelling approaches:", + ": Label as \u201csummary\u201d all sentences from the input text that have a ROUGE score above a threshold $t$.", + ": Label as \u201csummary\u201d the $m$ input text sentences with highest ROUGE score.", + "As in BIBREF11, The ROUGE score of an input sentence was the ROUGE-SU4 F1 score of the sentence against the set of reference summaries.", + "We conducted cross-validation experiments using various values of $t$ and $m$. Table TABREF26 shows the results for the best values of $t$ and $m$ obtained. The regressor and classifier used Support Vector Regression (SVR) and Support Vector Classification (SVC) respectively. To enable a fair comparison we used the same input features in all systems. These input features combine information from the question and the input sentence and are shown in Fig. FIGREF16. The features are based on BIBREF12, and are the same as in BIBREF1, plus the addition of the position of the input snippet. The best SVC and SVR parameters were determined by grid search.", + "Preliminary experiments showed a relatively high number of cases where the classifier did not classify any of the input sentences as \u201csummary\u201d. To solve this problem, and as mentioned above, the summariser used in Table TABREF26 introduces a backoff step that extracts the $n$ sentences with highest predicted values when the summary has less than $n$ sentences. The value of $n$ is as reported in our prior work and shown in Table TABREF25.", + "The results confirm BIBREF11's finding that classification outperforms regression. However, the actual choice of optimal labelling scheme was different: whereas in BIBREF11 the optimal labelling was based on a labelling threshold of 0.1, our experiments show a better result when using the top 5 sentences as the target summary. The reason for this difference might be the fact that BIBREF11 used all sentences from the abstracts of the relevant PubMed articles, whereas we use only the snippets as the input to our summariser. Consequently, the number of input sentences is now much smaller. We therefore report the results of using the labelling schema of top 5 snippets in all subsequent classifier-based experiments of this paper.", + "barchart=[fill=black!20,draw=black] errorbar=[very thin,draw=black!75] sscale=[very thin,draw=black!75]" + ], + [ + "Based on the findings of Section SECREF3, we apply minimal changes to the deep learning regression models of BIBREF2 to convert them to classification models. In particular, we add a sigmoid activation to the final layer, and use cross-entropy as the loss function. The complete architecture is shown in Fig. FIGREF28.", + "The bottom section of Table TABREF26 shows the results of several variants of the neural architecture. The table includes a neural regressor (NNR) and a neural classifier (NNC). The neural classifier is trained in two set ups: \u201cNNC top 5\u201d uses classification labels as described in Section SECREF3, and \u201cNNC SU4 F1\u201d uses the regression labels, that is, the ROUGE-SU4 F1 scores of each sentence. Of interest is the fact that \u201cNNC SU4 F1\u201d outperforms the neural regressor. We have not explored this further and we presume that the relatively good results are due to the fact that ROUGE values range between 0 and 1, which matches the full range of probability values that can be returned by the sigmoid activation of the classifier final layer.", + "Table TABREF26 also shows the standard deviation across the cross-validation folds. Whereas this standard deviation is fairly large compared with the differences in results, in general the results are compatible with the top part of the table and prior work suggesting that classification-based approaches improve over regression-based approaches." + ], + [ + "We also experiment with the use of reinforcement learning techniques. Again these experiments are based on BIBREF2, who uses REINFORCE to train a global policy. The policy predictor uses a simple feedforward network with a hidden layer.", + "The results reported by BIBREF2 used ROUGE Recall and indicated no improvement with respect to deep learning architectures. Human evaluation results are preferable over ROUGE but these were made available after the publication of the paper. When comparing the ROUGE and human evaluation results (Table TABREF29), we observe an inversion of the results. In particular, the reinforcement learning approaches (RL) of BIBREF2 receive good human evaluation results, and as a matter of fact they are the best of our runs in two of the batches. In contrast, the regression systems (NNR) fare relatively poorly. Section SECREF6 expands on the comparison between the ROUGE and human evaluation scores.", + "Encouraged by the results of Table TABREF29, we decided to continue with our experiments with reinforcement learning. We use the same features as in BIBREF2, namely the length (in number of sentences) of the summary generated so far, plus the $tf.idf$ vectors of the following:", + "Candidate sentence;", + "Entire input to summarise;", + "Summary generated so far;", + "Candidate sentences that are yet to be processed; and", + "Question.", + "The reward used by REINFORCE is the ROUGE value of the summary generated by the system. Since BIBREF2 observed a difference between the ROUGE values of the Python implementation of ROUGE and the original Perl version (partly because the Python implementation does not include ROUGE-SU4), we compare the performance of our system when trained with each of them. Table TABREF35 summarises some of our experiments. We ran the version trained on Python ROUGE once, and the version trained on Perl twice. The two Perl runs have different results, and one of them clearly outperforms the Python run. However, given the differences of results between the two Perl runs we advice to re-run the experiments multiple times and obtain the mean and standard deviation of the runs before concluding whether there is any statistical difference between the results. But it seems that there may be an improvement of the final evaluation results when training on the Perl ROUGE values, presumably because the final evaluation results are measured using the Perl implementation of ROUGE.", + "We have also tested the use of word embeddings instead of $tf.idf$ as input features to the policy model, while keeping the same neural architecture for the policy (one hidden layer using the same number of hidden nodes). In particular, we use the mean of word embeddings using 100 and 200 dimensions. These word embeddings were pre-trained using word2vec on PubMed documents provided by the organisers of BioASQ, as we did for the architectures described in previous sections. The results, not shown in the paper, indicated no major improvement, and re-runs of the experiments showed different results on different runs. Consequently, our submission to BioASQ included the original system using $tf.idf$ as input features in all batches but batch 2, as described in Section SECREF7." + ], + [ + "As mentioned in Section SECREF5, there appears to be a large discrepancy between ROUGE Recall and the human evaluations. This section describes a correlation analysis between human and ROUGE evaluations using the runs of all participants to all previous BioASQ challenges that included human evaluations (Phase B, ideal answers). The human evaluation results were scraped from the BioASQ Results page, and the ROUGE results were kindly provided by the organisers. We compute the correlation of each of the ROUGE metrics (recall, precision, F1 for ROUGE-2 and ROUGE-SU4) against the average of the human scores. The correlation metrics are Pearson, Kendall, and a revised Kendall correlation explained below.", + "The Pearson correlation between two variables is computed as the covariance of the two variables divided by the product of their standard deviations. This correlation is a good indication of a linear relation between the two variables, but may not be very effective when there is non-linear correlation.", + "The Spearman rank correlation and the Kendall rank correlation are two of the most popular among metrics that aim to detect non-linear correlations. The Spearman rank correlation between two variables can be computed as the Pearson correlation between the rank values of the two variables, whereas the Kendall rank correlation measures the ordinal association between the two variables using Equation DISPLAY_FORM36.", + "It is useful to account for the fact that the results are from 28 independent sets (3 batches in BioASQ 1 and 5 batches each year between BioASQ 2 and BioASQ 6). We therefore also compute a revised Kendall rank correlation measure that only considers pairs of variable values within the same set. The revised metric is computed using Equation DISPLAY_FORM37, where $S$ is the list of different sets.", + "Table TABREF38 shows the results of all correlation metrics. Overall, ROUGE-2 and ROUGE-SU4 give similar correlation values but ROUGE-SU4 is marginally better. Among precision, recall and F1, both precision and F1 are similar, but precision gives a better correlation. Recall shows poor correlation, and virtually no correlation when using the revised Kendall measure. For reporting the evaluation of results, it will be therefore more useful to use precision or F1. However, given the small difference between precision and F1, and given that precision may favour short summaries when used as a function to optimise in a machine learning setting (e.g. using reinforcement learning), it may be best to use F1 as the metric to optimise.", + "Fig. FIGREF40 shows the scatterplots of ROUGE-SU4 recall, precision and F1 with respect to the average human evaluation. We observe that the relation between ROUGE and the human evaluations is not linear, and that Precision and F1 have a clear correlation." + ], + [ + "Table TABREF41 shows the results and details of the runs submitted to BioASQ. The table uses ROUGE-SU4 Recall since this is the metric available at the time of writing this paper. However, note that, as explained in Section SECREF6, these results might differ from the final human evaluation results. Therefore we do not comment on the results, other than observing that the \u201cfirst $n$\u201d baseline produces the same results as the neural regressor. As mentioned in Section SECREF3, the labels used for the classification experiments are the 5 sentences with highest ROUGE-SU4 F1 score." + ], + [ + "Macquarie University's participation in BioASQ 7 focused on the task of generating the ideal answers. The runs use query-based extractive techniques and we experiment with classification, regression, and reinforcement learning approaches. At the time of writing there were no human evaluation results, and based on ROUGE-F1 scores under cross-validation on the training data we observed that classification approaches outperform regression approaches. We experimented with several approaches to label the individual sentences for the classifier and observed that the optimal labelling policy for this task differed from prior work.", + "We also observed poor correlation between ROUGE-Recall and human evaluation metrics and suggest to use alternative automatic evaluation metrics with better correlation, such as ROUGE-Precision or ROUGE-F1. Given the nature of precision-based metrics which could bias the system towards returning short summaries, ROUGE-F1 is probably more appropriate when using at development time, for example for the reward function used by a reinforcement learning system.", + "Reinforcement learning gives promising results, especially in human evaluations made on the runs submitted to BioASQ 6b. This year we introduced very small changes to the runs using reinforcement learning, and will aim to explore more complex reinforcement learning strategies and more complex neural models in the policy and value estimators." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0168/instruction.md b/qasper-0168/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..88c64e13257f0429802d541be637d53515aec5b4 --- /dev/null +++ b/qasper-0168/instruction.md @@ -0,0 +1,110 @@ +Name of Paper: Marrying Universal Dependencies and Universal Morphology + +Question: What are the main sources of recall errors in the mapping? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Background: Morphological Inflection", + "Two Schemata, Two Philosophies", + "Universal Dependencies", + "UniMorph", + "Similarities in the annotation", + "UD treebanks and UniMorph tables", + "A Deterministic Conversion", + "Experiments", + "Intrinsic evaluation", + "Extrinsic evaluation", + "Results", + "Related Work", + "Conclusion and Future Work", + "Acknowledgments" + ], + "paragraphs": [ + [ + "The two largest standardized, cross-lingual datasets for morphological annotation are provided by the Universal Dependencies BIBREF1 and Universal Morphology BIBREF2 , BIBREF3 projects. Each project's data are annotated according to its own cross-lingual schema, prescribing how features like gender or case should be marked. The schemata capture largely similar information, so one may want to leverage both UD's token-level treebanks and UniMorph's type-level lookup tables and unify the two resources. This would permit a leveraging of both the token-level UD treebanks and the type-level UniMorph tables of paradigms. Unfortunately, neither resource perfectly realizes its schema. On a dataset-by-dataset basis, they incorporate annotator errors, omissions, and human decisions when the schemata are underspecified; one such example is in fig:disagreement.", + "A dataset-by-dataset problem demands a dataset-by-dataset solution; our task is not to translate a schema, but to translate a resource. Starting from the idealized schema, we create a rule-based tool for converting UD-schema annotations to UniMorph annotations, incorporating language-specific post-edits that both correct infelicities and also increase harmony between the datasets themselves (rather than the schemata). We apply this conversion to the 31 languages with both UD and UniMorph data, and we report our method's recall, showing an improvement over the strategy which just maps corresponding schematic features to each other. Further, we show similar downstream performance for each annotation scheme in the task of morphological tagging.", + "This tool enables a synergistic use of UniMorph and Universal Dependencies, as well as teasing out the annotation discrepancies within and across projects. When one dataset disobeys its schema or disagrees with a related language, the flaws may not be noticed except by such a methodological dive into the resources. When the maintainers of the resources ameliorate these flaws, the resources move closer to the goal of a universal, cross-lingual inventory of features for morphological annotation.", + "The contributions of this work are:" + ], + [ + "Morphological inflection is the act of altering the base form of a word (the lemma, represented in fixed-width type) to encode morphosyntactic features. As an example from English, prove takes on the form proved to indicate that the action occurred in the past. (We will represent all surface forms in quotation marks.) The process occurs in the majority of the world's widely-spoken languages, typically through meaningful affixes. The breadth of forms created by inflection creates a challenge of data sparsity for natural language processing: The likelihood of observing a particular word form diminishes.", + "A classic result in psycholinguistics BIBREF4 shows that inflectional morphology is a fully productive process. Indeed, it cannot be that humans simply have the equivalent of a lookup table, where they store the inflected forms for retrieval as the syntactic context requires. Instead, there needs to be a mental process that can generate properly inflected words on demand. BIBREF4 showed this insightfully through the wug-test, an experiment where she forced participants to correctly inflect out-of-vocabulary lemmata, such as the novel noun wug.", + "Certain features of a word do not vary depending on its context: In German or Spanish where nouns are gendered, the word for onion will always be grammatically feminine. Thus, to prepare for later discussion, we divide the morphological features of a word into two categories: the modifiable inflectional features and the fixed lexical features.", + "A part of speech (POS) is a coarse syntactic category (like verb) that begets a word's particular menu of lexical and inflectional features. In English, verbs express no gender, and adjectives do not reflect person or number. The part of speech dictates a set of inflectional slots to be filled by the surface forms. Completing these slots for a given lemma and part of speech gives a paradigm: a mapping from slots to surface forms. Regular English verbs have five slots in their paradigm BIBREF5 , which we illustrate for the verb prove, using simple labels for the forms in tab:ptb.", + "A morphosyntactic schema prescribes how language can be annotated\u2014giving stricter categories than our simple labels for prove\u2014and can vary in the level of detail provided. Part of speech tags are an example of a very coarse schema, ignoring details of person, gender, and number. A slightly finer-grained schema for English is the Penn Treebank tagset BIBREF6 , which includes signals for English morphology. For instance, its VBZ tag pertains to the specially inflected 3rd-person singular, present-tense verb form (e.g. proves in tab:ptb).", + "If the tag in a schema is detailed enough that it exactly specifies a slot in a paradigm, it is called a morphosyntactic description (MSD). These descriptions require varying amounts of detail: While the English verbal paradigm is small enough to fit on a page, the verbal paradigm of the Northeast Caucasian language Archi can have over 1500000 slots BIBREF7 ." + ], + [ + "Unlike the Penn Treebank tags, the UD and UniMorph schemata are cross-lingual and include a fuller lexicon of attribute-value pairs, such as Person: 1. Each was built according to a different set of principles. UD's schema is constructed bottom-up, adapting to include new features when they're identified in languages. UniMorph, conversely, is top-down: A cross-lingual survey of the literature of morphological phenomena guided its design. UniMorph aims to be linguistically complete, containing all known morphosyntactic attributes. Both schemata share one long-term goal: a total inventory for annotating the possible morphosyntactic features of a word." + ], + [ + "The Universal Dependencies morphological schema comprises part of speech and 23 additional attributes (also called features in UD) annotating meaning or syntax, as well as language-specific attributes. In order to ensure consistent annotation, attributes are included into the general UD schema if they occur in several corpora. Language-specific attributes are used when only one corpus annotates for a specific feature.", + "The UD schema seeks to balance language-specific and cross-lingual concerns. It annotates for both inflectional features such as case and lexical features such as gender. Additionally, the UD schema annotates for features which can be interpreted as derivational in some languages. For example, the Czech UD guidance uses a Coll value for the Number feature to denote mass nouns (for example, \"lidstvo\" \"humankind\" from the root \"lid\" \"people\").", + "UD represents a confederation of datasets BIBREF8 annotated with dependency relationships (which are not the focus of this work) and morphosyntactic descriptions. Each dataset is an annotated treebank, making it a resource of token-level annotations. The schema is guided by these treebanks, with feature names chosen for relevance to native speakers. (In sec:unimorph, we will contrast this with UniMorph's treatment of morphosyntactic categories.) The UD datasets have been used in the CoNLL shared tasks BIBREF9 ." + ], + [ + "In the Universal Morphological Feature Schema BIBREF10 , there are at least 212 values, spread across 23 attributes. It identifies some attributes that UD excludes like information structure and deixis, as well as providing more values for certain attributes, like 23 different noun classes endemic to Bantu languages. As it is a schema for marking morphology, its part of speech attribute does not have POS values for punctuation, symbols, or miscellany (Punct, Sym, and X in Universal Dependencies).", + "Like the UD schema, the decomposition of a word into its lemma and MSD is directly comparable across languages. Its features are informed by a distinction between universal categories, which are widespread and psychologically real to speakers; and comparative concepts, only used by linguistic typologists to compare languages BIBREF11 . Additionally, it strives for identity of meaning across languages, not simply similarity of terminology. As a prime example, it does not regularly label a dative case for nouns, for reasons explained in depth by BIBREF11 .", + "The UniMorph resources for a language contain complete paradigms extracted from Wiktionary BIBREF12 , BIBREF13 . Word types are annotated to form a database, mapping a lemma\u2013tag pair to a surface form. The schema is explained in detail in BIBREF10 . It has been used in the SIGMORPHON shared task BIBREF14 and the CoNLL\u2013SIGMORPHON shared tasks BIBREF15 , BIBREF16 . Several components of the UniMorph schema have been adopted by UD." + ], + [ + "While the two schemata annotate different features, their annotations often look largely similar. Consider the attested annotation of the Spanish word mandaba (I/he/she/it) commanded. tab:annotations shows that these annotations share many attributes.", + "Some conversions are straightforward: VERB to V, Mood=Ind to IND, Number=Sing to SG, and Person=3 to 3. One might also suggest mapping Tense=Imp to IPFV, though this crosses semantic categories: IPFV represents the imperfective aspect, whereas Tense=Imp comes from imperfect, the English name often given to Spanish's pasado continuo form. The imperfect is a verb form which combines both past tense and imperfective aspect. UniMorph chooses to split this into the atoms PST and IPFV, while UD unifies them according to the familiar name of the tense." + ], + [ + "Prima facie, the alignment task may seem trivial. But we've yet to explore the humans in the loop. This conversion is a hard problem because we're operating on idealized schemata. We're actually annotating human decisions\u2014and human mistakes. If both schemata were perfectly applied, their overlapping attributes could be mapped to each other simply, in a cross-lingual and totally general way. Unfortunately, the resources are imperfect realizations of their schemata. The cross-lingual, cross-resource, and within-resource problems that we'll note mean that we need a tailor-made solution for each language.", + "Showcasing their schemata, the Universal Dependencies and UniMorph projects each present large, annotated datasets. UD's v2.1 release BIBREF1 has 102 treebanks in 60 languages. The large resource, constructed by independent parties, evinces problems in the goal of a universal inventory of annotations. Annotators may choose to omit certain values (like the coerced gender of refrescante in fig:disagreement), and they may disagree on how a linguistic concept is encoded. (See, e.g., BIBREF11 's ( BIBREF11 ) description of the dative case.) Additionally, many of the treebanks were created by fully- or semi-automatic conversion from treebanks with less comprehensive annotation schemata than UD BIBREF0 . For instance, the Spanish word vas you go is incorrectly labeled Gender: Fem|Number: Pl because it ends in a character sequence which is common among feminine plural nouns. (Nevertheless, the part of speech field for vas is correct.)", + "UniMorph's development is more centralized and pipelined. Inflectional paradigms are scraped from Wiktionary, annotators map positions in the scraped data to MSDs, and the mapping is automatically applied to all of the scraped paradigms. Because annotators handle languages they are familiar with (or related ones), realization of the schema is also done on a language-by-language basis. Further, the scraping process does not capture lexical aspects that are not inflected, like noun gender in many languages. The schema permits inclusion of these details; their absence is an artifact of the data collection process. Finally, UniMorph records only exist for nouns, verbs, and adjectives, though the schema is broader than these categories.", + "For these reasons, we treat the corpora as imperfect realizations of the schemata. Moreover, we contend that ambiguity in the schemata leave the door open to allow for such imperfections. With no strict guidance, it's natural that annotators would take different paths. Nevertheless, modulo annotator disagreement, we assume that within a particular corpus, one word form will always be consistently annotated.", + "Three categories of annotation difficulty are missing values, language-specific attributes, and multiword expressions." + ], + [ + "In our work, the goal is not simply to translate one schema into the other, but to translate one resource (the imperfect manifestation of the schema) to match the other. The differences between the schemata and discrepancies in annotation mean that the transformation of annotations from one schema to the other is not straightforward.", + "Two naive options for the conversion are a lookup table of MSDs and a lookup table of the individual attribute-value pairs which comprise the MSDs. The former is untenable: the table of all UD feature combinations (including null features, excluding language-specific attributes) would have 2.445e17 entries. Of course, most combinations won't exist, but this gives a sense of the table's scale. Also, it doesn't leverage the factorial nature of the annotations: constructing the table would require a massive duplication of effort. On the other hand, attribute-value lookup lacks the flexibility to show how a pair of values interacts. Neither approach would handle language- and annotator-specific tendencies in the corpora.", + "Our approach to converting UD MSDs to UniMorph MSDs begins with the attribute-value lookup, then amends it on a language-specific basis. Alterations informed by the MSD and the word form, like insertion, substitution, and deletion, increase the number of agreeing annotations. They are critical for work that examines the MSD monolithically instead of feature-by-feature BIBREF25 , BIBREF26 : Without exact matches, converting the individual tags becomes hollow.", + "Beginning our process, we relied on documentation of the two schemata to create our initial, language-agnostic mapping of individual values. This mapping has 140 pairs in it. Because the mapping was derived purely from the schemata, it is a useful approximation of how well the schemata match up. We note, however, that the mapping does not handle idiosyncrasies like the many uses of dative or features which are represented in UniMorph by argument templates: possession and ergative\u2013absolutive argument marking. The initial step of our conversion is using this mapping to populate a proposed UniMorph MSD.", + "As shown in sec:results, the initial proposal is often frustratingly deficient. Thus we introduce the post-edits. To concoct these, we looked into UniMorph corpora for these languages, compared these to the conversion outputs, and then sought to bring the conversion outputs closer to the annotations in the actual UniMorph corpora. When a form and its lemma existed in both corpora, we could directly inspect how the annotations differed. Our process of iteratively refining the conversion implies a table which exactly maps any combination of UD MSD and its related values (lemma, form, etc.) to a UniMorph MSD, though we do not store the table explicitly.", + "Some conversion rules we've created must be applied before or after others. These sequential dependencies provide conciseness. Our post-editing procedure operates on the initial MSD hypothesis as follows:" + ], + [ + "We evaluate our tool on two tasks:", + "To be clear, our scope is limited to the schema conversion. Future work will explore NLP tasks that exploit both the created token-level UniMorph data and the existing type-level UniMorph data." + ], + [ + "We transform all UD data to the UniMorph. We compare the simple lookup-based transformation to the one with linguistically informed post-edits on all languages with both UD and UniMorph data. We then evaluate the recall of MSDs without partial credit.", + "Because the UniMorph tables only possess annotations for verbs, nouns, adjectives, or some combination, we can only examine performance for these parts of speech. We consider two words to be a match if their form and lemma are present in both resources. Syncretism allows a single surface form to realize multiple MSDs (Spanish mandaba can be first- or third-person), so we define success as the computed MSD matching any of the word's UniMorph MSDs. This gives rise to an equation for recall: of the word\u2013lemma pairs found in both resources, how many of their UniMorph-converted MSDs are present in the UniMorph tables?", + "Our problem here is not a learning problem, so the question is ill-posed. There is no training set, and the two resources for a given language make up a test set. The quality of our model\u2014the conversion tool\u2014comes from how well we encode prior knowledge about the relationship between the UD and UniMorph corpora." + ], + [ + "If the UniMorph-converted treebanks perform differently on downstream tasks, then they convey different information. This signals a failure of the conversion process. As a downstream task, we choose morphological tagging, a critical step to leveraging morphological information on new text.", + "We evaluate taggers trained on the transformed UD data, choosing eight languages randomly from the intersection of UD and UniMorph resources. We report the macro-averaged F1 score of attribute-value pairs on a held-out test set, with official train/validation/test splits provided in the UD treebanks. As a reference point, we also report tagging accuracy on those languages' untransformed data.", + "We use the state-of-the-art morphological tagger of BIBREF0 . It is a factored conditional random field with potentials for each attribute, attribute pair, and attribute transition. The potentials are computed by neural networks, predicting the values of each attribute jointly but not monolithically. Inference with the potentials is performed approximately by loopy belief propagation. We use the authors' hyperparameters.", + "We note a minor implementation detail for the sake of reproducibility. The tagger exploits explicit guidance about the attribute each value pertains to. The UniMorph schema's values are globally unique, but their attributes are not explicit. For example, the UniMorph Masc denotes a masculine gender. We amend the code of BIBREF0 to incorporate attribute identifiers for each UniMorph value." + ], + [ + "We present the intrinsic task's recall scores in tab:recall. Bear in mind that due to annotation errors in the original corpora (like the vas example from sec:resources), the optimal score is not always $100\\%$ . Some shortcomings of recall come from irremediable annotation discrepancies. Largely, we are hamstrung by differences in choice of attributes to annotate. When one resource marks gender and the other marks case, we can't infer the gender of the word purely from its surface form. The resources themselves would need updating to encode the relevant morphosyntactic information. Some languages had a very low number of overlapping forms, and no tag matches or near-matches between them: Arabic, Hindi, Lithuanian, Persian, and Russian. A full list of observed, irremediable discrepancies is presented alongside the codebase.", + "There are three other transformations for which we note no improvement here. Because of the problem in Basque argument encoding in the UniMorph dataset\u2014which only contains verbs\u2014we note no improvement in recall on Basque. Irish also does not improve: UD marks gender on nouns, while UniMorph marks case. Adjectives in UD are also underspecified. The verbs, though, are already correct with the simple mapping. Finally, with Dutch, the UD annotations are impoverished compared to the UniMorph annotations, and missing attributes cannot be inferred without external knowledge.", + "For the extrinsic task, the performance is reasonably similar whether UniMorph or UD; see tab:tagging. A large fluctuation would suggest that the two annotations encode distinct information. On the contrary, the similarities suggest that the UniMorph-mapped MSDs have similar content. We recognize that in every case, tagging F1 increased\u2014albeit by amounts as small as $0.16$ points. This is in part due to the information that is lost in the conversion. UniMorph's schema does not indicate the type of pronoun (demonstrative, interrogative, etc.), and when lexical information is not recorded in UniMorph, we delete it from the MSD during transformation. On the other hand, UniMorph's atomic tags have more parts to guess, but they are often related. (E.g. Ipfv always entails Pst in Spanish.) Altogether, these forces seem to have little impact on tagging performance." + ], + [ + "The goal of a tagset-to-tagset mapping of morphological annotations is shared by the Interset project BIBREF28 . Interset decodes features in the source corpus to a tag interlingua, then encodes that into target corpus features. (The idea of an interlingua is drawn from machine translation, where a prevailing early mindset was to convert to a universal representation, then encode that representation's semantics in the target language. Our approach, by contrast, is a direct flight from the source to the target.) Because UniMorph corpora are noisy, the encoding from the interlingua would have to be rewritten for each target. Further, decoding the UD MSD into the interlingua cannot leverage external information like the lemma and form.", + "The creators of HamleDT sought to harmonize dependency annotations among treebanks, similar to our goal of harmonizing across resources BIBREF29 . The treebanks they sought to harmonize used multiple diverse annotation schemes, which the authors unified under a single scheme.", + " BIBREF30 present mappings into a coarse, universal part of speech for 22 languages. Working with POS tags rather than morphological tags (which have far more dimensions), their space of options to harmonize is much smaller than ours.", + "Our extrinsic evaluation is most in line with the paradigm of BIBREF31 (and similar work therein), who compare syntactic parser performance on UD treebanks annotated with two styles of dependency representation. Our problem differs, though, in that the dependency representations express different relationships, while our two schemata vastly overlap. As our conversion is lossy, we do not appraise the learnability of representations as they did.", + "In addition to using the number of extra rules as a proxy for harmony between resources, one could perform cross-lingual projection of morphological tags BIBREF32 , BIBREF33 . Our approach succeeds even without parallel corpora." + ], + [ + "We created a tool for annotating Universal Dependencies CoNLL-U files with UniMorph annotations. Our tool is ready to use off-the-shelf today, requires no training, and is deterministic. While under-specification necessitates a lossy and imperfect conversion, ours is interpretable. Patterns of mistakes can be identified and ameliorated.", + "The tool allows a bridge between resources annotated in the Universal Dependencies and Universal Morphology (UniMorph) schemata. As the Universal Dependencies project provides a set of treebanks with token-level annotation, while the UniMorph project releases type-level annotated tables, the newfound compatibility opens up new experiments. A prime example of exploiting token- and type-level data is BIBREF34 . That work presents a part-of-speech (POS) dictionary built from Wiktionary, where the POS tagger is also constrained to options available in their type-level POS dictionary, improving performance. Our transformation means that datasets are prepared for similar experiments with morphological tagging. It would also be reasonable to incorporate this tool as a subroutine to UDPipe BIBREF35 and Udapi BIBREF36 . We leave open the task of converting in the opposite direction, turning UniMorph MSDs into Universal Dependencies MSDs.", + "Because our conversion rules are interpretable, we identify shortcomings in both resources, using each as validation for the other. We were able to find specific instances of incorrectly applied UniMorph annotation, as well as specific instances of cross-lingual inconsistency in both resources. These findings will harden both resources and better align them with their goal of universal, cross-lingual annotation." + ], + [ + "We thank Hajime Senuma and John Sylak-Glassman for early comments in devising the starting language-independent mapping from Universal Dependencies to UniMorph." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0192/instruction.md b/qasper-0192/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..f58d53b48b3d11efd3d8a560ab3e1c215c13b24e --- /dev/null +++ b/qasper-0192/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: How Language-Neutral is Multilingual BERT? + +Question: How they demonstrate that language-neutral component is sufficiently general in terms of modeling semantics to allow high-accuracy word-alignment? \ No newline at end of file diff --git a/qasper-0195/instruction.md b/qasper-0195/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..eeb4c8b8b05f874ba019944d74caf1f800ea1f5f --- /dev/null +++ b/qasper-0195/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: How Language-Neutral is Multilingual BERT? + +Question: What challenges this work presents that must be solved to build better language-neutral representations? \ No newline at end of file diff --git a/qasper-0210/instruction.md b/qasper-0210/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..4e82f44e81637d77456a6d6436af3f9b4e783d43 --- /dev/null +++ b/qasper-0210/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Exploring Question Understanding and Adaptation in Neural-Network-Based Question Answering + +Question: what is the architecture of the baseline model? \ No newline at end of file diff --git a/qasper-0217/instruction.md b/qasper-0217/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..0c0dcfc87079401777768d3ce90ffa1df4e9b613 --- /dev/null +++ b/qasper-0217/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction + +Question: What are state-of-the art models for this task? \ No newline at end of file diff --git a/qasper-0218/instruction.md b/qasper-0218/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..88c7aac9fd62af9e709b6d247fdb4f9973239146 --- /dev/null +++ b/qasper-0218/instruction.md @@ -0,0 +1,168 @@ +Name of Paper: Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction + +Question: How better does HAKE model peform than state-of-the-art methods? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work", + "Related Work ::: Model Category", + "Related Work ::: The Ways to Model Hierarchy Structures", + "The Proposed HAKE", + "The Proposed HAKE ::: Two Categories of Entities", + "The Proposed HAKE ::: Hierarchy-Aware Knowledge Graph Embedding", + "The Proposed HAKE ::: Loss Function", + "Experiments and Analysis", + "Experiments and Analysis ::: Experimental Settings", + "Experiments and Analysis ::: Main Results", + "Experiments and Analysis ::: Analysis on Relation Embeddings", + "Experiments and Analysis ::: Analysis on Entity Embeddings", + "Experiments and Analysis ::: Ablation Studies", + "Experiments and Analysis ::: Comparison with Other Related Work", + "Conclusion", + "Appendix", + "A. Analysis on Relation Patterns", + "B. Analysis on Negative Entity Embeddings", + "C. Analysis on Moduli of Entity Embeddings", + "D. More Results on Semantic Hierarchies" + ], + "paragraphs": [ + [ + "Knowledge graphs are usually collections of factual triples\u2014(head entity, relation, tail entity), which represent human knowledge in a structured way. In the past few years, we have witnessed the great achievement of knowledge graphs in many areas, such as natural language processing BIBREF0, question answering BIBREF1, and recommendation systems BIBREF2.", + "Although commonly used knowledge graphs contain billions of triples, they still suffer from the incompleteness problem that a lot of valid triples are missing, as it is impractical to find all valid triples manually. Therefore, knowledge graph completion, also known as link prediction in knowledge graphs, has attracted much attention recently. Link prediction aims to automatically predict missing links between entities based on known links. It is a challenging task as we not only need to predict whether there is a relation between two entities, but also need to determine which relation it is.", + "Inspired by word embeddings BIBREF3 that can well capture semantic meaning of words, researchers turn to distributed representations of knowledge graphs (aka, knowledge graph embeddings) to deal with the link prediction problem. Knowledge graph embeddings regard entities and relations as low dimensional vectors (or matrices, tensors), which can be stored and computed efficiently. Moreover, like in the case of word embeddings, knowledge graph embeddings can preserve the semantics and inherent structures of entities and relations. Therefore, other than the link prediction task, knowledge graph embeddings can also be used in various downstream tasks, such as triple classification BIBREF4, relation inference BIBREF5, and search personalization BIBREF6.", + "The success of existing knowledge graph embedding models heavily relies on their ability to model connectivity patterns of the relations, such as symmetry/antisymmetry, inversion, and composition BIBREF7. For example, TransE BIBREF8, which represent relations as translations, can model the inversion and composition patterns. DistMult BIBREF9, which models the three-way interactions between head entities, relations, and tail entities, can model the symmetry pattern. RotatE BIBREF7, which represents entities as points in a complex space and relations as rotations, can model relation patterns including symmetry/antisymmetry, inversion, and composition. However, many existing models fail to model semantic hierarchies in knowledge graphs.", + "Semantic hierarchy is a ubiquitous property in knowledge graphs. For instance, WordNet BIBREF10 contains the triple [arbor/cassia/palm, hypernym, tree], where \u201ctree\u201d is at a higher level than \u201carbor/cassia/palm\u201d in the hierarchy. Freebase BIBREF11 contains the triple [England, /location/location/contains, Pontefract/Lancaster], where \u201cPontefract/Lancaster\u201d is at a lower level than \u201cEngland\u201d in the hierarchy. Although there exists some work that takes the hierarchy structures into account BIBREF12, BIBREF13, they usually require additional data or process to obtain the hierarchy information. Therefore, it is still challenging to find an approach that is capable of modeling the semantic hierarchy automatically and effectively.", + "In this paper, we propose a novel knowledge graph embedding model\u2014namely, Hierarchy-Aware Knowledge Graph Embedding (HAKE). To model the semantic hierarchies, HAKE is expected to distinguish entities in two categories: (a) at different levels of the hierarchy; (b) at the same level of the hierarchy. Inspired by the fact that entities that have the hierarchical properties can be viewed as a tree, we can use the depth of a node (entity) to model different levels of the hierarchy. Thus, we use modulus information to model entities in the category (a), as the size of moduli can reflect the depth. Under the above settings, entities in the category (b) will have roughly the same modulus, which is hard to distinguish. Inspired by the fact that the points on the same circle can have different phases, we use phase information to model entities in the category (b). Combining the modulus and phase information, HAKE maps entities into the polar coordinate system, where the radial coordinate corresponds to the modulus information and the angular coordinate corresponds to the phase information. Experiments show that our proposed HAKE model can not only clearly distinguish the semantic hierarchies of entities, but also significantly and consistently outperform several state-of-the-art methods on the benchmark datasets.", + "", + "Notations Throughout this paper, we use lower-case letters $h$, $r$, and $t$ to represent head entities, relations, and tail entities, respectively. The triplet $(h,r,t)$ denotes a fact in knowledge graphs. The corresponding boldface lower-case letters $\\textbf {h}$, $\\textbf {r}$ and $\\textbf {t}$ denote the embeddings (vectors) of head entities, relations, and tail entities. The $i$-th entry of a vector $\\textbf {h}$ is denoted as $[\\textbf {h}]_i$. Let $k$ denote the embedding dimension.", + "Let $\\circ :\\mathbb {R}^n\\times \\mathbb {R}^n\\rightarrow \\mathbb {R}^n$ denote the Hadamard product between two vectors, that is,", + "and $\\Vert \\cdot \\Vert _1$, $\\Vert \\cdot \\Vert _2$ denote the $\\ell _1$ and $\\ell _2$ norm, respectively." + ], + [ + "In this section, we will describe the related work and the key differences between them and our work in two aspects\u2014the model category and the way to model hierarchy structures in knowledge graphs." + ], + [ + "Roughly speaking, we can divide knowledge graph embedding models into three categories\u2014translational distance models, bilinear models, and neural network based models. Table TABREF2 exhibits several popular models.", + "Translational distance models describe relations as translations from source entities to target entities. TransE BIBREF8 supposes that entities and relations satisfy $\\textbf {h}+\\textbf {r}\\approx \\textbf {t}$, where $\\textbf {h}, \\textbf {r}, \\textbf {t} \\in \\mathbb {R}^n$, and defines the corresponding score function as $f_r(\\textbf {h},\\textbf {t})=-\\Vert \\textbf {h}+\\textbf {r}-\\textbf {t}\\Vert _{1/2}$. However, TransE does not perform well on 1-N, N-1 and N-N relations BIBREF14. TransH BIBREF14 overcomes the many-to-many relation problem by allowing entities to have distinct representations given different relations. The score function is defined as $f_r(\\textbf {h},\\textbf {t})=-\\Vert \\textbf {h}_{\\perp }+\\textbf {r}-\\textbf {t}_{\\perp }\\Vert _2$, where $\\textbf {h}_{\\perp }$ and $\\textbf {t}_{\\perp }$ are the projections of entities onto relation-specific hyperplanes. ManifoldE BIBREF15 deals with many-to-many problems by relaxing the hypothesis $\\textbf {h}+\\textbf {r}\\approx \\textbf {t}$ to $\\Vert \\textbf {h}+\\textbf {r}-\\textbf {t}\\Vert _2^2\\approx \\theta _r^2$ for each valid triple. In this way, the candidate entities can lie on a manifold instead of exact point. The corresponding score function is defined as $f_r(\\textbf {h},\\textbf {t})=-(\\Vert \\textbf {h}+\\textbf {r}-\\textbf {t}\\Vert _2^2-\\theta _r^2)^2$. More recently, to better model symmetric and antisymmetric relations, RotatE BIBREF7 defines each relation as a rotation from source entities to target entities in a complex vector space. The score function is defined as $f_r(\\textbf {h},\\textbf {t})=-\\Vert \\textbf {h}\\circ \\textbf {r}-\\textbf {t}\\Vert _1$, where $\\textbf {h},\\textbf {r},\\textbf {t}\\in \\mathbb {C}^k$ and $|[\\textbf {r}]_i|=1$.", + "Bilinear models product-based score functions to match latent semantics of entities and relations embodied in their vector space representations. RESCAL BIBREF16 represents each relation as a full rank matrix, and defines the score function as $f_r(\\textbf {h},\\textbf {t})=\\textbf {h}^\\top \\textbf {M}_r \\textbf {t}$, which can also be seen as a bilinear function. As full rank matrices are prone to overfitting, recent works turn to make additional assumptions on $\\textbf {M}_r$. For example, DistMult BIBREF9 assumes $\\textbf {M}_r$ to be a diagonal matrix, and ANALOGY BIBREF19 supposes that $\\textbf {M}_r$ is normal. However, these simplified models are usually less expressive and not powerful enough for general knowledge graphs. Differently, ComplEx BIBREF17 extends DistMult by introducing complex-valued embeddings to better model asymmetric and inverse relations. HolE BIBREF20 combines the expressive power of RESCAL with the efficiency and simplicity of DistMult by using the circular correlation operation.", + "Neural network based models have received greater attention in recent years. For example, MLP BIBREF21 and NTN BIBREF22 use a fully connected neural network to determine the scores of given triples. ConvE BIBREF18 and ConvKB BIBREF23 employ convolutional neural networks to define score functions. Recently, graph convolutional networks are also introduced, as knowledge graphs obviously have graph structures BIBREF24.", + "Our proposed model HAKE belongs to the translational distance models. More specifically, HAKE shares similarities with RotatE BIBREF7, in which the authors claim that they use both modulus and phase information. However, there exist two major differences between RotatE and HAKE. Detailed differences are as follows.", + "The aims are different. RotatE aims to model the relation patterns including symmetry/antisymmetry, inversion, and composition. HAKE aims to model the semantic hierarchy, while it can also model all the relation patterns mentioned above.", + "The ways to use modulus information are different. RotatE models relations as rotations in the complex space, which encourages two linked entities to have the same modulus, no matter what the relation is. The different moduli in RotatE come from the inaccuracy in training. Instead, HAKE explicitly models the modulus information, which significantly outperforms RotatE in distinguishing entities at different levels of the hierarchy." + ], + [ + "Another related problem is how to model hierarchy structures in knowledge graphs. Some recent work considers the problem in different ways. BIBREF25 embed entities and categories jointly into a semantic space and designs models for the concept categorization and dataless hierarchical classification tasks. BIBREF13 use clustering algorithms to model the hierarchical relation structures. BIBREF12 proposed TKRL, which embeds the type information into knowledge graph embeddings. That is, TKRL requires additional hierarchical type information for entities.", + "Different from the previous work, our work", + "considers the link prediction task, which is a more common task for knowledge graph embeddings;", + "can automatically learn the semantic hierarchy in knowledge graphs without using clustering algorithms;", + "does not require any additional information other than the triples in knowledge graphs." + ], + [ + "In this section, we introduce our proposed model HAKE. We first introduce two categories of entities that reflect the semantic hierarchies in knowledge graphs. Afterwards, we introduce our proposed HAKE that can model entities in both of the categories." + ], + [ + "To model the semantic hierarchies of knowledge graphs, a knowledge graph embedding model must be capable of distinguishing entities in the following two categories.", + "Entities at different levels of the hierarchy. For example, \u201cmammal\u201d and \u201cdog\u201d, \u201crun\u201d and \u201dmove\u201d.", + "Entities at the same level of the hierarchy. For example, \u201crose\u201d and \u201cpeony\u201d, \u201ctruck\u201d and \u201dlorry\u201d." + ], + [ + "To model both of the above categories, we propose a hierarchy-aware knowledge graph embedding model\u2014HAKE. HAKE consists of two parts\u2014the modulus part and the phase part\u2014which aim to model entities in the two different categories, respectively. Figure FIGREF13 gives an illustration of the proposed model.", + "To distinguish embeddings in the different parts, we use $\\textbf {e}_m$ ($\\textbf {e}$ can be $\\textbf {h}$ or $\\textbf {t}$) and $\\textbf {r}_m$ to denote the entity embedding and relation embedding in the modulus part, and use $\\textbf {e}_p$ ($\\textbf {e}$ can be $\\textbf {h}$ or $\\textbf {t}$) and $\\textbf {r}_p$ to denote the entity embedding and relation embedding in the phase part.", + "The modulus part aims to model the entities at different levels of the hierarchy. Inspired by the fact that entities that have hierarchical property can be viewed as a tree, we can use the depth of a node (entity) to model different levels of the hierarchy. Therefore, we use modulus information to model entities in the category (a), as moduli can reflect the depth in a tree. Specifically, we regard each entry of $\\textbf {h}_m$ and $\\textbf {t}_m$, that is, $[\\textbf {h}_m]_i$ and $[\\textbf {t}_m]_i$, as a modulus, and regard each entry of $\\textbf {r}_m$, that is, $[\\textbf {r}]_i$, as a scaling transformation between two moduli. We can formulate the modulus part as follows:", + "The corresponding distance function is:", + "Note that we allow the entries of entity embeddings to be negative but restrict the entries of relation embeddings to be positive. This is because that the signs of entity embeddings can help us to predict whether there exists a relation between two entities. For example, if there exists a relation $r$ between $h$ and $t_1$, and no relation between $h$ and $t_2$, then $(h, r, t_1)$ is a positive sample and $(h, r, t_2)$ is a negative sample. Our goal is to minimize $d_r(\\textbf {h}_m, \\textbf {t}_{1,m})$ and maximize $d_r(\\textbf {h}_m, \\textbf {t}_{2,m})$, so as to make a clear distinction between positive and negative samples. For the positive sample, $[\\textbf {h}]_i$ and $[\\textbf {t}_1]_i$ tend to share the same sign, as $[\\textbf {r}_m]_i>0$. For the negative sample, the signs of $[\\textbf {h}_m]_i$ and $[\\textbf {t}_{2,m}]_i$ can be different if we initialize their signs randomly. In this way, $d_r(\\textbf {h}_m, \\textbf {t}_{2,m})$ is more likely to be larger than $d_r(\\textbf {h}_m, \\textbf {t}_{1,m})$, which is exactly what we desire. We will validate this argument by experiments in Section 4 of the supplementary material.", + "Further, we can expect the entities at higher levels of the hierarchy to have smaller modulus, as these entities are more close to the root of the tree.", + "If we use only the modulus part to embed knowledge graphs, then the entities in the category (b) will have the same modulus. Moreover, suppose that $r$ is a relation that reflects the same semantic hierarchy, then $[\\textbf {r}]_i$ will tend to be one, as $h\\circ r\\circ r=h$ holds for all $h$. Hence, embeddings of the entities in the category (b) tend to be the same, which makes it hard to distinguish these entities. Therefore, a new module is required to model the entities in the category (b).", + "The phase part aims to model the entities at the same level of the semantic hierarchy. Inspired by the fact that points on the same circle (that is, have the same modulus) can have different phases, we use phase information to distinguish entities in the category (b). Specifically, we regard each entry of $\\textbf {h}_p$ and $\\textbf {t}_p$, that is, $[\\textbf {h}_p]_i$ and $[\\textbf {t}_p]_i$ as a phase, and regard each entry of $\\textbf {r}_p$, that is, $[\\textbf {r}_p]_i$, as a phase transformation. We can formulate the phase part as follows:", + "The corresponding distance function is:", + "where $\\sin (\\cdot )$ is an operation that applies the sine function to each element of the input. Note that we use a sine function to measure the distance between phases instead of using $\\Vert \\textbf {h}_p+\\textbf {r}_p-\\textbf {t}_p\\Vert _1$, as phases have periodic characteristic. This distance function shares the same formulation with that of pRotatE BIBREF7.", + "Combining the modulus part and the phase part, HAKE maps entities into the polar coordinate system, where the radial coordinate and the angular coordinates correspond to the modulus part and the phase part, respectively. That is, HAKE maps an entity $h$ to $[\\textbf {h}_m;\\textbf {h}_p]$, where $\\textbf {h}_m$ and $\\textbf {h}_p$ are generated by the modulus part and the phase part, respectively, and $[\\,\\cdot \\,; \\,\\cdot \\,]$ denotes the concatenation of two vectors. Obviously, $([\\textbf {h}_m]_i,[\\textbf {h}_p]_i)$ is a 2D point in the polar coordinate system. Specifically, we formulate HAKE as follows:", + "The distance function of HAKE is:", + "where $\\lambda \\in \\mathbb {R}$ is a parameter that learned by the model. The corresponding score function is", + "When two entities have the same moduli, then the modulus part $d_{r,m}(\\textbf {h}_m,\\textbf {t}_m)=0$. However, the phase part $d_{r,p}(\\textbf {h}_p,\\textbf {t}_p)$ can be very different. By combining the modulus part and the phase part, HAKE can model the entities in both the category (a) and the category (b). Therefore, HAKE can model semantic hierarchies of knowledge graphs.", + "When evaluating the models, we find that adding a mixture bias to $d_{r,m}(\\textbf {h},\\textbf {t})$ can help to improve the performance of HAKE. The modified $d_{r,m}(\\textbf {h},\\textbf {t})$ is given by:", + "where $0<\\textbf {r}^{\\prime }_m<1$ is a vector that have the same dimension with $\\textbf {r}_m$. Indeed, the above distance function is equivalent to", + "where $/$ denotes the element-wise division operation. If we let $\\textbf {r}_m\\leftarrow (1-\\textbf {r}_m^{\\prime })/(\\textbf {r}_m+\\textbf {r}_m^{\\prime })$, then the modified distance function is exactly the same as the original one when compare the distances of different entity pairs. For notation convenience, we still use $d_{r,m}(\\textbf {h},\\textbf {t})=\\Vert \\textbf {h}_m\\circ \\textbf {r}_m-\\textbf {t}_m\\Vert _2$ to represent the modulus part. We will conduct ablation studies on the bias in the experiment section." + ], + [ + "To train the model, we use the negative sampling loss functions with self-adversarial training BIBREF7:", + "where $\\gamma $ is a fixed margin, $\\sigma $ is the sigmoid function, and $(h^{\\prime }_i,r,t^{\\prime }_i)$ is the $i$th negative triple. Moreover,", + "is the probability distribution of sampling negative triples, where $\\alpha $ is the temperature of sampling." + ], + [ + "This section is organized as follows. First, we introduce the experimental settings in detail. Then, we show the effectiveness of our proposed model on three benchmark datasets. Finally, we analyze the embeddings generated by HAKE, and show the results of ablation studies. The code of HAKE is available on GitHub at https://github.com/MIRALab-USTC/KGE-HAKE." + ], + [ + "We evaluate our proposed models on three commonly used knowledge graph datasets\u2014WN18RR BIBREF26, FB15k-237 BIBREF18, and YAGO3-10 BIBREF27. Details of these datasets are summarized in Table TABREF18.", + "WN18RR, FB15k-237, and YAGO3-10 are subsets of WN18 BIBREF8, FB15k BIBREF8, and YAGO3 BIBREF27, respectively. As pointed out by BIBREF26 and BIBREF18, WN18 and FB15k suffer from the test set leakage problem. One can attain the state-of-the-art results even using a simple rule based model. Therefore, we use WN18RR and FB15k-237 as the benchmark datasets.", + "Evaluation Protocol Following BIBREF8, for each triple $(h,r,t)$ in the test dataset, we replace either the head entity $h$ or the tail entity $t$ with each candidate entity to create a set of candidate triples. We then rank the candidate triples in descending order by their scores. It is worth noting that we use the \u201cFiltered\u201d setting as in BIBREF8, which does not take any existing valid triples into accounts at ranking. We choose Mean Reciprocal Rank (MRR) and Hits at N (H@N) as the evaluation metrics. Higher MRR or H@N indicate better performance.", + "Training Protocol We use Adam BIBREF28 as the optimizer, and use grid search to find the best hyperparameters based on the performance on the validation datasets. To make the model easier to train, we add an additional coefficient to the distance function, i.e., $d_{r}(\\textbf {h},\\textbf {t})=\\lambda _1d_{r,m}(\\textbf {h}_m,\\textbf {t}_m)+\\lambda _2 d_{r,p}(\\textbf {h}_p,\\textbf {t}_p)$, where $\\lambda _1,\\lambda _2\\in \\mathbb {R}$.", + "Baseline Model One may argue that the phase part is unnecessary, as we can distinguish entities in the category (b) by allowing $[\\textbf {r}]_i$ to be negative. We propose a model\u2014ModE\u2014that uses only the modulus part but allow $[\\textbf {r}]_i<0$. Specifically, the distance function of ModE is" + ], + [ + "In this part, we show the performance of our proposed models\u2014HAKE and ModE\u2014against existing state-of-the-art methods, including TransE BIBREF8, DistMult BIBREF9, ComplEx BIBREF17, ConvE BIBREF18, and RotatE BIBREF7.", + "Table TABREF19 shows the performance of HAKE, ModE, and several previous models. Our baseline model ModE shares similar simplicity with TransE, but significantly outperforms it on all datasets. Surprisingly, ModE even outperforms more complex models such as DistMult, ConvE and Complex on all datasets, and beats the state-of-the-art model\u2014RotatE\u2014on FB15k-237 and YAGO3-10 datasets, which demonstrates the great power of modulus information. Table TABREF19 also shows that our HAKE significantly outperforms existing state-of-the-art methods on all datasets.", + "WN18RR dataset consists of two kinds of relations: the symmetric relations such as $\\_similar\\_to$, which link entities in the category (b); other relations such as $\\_hypernym$ and $\\_member\\_meronym$, which link entities in the category (a). Actually, RotatE can model entities in the category (b) very well BIBREF7. However, HAKE gains a 0.021 higher MRR, a 2.4% higher H@1, and a 2.4% higher H@3 against RotatE, respectively. The superior performance of HAKE compared with RotatE implies that our proposed model can better model different levels in the hierarchy.", + "FB15k-237 dataset has more complex relation types and fewer entities, compared with WN18RR and YAGO3-10. Although there are relations that reflect hierarchy in FB15k-237, there are also lots of relations, such as \u201c/location/location/time_zones\u201d and \u201c/film/film/prequel\u201d, that do not lead to hierarchy. The characteristic of this dataset accounts for why our proposed models doesn't outperform the previous state-of-the-art as much as that of WN18RR and YAGO3-10 datasets. However, the results also show that our models can gain better performance so long as there exists semantic hierarchies in knowledge graphs. As almost all knowledge graphs have such hierarchy structures, our model is widely applicable.", + "YAGO3-10 datasets contains entities with high relation-specific indegree BIBREF18. For example, the link prediction task $(?, hasGender, male)$ has over 1000 true answers, which makes the task challenging. Fortunately, we can regard \u201cmale\u201d as an entity at higher level of the hierarchy and the predicted head entities as entities at lower level. In this way, YAGO3-10 is a dataset that clearly has semantic hierarchy property, and we can expect that our proposed models is capable of working well on this dataset. Table TABREF19 validates our expectation. Both ModE and HAKE significantly outperform the previous state-of-the-art. Notably, HAKE gains a 0.050 higher MRR, 6.0% higher H@1 and 4.6% higher H@3 than RotatE, respectively." + ], + [ + "In this part, we first show that HAKE can effectively model the hierarchy structures by analyzing the moduli of relation embeddings. Then, we show that the phase part of HAKE can help us to distinguish entities at the same level of the hierarchy by analyzing the phases of relation embeddings.", + "In Figure FIGREF20, we plot the distribution histograms of moduli of six relations. These relations are drawn from WN18RR, FB15k-237, and YAGO3-10. Specifically, the relations in Figures FIGREF20a, FIGREF20c, FIGREF20e and FIGREF20f are drawn from WN18RR. The relation in Figure FIGREF20d is drawn from FB15k-237. The relation in Figure FIGREF20b is drawn from YAGO3-10. We divide the relations in Figure FIGREF20 into three groups.", + "Relations in Figures FIGREF20c and FIGREF20d connect the entities at the same level of the semantic hierarchy;", + "Relations in Figures FIGREF20a and FIGREF20b represent that tail entities are at higher levels than head entities of the hierarchy;", + "Relations in Figures FIGREF20e and FIGREF20f represent that tail entities are at lower levels than head entities of the hierarchy.", + "As described in the model description section, we expect entities at higher levels of the hierarchy to have small moduli. The experiments validate our expectation. For both ModE and HAKE, most entries of the relations in the group (A) take values around one, which leads to that the head entities and tail entities have approximately the same moduli. In the group (B), most entries of the relations take values less than one, which results in that the head entities have smaller moduli than the tail entities. The cases in the group (C) are contrary to that in the group (B). These results show that our model can capture the semantic hierarchies in knowledge graphs. Moreover, compared with ModE, the relation embeddings' moduli of HAKE have lower variances, which shows that HAKE can model hierarchies more clearly.", + "As mentioned above, relations in the group (A) reflect the same semantic hierarchy, and are expected to have the moduli of about one. Obviously, it is hard to distinguish entities linked by these relations only using the modulus part. In Figure FIGREF22, we plot the phases of the relations in the group (A). The results show that the entities at the same level of the hierarchy can be distinguished by their phases, as many phases have the values of $\\pi $." + ], + [ + "In this part, to further show that HAKE can capture the semantic hierarchies between entities, we visualize the embeddings of several entity pairs.", + "We plot the entity embeddings of two models: the previous state-of-the-art RotatE and our proposed HAKE. RotatE regards each entity as a group of complex numbers. As a complex number can be seen as a point on a 2D plane, we can plot the entity embeddings on a 2D plane. As for HAKE, we have mentioned that it maps entities into the polar coordinate system. Therefore, we can also plot the entity embeddings generated by HAKE on a 2D plane based on their polar coordinates. For a fair comparison, we set $k=500$. That is, each plot contains 500 points, and the actual dimension of entity embeddings is 1000. Note that we use the logarithmic scale to better display the differences between entity embeddings. As all the moduli have values less than one, after applying the logarithm operation, the larger radii in the figures will actually represent smaller modulus.", + "Figure FIGREF29 shows the visualization results of three triples from the WN18RR dataset. Compared with the tail entities, the head entities in Figures FIGREF29a, FIGREF29b, and FIGREF29c are at lower levels, similar levels, higher levels in the semantic hierarchy, respectively. We can see that there exist clear concentric circles in the visualization results of HAKE, which demonstrates that HAKE can effectively model the semantic hierarchies. However, in RotatE, the entity embeddings in all three subfigures are mixed, making it hard to distinguish entities at different levels in the hierarchy." + ], + [ + "In this part, we conduct ablation studies on the modulus part and the phase part of HAKE, as well as the mixture bias item. Table TABREF26 shows the results on three benchmark datasets.", + "We can see that the bias can improve the performance of HAKE on nearly all metrics. Specifically, the bias improves the H@1 score of $4.7\\%$ on YAGO3-10 dataset, which illustrates the effectiveness of the bias.", + "We also observe that the modulus part of HAKE does not perform well on all datasets, due to its inability to distinguish the entities at the same level of the hierarchy. When only using the phase part, HAKE degenerates to the pRotatE model BIBREF7. It performs better than the modulus part, because it can well model entities at the same level of the hierarchy. However, our HAKE model significantly outperforms the modulus part and the phase part on all datasets, which demonstrates the importance to combine the two parts for modeling semantic hierarchies in knowledge graphs." + ], + [ + "We compare our models with TKRL models BIBREF12, which also aim to model the hierarchy structures. For the difference between HAKE and TKRL, please refer to the Related Work section. Table TABREF27 shows the H@10 scores of HAKE and TKRLs on FB15k dataset. The best performance of TKRL is .734 obtained by the WHE+STC version, while the H@10 score of our HAKE model is .884. The results show that HAKE significantly outperforms TKRL, though it does not require additional information." + ], + [ + "To model the semantic hierarchies in knowledge graphs, we propose a novel hierarchy-aware knowledge graph embedding model\u2014HAKE\u2014which maps entities into the polar coordinate system. Experiments show that our proposed HAKE significantly outperforms several existing state-of-the-art methods on benchmark datasets for the link prediction task. A further investigation shows that HAKE is capable of modeling entities at both different levels and the same levels in the semantic hierarchies." + ], + [ + "In this appendix, we will provide analysis on relation patterns, negative entity embeddings, and moduli of entity embeddings. Then, we will give more visualization results on semantic hierarchies." + ], + [ + "In this section, we prove that our HAKE model can infer the (anti)symmetry, inversion and composition relation patterns. Detailed propositions and their proofs are as follows.", + "Proposition 1 HAKE can infer the (anti)symmetry pattern.", + "If $r(x, y)$ and $r(y, x)$ hold, we have", + "Then we have", + "Otherwise, if $r(x, y)$ and $\\lnot r(y, x)$ hold, we have", + "Proposition 2 HAKE can infer the inversion pattern.", + "If $r_1(x, y)$ and $r_2(y, x)$ hold, we have", + "Then, we have", + "", + "Proposition 3 HAKE can infer the composition pattern.", + "If $r_1(x, z)$, $r_2(x, y)$ and $r_3(y, z)$ hold, we have", + "Then we have" + ], + [ + "We denote the linked entity pairs as the set of entity pairs linked by some relation, and denote the unlinked entity pairs as the set of entity pairs that no triple contains in the train/valid/test dataset. It is worth noting that the unlinked paris may contain valid triples, as the knowledge graph is incomplete. For both the linked and the unlinked entity pairs, we count the embedding entries of two entities that have different signs. Figure FIGREF34 shows the result.", + "For the linked entity pairs, as we expected, most of the entries have the same sign. Due to the large amount of unlinked entity pairs, we randomly sample a part of them for plotting. For the unlinked entity pairs, around half of the entries have different signs, which is consistent with the random initialization. The results support our hypothesis that the negative signs of entity embeddings can help our model to distinguish positive and negative triples." + ], + [ + "Figure FIGREF37 shows the modulus of entity embeddings. We can observe that RotatE encourages the modulus of embeddings to be the same, as the relations are modeled as rotations in a complex space. Compared with RotatE, the modulus of entity embeddings in HAKE are more dispersed, making it to have more potential to model the semantic hierarchies." + ], + [ + "In this part, we visualize more triples from WN18RR. We plot the head and tail entities on 2D planes using the same method as that in the main text. The visualization results are in Figure FIGREF41, where the subcaptions demonstrate the corresponding triples. The figures show that, compared with RotatE, our HAKE model can better model the entities both in different hierarchies and in the same hierarchy.", + "" + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0219/instruction.md b/qasper-0219/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..2b31259fa1b8ead35a1a401854fb65e2323a82a6 --- /dev/null +++ b/qasper-0219/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction + +Question: How are entities mapped onto polar coordinate system? \ No newline at end of file diff --git a/qasper-0221/instruction.md b/qasper-0221/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..625c6ed8480fbf8f0978bd9bdf6ff8930c13a078 --- /dev/null +++ b/qasper-0221/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Machine Translation from Natural Language to Code using Long-Short Term Memory + +Question: What dataset do they use? \ No newline at end of file diff --git a/qasper-0226/instruction.md b/qasper-0226/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..e15fff258106b70df912322b35f7b5a008acf77c --- /dev/null +++ b/qasper-0226/instruction.md @@ -0,0 +1,80 @@ +Name of Paper: Machine Translation from Natural Language to Code using Long-Short Term Memory + +Question: What programming language is target language? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Problem Description", + "Problem Description ::: Programming Language Diversity", + "Problem Description ::: Human Language Factor", + "Problem Description ::: NLP of statements", + "Proposed Methodology", + "Proposed Methodology ::: Statistical Machine Translation", + "Proposed Methodology ::: Statistical Machine Translation ::: Data Preparation", + "Proposed Methodology ::: Statistical Machine Translation ::: Vocabulary Generation", + "Proposed Methodology ::: Statistical Machine Translation ::: Neural Model Training", + "Result Analysis", + "Conclusion & Future Works", + "Acknowledgment" + ], + "paragraphs": [ + [ + "Removing computer-human language barrier is an inevitable advancement researchers are thriving to achieve for decades. One of the stages of this advancement will be coding through natural human language instead of traditional programming language. On naturalness of computer programming D. Knuth said, \u201cLet us change our traditional attitude to the construction of programs: Instead of imagining that our main task is to instruct a computer what to do, let us concentrate rather on explaining to human beings what we want a computer to do.\u201dBIBREF0. Unfortunately, learning programming language is still necessary to instruct it. Researchers and developers are working to overcome this human-machine language barrier. Multiple branches exists to solve this challenge (i.e. inter-conversion of different programming language to have universally connected programming languages). Automatic code generation through natural language is not a new concept in computer science studies. However, it is difficult to create such tool due to these following three reasons\u2013", + "Programming languages are diverse", + "An individual person expresses logical statements differently than other", + "Natural Language Processing (NLP) of programming statements is challenging since both human and programming language evolve over time", + "In this paper, a neural approach to translate pseudo-code or algorithm like human language expression into programming language code is proposed." + ], + [ + "Code repositories (i.e. Git, SVN) flourished in the last decade producing big data of code allowing data scientists to perform machine learning on these data. In 2017, Allamanis M et al. published a survey in which they presented the state-of-the-art of the research areas where machine learning is changing the way programmers code during software engineering and development process BIBREF1. This paper discusses what are the restricting factors of developing such text-to-code conversion method and what problems need to be solved\u2013" + ], + [ + "According to the sources, there are more than a thousand actively maintained programming languages, which signifies the diversity of these language . These languages were created to achieve different purpose and use different syntaxes. Low-level languages such as assembly languages are easier to express in human language because of the low or no abstraction at all whereas high-level, or Object-Oriented Programing (OOP) languages are more diversified in syntax and expression, which is challenging to bring into a unified human language structure. Nonetheless, portability and transparency between different programming languages also remains a challenge and an open research area. George D. et al. tried to overcome this problem through XML mapping BIBREF2. They tried to convert codes from C++ to Java using XML mapping as an intermediate language. However, the authors encountered challenges to support different features of both languages." + ], + [ + "One of the motivations behind this paper is - as long as it is about programming, there is a finite and small set of expression which is used in human vocabulary. For instance, programmers express a for-loop in a very few specific ways BIBREF3. Variable declaration and value assignment expressions are also limited in nature. Although all codes are executable, human representation through text may not due to the semantic brittleness of code. Since high-level languages have a wide range of syntax, programmers use different linguistic expressions to explain those. For instance, small changes like swapping function arguments can significantly change the meaning of the code. Hence the challenge remains in processing human language to understand it properly which brings us to the next problem-" + ], + [ + "Although there is a finite set of expressions for each programming statements, it is a challenge to extract information from the statements of the code accurately. Semantic analysis of linguistic expression plays an important role in this information extraction. For instance, in case of a loop, what is the initial value? What is the step value? When will the loop terminate?", + "Mihalcea R. et al. has achieved a variable success rate of 70-80% in producing code just from the problem statement expressed in human natural language BIBREF3. They focused solely on the detection of step and loops in their research. Another research group from MIT, Lei et al. use a semantic learning model for text to detect the inputs. The model produces a parser in C++ which can successfully parse more than 70% of the textual description of input BIBREF4. The test dataset and model was initially tested and targeted against ACM-ICPC participants\u00ednputs which contains diverse and sometimes complex input instructions.", + "A recent survey from Allamanis M. et al. presented the state-of-the-art on the area of naturalness of programming BIBREF1. A number of research works have been conducted on text-to-code or code-to-text area in recent years. In 2015, Oda et al. proposed a way to translate each line of Python code into natural language pseudocode using Statistical Machine Learning Technique (SMT) framework BIBREF5 was used. This translation framework was able to - it can successfully translate the code to natural language pseudo coded text in both English and Japanese. In the same year, Chris Q. et al. mapped natural language with simple if-this-then-that logical rules BIBREF6. Tihomir G. and Viktor K. developed an Integrated Development Environment (IDE) integrated code assistant tool anyCode for Java which can search, import and call function just by typing desired functionality through text BIBREF7. They have used model and mapping framework between function signatures and utilized resources like WordNet, Java Corpus, relational mapping to process text online and offline.", + "Recently in 2017, P. Yin and G. Neubig proposed a semantic parser which generates code through its neural model BIBREF8. They formulated a grammatical model which works as a skeleton for neural network training. The grammatical rules are defined based on the various generalized structure of the statements in the programming language." + ], + [ + "The use of machine learning techniques such as SMT proved to be at most 75% successful in converting human text to executable code. BIBREF9. A programming language is just like a language with less vocabulary compared to a typical human language. For instance, the code vocabulary of the training dataset was 8814 (including variable, function, class names), whereas the English vocabulary to express the same code was 13659 in total. Here, programming language is considered just like another human language and widely used SMT techniques have been applied." + ], + [ + "SMT techniques are widely used in Natural Language Processing (NLP). SMT plays a significant role in translation from one language to another, especially in lexical and grammatical rule extraction. In SMT, bilingual grammatical structures are automatically formed by statistical approaches instead of explicitly providing a grammatical model. This reduces months and years of work which requires significant collaboration between bi-lingual linguistics. Here, a neural network based machine translation model is used to translate regular text into programming code." + ], + [ + "SMT techniques require a parallel corpus in thr source and thr target language. A text-code parallel corpus similar to Fig. FIGREF12 is used in training. This parallel corpus has 18805 aligned data in it . In source data, the expression of each line code is written in the English language. In target data, the code is written in Python programming language." + ], + [ + "To train the neural model, the texts should be converted to a computational entity. To do that, two separate vocabulary files are created - one for the source texts and another for the code. Vocabulary generation is done by tokenization of words. Afterwards, the words are put into their contextual vector space using the popular word2vec BIBREF10 method to make the words computational." + ], + [ + "In order to train the translation model between text-to-code an open source Neural Machine Translation (NMT) - OpenNMT implementation is utilized BIBREF11. PyTorch is used as Neural Network coding framework. For training, three types of Recurrent Neural Network (RNN) layers are used \u2013 an encoder layer, a decoder layer and an output layer. These layers together form a LSTM model. LSTM is typically used in seq2seq translation.", + "In Fig. FIGREF13, the neural model architecture is demonstrated. The diagram shows how it takes the source and target text as input and uses it for training. Vector representation of tokenized source and target text are fed into the model. Each token of the source text is passed into an encoder cell. Target text tokens are passed into a decoder cell. Encoder cells are part of the encoder RNN layer and decoder cells are part of the decoder RNN layer. End of the input sequence is marked by a $<$eos$>$ token. Upon getting the $<$eos$>$ token, the final cell state of encoder layer initiate the output layer sequence. At each target cell state, attention is applied with the encoder RNN state and combined with the current hidden state to produce the prediction of next target token. This predictions are then fed back to the target RNN. Attention mechanism helps us to overcome the fixed length restriction of encoder-decoder sequence and allows us to process variable length between input and output sequence. Attention uses encoder state and pass it to the decoder cell to give particular attention to the start of an output layer sequence. The encoder uses an initial state to tell the decoder what it is supposed to generate. Effectively, the decoder learns to generate target tokens, conditioned on the input sequence. Sigmoidal optimization is used to optimize the prediction." + ], + [ + "Training parallel corpus had 18805 lines of annotated code in it. The training model is executed several times with different training parameters. During the final training process, 500 validation data is used to generate the recurrent neural model, which is 3% of the training data. We run the training with epoch value of 10 with a batch size of 64. After finishing the training, the accuracy of the generated model using validation data from the source corpus was 74.40% (Fig. FIGREF17).", + "Although the generated code is incoherent and often predict wrong code token, this is expected because of the limited amount of training data. LSTM generally requires a more extensive set of data (100k+ in such scenario) to build a more accurate model. The incoherence can be resolved by incorporating coding syntax tree model in future. For instance\u2013", + "\"define the method tzname with 2 arguments: self and dt.\"", + "is translated into\u2013", + "def __init__ ( self , regex ) :.", + "The translator is successfully generating the whole codeline automatically but missing the noun part (parameter and function name) part of the syntax." + ], + [ + "The main advantage of translating to a programming language is - it has a concrete and strict lexical and grammatical structure which human languages lack. The aim of this paper was to make the text-to-code framework work for general purpose programming language, primarily Python. In later phase, phrase-based word embedding can be incorporated for improved vocabulary mapping. To get more accurate target code for each line, Abstract Syntax Tree(AST) can be beneficial.", + "The contribution of this research is a machine learning model which can turn the human expression into coding expressions. This paper also discusses available methods which convert natural language to programming language successfully in fixed or tightly bounded linguistic paradigm. Approaching this problem using machine learning will give us the opportunity to explore the possibility of unified programming interface as well in the future." + ], + [ + "We would like to thank Dr. Khandaker Tabin Hasan, Head of the Depertment of Computer Science, American International University-Bangladesh for his inspiration and encouragement in all of our research works. Also, thanks to Future Technology Conference - 2019 committee for partially supporting us to join the conference and one of our colleague - Faheem Abrar, Software Developer for his thorough review and comments on this research work and supporting us by providing fund." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0228/instruction.md b/qasper-0228/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..a9be6fd97501b230d940ebe1cf29c4daf4e2083f --- /dev/null +++ b/qasper-0228/instruction.md @@ -0,0 +1,240 @@ +Name of Paper: A Survey and Taxonomy of Adversarial Neural Networks for Text-to-Image Synthesis + +Question: Is text-to-image synthesis trained is suppervized or unsuppervized manner? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Introduction ::: blackTraditional Learning Based Text-to-image Synthesis", + "Introduction ::: GAN Based Text-to-image Synthesis", + "Related Work", + "Preliminaries and Frameworks", + "Preliminaries and Frameworks ::: Generative Adversarial Neural Network", + "Preliminaries and Frameworks ::: cGAN: Conditional GAN", + "Preliminaries and Frameworks ::: Simple GAN Frameworks for Text-to-Image Synthesis", + "Preliminaries and Frameworks ::: Advanced GAN Frameworks for Text-to-Image Synthesis", + "Text-to-Image Synthesis Taxonomy and Categorization", + "Text-to-Image Synthesis Taxonomy and Categorization ::: GAN based Text-to-Image Synthesis Taxonomy", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Semantic Enhancement GANs", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Semantic Enhancement GANs ::: DC-GAN", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Semantic Enhancement GANs ::: DC-GAN Extensions", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Semantic Enhancement GANs ::: MC-GAN", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Resolution Enhancement GANs", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Resolution Enhancement GANs ::: StackGAN", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Resolution Enhancement GANs ::: StackGAN++", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Resolution Enhancement GANs ::: AttnGAN", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Resolution Enhancement GANs ::: HDGAN", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Diversity Enhancement GANs", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Diversity Enhancement GANs ::: AC-GAN", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Diversity Enhancement GANs ::: TAC-GAN", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Diversity Enhancement GANs ::: Text-SeGAN", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Diversity Enhancement GANs ::: MirrorGAN and Scene Graph GAN", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Motion Enhancement GANs", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Motion Enhancement GANs ::: ObamaNet and T2S", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Motion Enhancement GANs ::: T2V", + "Text-to-Image Synthesis Taxonomy and Categorization ::: Motion Enhancement GANs ::: StoryGAN", + "GAN Based Text-to-Image Synthesis Applications, Benchmark, and Evaluation and Comparisons ::: Text-to-image Synthesis Applications", + "GAN Based Text-to-Image Synthesis Applications, Benchmark, and Evaluation and Comparisons ::: Text-to-image Synthesis Benchmark Datasets", + "GAN Based Text-to-Image Synthesis Applications, Benchmark, and Evaluation and Comparisons ::: Text-to-image Synthesis Benchmark Evaluation Metrics", + "GAN Based Text-to-Image Synthesis Applications, Benchmark, and Evaluation and Comparisons ::: GAN Based Text-to-image Synthesis Results Comparison", + "GAN Based Text-to-Image Synthesis Applications, Benchmark, and Evaluation and Comparisons ::: Notable Mentions", + "Conclusion", + "conflict of interest" + ], + "paragraphs": [ + [ + "\u201c (GANs), and the variations that are now being proposed is the most interesting idea in the last 10 years in ML, in my opinion.\u201d (2016)", + "\u2013 Yann LeCun", + "A picture is worth a thousand words! While written text provide efficient, effective, and concise ways for communication, visual content, such as images, is a more comprehensive, accurate, and intelligible method of information sharing and understanding. Generation of images from text descriptions, i.e. text-to-image synthesis, is a complex computer vision and machine learning problem that has seen great progress over recent years. Automatic image generation from natural language may allow users to describe visual elements through visually-rich text descriptions. The ability to do so effectively is highly desirable as it could be used in artificial intelligence applications such as computer-aided design, image editing BIBREF0, BIBREF1, game engines for the development of the next generation of video gamesBIBREF2, and pictorial art generation BIBREF3." + ], + [ + "In the early stages of research, text-to-image synthesis was mainly carried out through a search and supervised learning combined process BIBREF4, as shown in Figure FIGREF4. In order to connect text descriptions to images, one could use correlation between keywords (or keyphrase) & images that identifies informative and \u201cpicturable\u201d text units; then, these units would search for the most likely image parts conditioned on the text, eventually optimizing the picture layout conditioned on both the text and the image parts. Such methods often integrated multiple artificial intelligence key components, including natural language processing, computer vision, computer graphics, and machine learning.", + "The major limitation of the traditional learning based text-to-image synthesis approaches is that they lack the ability to generate new image content; they can only change the characteristics of the given/training images. Alternatively, research in generative models has advanced significantly and delivers solutions to learn from training images and produce new visual content. For example, Attribute2Image BIBREF5 models each image as a composite of foreground and background. In addition, a layered generative model with disentangled latent variables is learned, using a variational auto-encoder, to generate visual content. Because the learning is customized/conditioned by given attributes, the generative models of Attribute2Image can generate images with respect to different attributes, such as gender, hair color, age, etc., as shown in Figure FIGREF5." + ], + [ + "Although generative model based text-to-image synthesis provides much more realistic image synthesis results, the image generation is still conditioned by the limited attributes. In recent years, several papers have been published on the subject of text-to-image synthesis. Most of the contributions from these papers rely on multimodal learning approaches that include generative adversarial networks and deep convolutional decoder networks as their main drivers to generate entrancing images from text BIBREF7, BIBREF8, BIBREF9, BIBREF10, BIBREF11.", + "First introduced by Ian Goodfellow et al. BIBREF9, generative adversarial networks (GANs) consist of two neural networks paired with a discriminator and a generator. These two models compete with one another, with the generator attempting to produce synthetic/fake samples that will fool the discriminator and the discriminator attempting to differentiate between real (genuine) and synthetic samples. Because GANs' adversarial training aims to cause generators to produce images similar to the real (training) images, GANs can naturally be used to generate synthetic images (image synthesis), and this process can even be customized further by using text descriptions to specify the types of images to generate, as shown in Figure FIGREF6.", + "Much like text-to-speech and speech-to-text conversion, there exists a wide variety of problems that text-to-image synthesis could solve in the computer vision field specifically BIBREF8, BIBREF12. Nowadays, researchers are attempting to solve a plethora of computer vision problems with the aid of deep convolutional networks, generative adversarial networks, and a combination of multiple methods, often called multimodal learning methods BIBREF8. For simplicity, multiple learning methods will be referred to as multimodal learning hereafter BIBREF13. Researchers often describe multimodal learning as a method that incorporates characteristics from several methods, algorithms, and ideas. This can include ideas from two or more learning approaches in order to create a robust implementation to solve an uncommon problem or improve a solution BIBREF8, BIBREF14, BIBREF15, BIBREF16, BIBREF17.", + "black In this survey, we focus primarily on reviewing recent works that aim to solve the challenge of text-to-image synthesis using generative adversarial networks (GANs). In order to provide a clear roadmap, we propose a taxonomy to summarize reviewed GANs into four major categories. Our review will elaborate the motivations of methods in each category, analyze typical models, their network architectures, and possible drawbacks for further improvement. The visual abstract of the survey and the list of reviewed GAN frameworks is shown in Figure FIGREF8.", + "black The remainder of the survey is organized as follows. Section 2 presents a brief summary of existing works on subjects similar to that of this paper and highlights the key distinctions making ours unique. Section 3 gives a short introduction to GANs and some preliminary concepts related to image generation, as they are the engines that make text-to-image synthesis possible and are essential building blocks to achieve photo-realistic images from text descriptions. Section 4 proposes a taxonomy to summarize GAN based text-to-image synthesis, discusses models and architectures of novel works focused solely on text-to-image synthesis. This section will also draw key contributions from these works in relation to their applications. Section 5 reviews GAN based text-to-image synthesis benchmarks, performance metrics, and comparisons, including a simple review of GANs for other applications. In section 6, we conclude with a brief summary and outline ideas for future interesting developments in the field of text-to-image synthesis." + ], + [ + "With the growth and success of GANs, deep convolutional decoder networks, and multimodal learning methods, these techniques were some of the first procedures which aimed to solve the challenge of image synthesis. Many engineers and scientists in computer vision and AI have contributed through extensive studies and experiments, with numerous proposals and publications detailing their contributions. Because GANs, introduced by BIBREF9, are emerging research topics, their practical applications to image synthesis are still in their infancy. Recently, many new GAN architectures and designs have been proposed to use GANs for different applications, e.g. using GANs to generate sentimental texts BIBREF18, or using GANs to transform natural images into cartoons BIBREF19.", + "Although GANs are becoming increasingly popular, very few survey papers currently exist to summarize and outline contemporaneous technical innovations and contributions of different GAN architectures BIBREF20, BIBREF21. Survey papers specifically attuned to analyzing different contributions to text-to-image synthesis using GANs are even more scarce. We have thus found two surveys BIBREF6, BIBREF7 on image synthesis using GANs, which are the two most closely related publications to our survey objective. In the following paragraphs, we briefly summarize each of these surveys and point out how our objectives differ from theirs.", + "In BIBREF6, the authors provide an overview of image synthesis using GANs. In this survey, the authors discuss the motivations for research on image synthesis and introduce some background information on the history of GANs, including a section dedicated to core concepts of GANs, namely generators, discriminators, and the min-max game analogy, and some enhancements to the original GAN model, such as conditional GANs, addition of variational auto-encoders, etc.. In this survey, we will carry out a similar review of the background knowledge because the understanding of these preliminary concepts is paramount for the rest of the paper. Three types of approaches for image generation are reviewed, including direct methods (single generator and discriminator), hierarchical methods (two or more generator-discriminator pairs, each with a different goal), and iterative methods (each generator-discriminator pair generates a gradually higher-resolution image). Following the introduction, BIBREF6 discusses methods for text-to-image and image-to-image synthesis, respectively, and also describes several evaluation metrics for synthetic images, including inception scores and Frechet Inception Distance (FID), and explains the significance of the discriminators acting as learned loss functions as opposed to fixed loss functions.", + "Different from the above survey, which has a relatively broad scope in GANs, our objective is heavily focused on text-to-image synthesis. Although this topic, text-to-image synthesis, has indeed been covered in BIBREF6, they did so in a much less detailed fashion, mostly listing the many different works in a time-sequential order. In comparison, we will review several representative methods in the field and outline their models and contributions in detail.", + "Similarly to BIBREF6, the second survey paper BIBREF7 begins with a standard introduction addressing the motivation of image synthesis and the challenges it presents followed by a section dedicated to core concepts of GANs and enhancements to the original GAN model. In addition, the paper covers the review of two types of applications: (1) unconstrained applications of image synthesis such as super-resolution, image inpainting, etc., and (2) constrained image synthesis applications, namely image-to-image, text-to-image, and sketch-to image, and also discusses image and video editing using GANs. Again, the scope of this paper is intrinsically comprehensive, while we focus specifically on text-to-image and go into more detail regarding the contributions of novel state-of-the-art models.", + "Other surveys have been published on related matters, mainly related to the advancements and applications of GANs BIBREF22, BIBREF23, but we have not found any prior works which focus specifically on text-to-image synthesis using GANs. To our knowledge, this is the first paper to do so.", + "black" + ], + [ + "In this section, we first introduce preliminary knowledge of GANs and one of its commonly used variants, conditional GAN (i.e. cGAN), which is the building block for many GAN based text-to-image synthesis models. After that, we briefly separate GAN based text-to-image synthesis into two types, Simple GAN frameworks vs. Advanced GAN frameworks, and discuss why advanced GAN architecture for image synthesis.", + "black Notice that the simple vs. advanced GAN framework separation is rather too brief, our taxonomy in the next section will propose a taxonomy to summarize advanced GAN frameworks into four categories, based on their objective and designs." + ], + [ + "Before moving on to a discussion and analysis of works applying GANs for text-to-image synthesis, there are some preliminary concepts, enhancements of GANs, datasets, and evaluation metrics that are present in some of the works described in the next section and are thus worth introducing.", + "As stated previously, GANs were introduced by Ian Goodfellow et al. BIBREF9 in 2014, and consist of two deep neural networks, a generator and a discriminator, which are trained independently with conflicting goals: The generator aims to generate samples closely related to the original data distribution and fool the discriminator, while the discriminator aims to distinguish between samples from the generator model and samples from the true data distribution by calculating the probability of the sample coming from either source. A conceptual view of the generative adversarial network (GAN) architecture is shown in Figure FIGREF11.", + "The training of GANs is an iterative process that, with each iteration, updates the generator and the discriminator with the goal of each defeating the other. leading each model to become increasingly adept at its specific task until a threshold is reached. This is analogous to a min-max game between the two models, according to the following equation:", + "In Eq. (DISPLAY_FORM10), $x$ denotes a multi-dimensional sample, e.g., an image, and $z$ denotes a multi-dimensional latent space vector, e.g., a multidimensional data point following a predefined distribution function such as that of normal distributions. $D_{\\theta _d}()$ denotes a discriminator function, controlled by parameters $\\theta _d$, which aims to classify a sample into a binary space. $G_{\\theta _g}()$ denotes a generator function, controlled by parameters $\\theta _g$, which aims to generate a sample from some latent space vector. For example, $G_{\\theta _g}(z)$ means using a latent vector $z$ to generate a synthetic/fake image, and $D_{\\theta _d}(x)$ means to classify an image $x$ as binary output (i.e. true/false or 1/0). In the GAN setting, the discriminator $D_{\\theta _d}()$ is learned to distinguish a genuine/true image (labeled as 1) from fake images (labeled as 0). Therefore, given a true image $x$, the ideal output from the discriminator $D_{\\theta _d}(x)$ would be 1. Given a fake image generated from the generator $G_{\\theta _g}(z)$, the ideal prediction from the discriminator $D_{\\theta _d}(G_{\\theta _g}(z))$ would be 0, indicating the sample is a fake image.", + "Following the above definition, the $\\min \\max $ objective function in Eq. (DISPLAY_FORM10) aims to learn parameters for the discriminator ($\\theta _d$) and generator ($\\theta _g$) to reach an optimization goal: The discriminator intends to differentiate true vs. fake images with maximum capability $\\max _{\\theta _d}$ whereas the generator intends to minimize the difference between a fake image vs. a true image $\\min _{\\theta _g}$. In other words, the discriminator sets the characteristics and the generator produces elements, often images, iteratively until it meets the attributes set forth by the discriminator. GANs are often used with images and other visual elements and are notoriously efficient in generating compelling and convincing photorealistic images. Most recently, GANs were used to generate an original painting in an unsupervised fashion BIBREF24. The following sections go into further detail regarding how the generator and discriminator are trained in GANs.", + "Generator - In image synthesis, the generator network can be thought of as a mapping from one representation space (latent space) to another (actual data) BIBREF21. When it comes to image synthesis, all of the images in the data space fall into some distribution in a very complex and high-dimensional feature space. Sampling from such a complex space is very difficult, so GANs instead train a generator to create synthetic images from a much more simple feature space (usually random noise) called the latent space. The generator network performs up-sampling of the latent space and is usually a deep neural network consisting of several convolutional and/or fully connected layers BIBREF21. The generator is trained using gradient descent to update the weights of the generator network with the aim of producing data (in our case, images) that the discriminator classifies as real.", + "Discriminator - The discriminator network can be thought of as a mapping from image data to the probability of the image coming from the real data space, and is also generally a deep neural network consisting of several convolution and/or fully connected layers. However, the discriminator performs down-sampling as opposed to up-sampling. Like the generator, it is trained using gradient descent but its goal is to update the weights so that it is more likely to correctly classify images as real or fake.", + "In GANs, the ideal outcome is for both the generator's and discriminator's cost functions to converge so that the generator produces photo-realistic images that are indistinguishable from real data, and the discriminator at the same time becomes an expert at differentiating between real and synthetic data. This, however, is not possible since a reduction in cost of one model generally leads to an increase in cost of the other. This phenomenon makes training GANs very difficult, and training them simultaneously (both models performing gradient descent in parallel) often leads to a stable orbit where neither model is able to converge. To combat this, the generator and discriminator are often trained independently. In this case, the GAN remains the same, but there are different training stages. In one stage, the weights of the generator are kept constant and gradient descent updates the weights of the discriminator, and in the other stage the weights of the discriminator are kept constant while gradient descent updates the weights of the generator. This is repeated for some number of epochs until a desired low cost for each model is reached BIBREF25." + ], + [ + "Conditional Generative Adversarial Networks (cGAN) are an enhancement of GANs proposed by BIBREF26 shortly after the introduction of GANs by BIBREF9. The objective function of the cGAN is defined in Eq. (DISPLAY_FORM13) which is very similar to the GAN objective function in Eq. (DISPLAY_FORM10) except that the inputs to both discriminator and generator are conditioned by a class label $y$.", + "The main technical innovation of cGAN is that it introduces an additional input or inputs to the original GAN model, allowing the model to be trained on information such as class labels or other conditioning variables as well as the samples themselves, concurrently. Whereas the original GAN was trained only with samples from the data distribution, resulting in the generated sample reflecting the general data distribution, cGAN enables directing the model to generate more tailored outputs.", + "In Figure FIGREF14, the condition vector is the class label (text string) \"Red bird\", which is fed to both the generator and discriminator. It is important, however, that the condition vector is related to the real data. If the model in Figure FIGREF14 was trained with the same set of real data (red birds) but the condition text was \"Yellow fish\", the generator would learn to create images of red birds when conditioned with the text \"Yellow fish\".", + "Note that the condition vector in cGAN can come in many forms, such as texts, not just limited to the class label. Such a unique design provides a direct solution to generate images conditioned by predefined specifications. As a result, cGAN has been used in text-to-image synthesis since the very first day of its invention although modern approaches can deliver much better text-to-image synthesis results.", + "black" + ], + [ + "In order to generate images from text, one simple solution is to employ the conditional GAN (cGAN) designs and add conditions to the training samples, such that the GAN is trained with respect to the underlying conditions. Several pioneer works have followed similar designs for text-to-image synthesis.", + "black An essential disadvantage of using cGAN for text-to-image synthesis is that that it cannot handle complicated textual descriptions for image generation, because cGAN uses labels as conditions to restrict the GAN inputs. If the text inputs have multiple keywords (or long text descriptions) they cannot be used simultaneously to restrict the input. Instead of using text as conditions, another two approaches BIBREF8, BIBREF16 use text as input features, and concatenate such features with other features to train discriminator and generator, as shown in Figure FIGREF15(b) and (c). To ensure text being used as GAN input, a feature embedding or feature representation learning BIBREF29, BIBREF30 function $\\varphi ()$ is often introduced to convert input text as numeric features, which are further concatenated with other features to train GANs.", + "black" + ], + [ + "Motivated by the GAN and conditional GAN (cGAN) design, many GAN based frameworks have been proposed to generate images, with different designs and architectures, such as using multiple discriminators, using progressively trained discriminators, or using hierarchical discriminators. Figure FIGREF17 outlines several advanced GAN frameworks in the literature. In addition to these frameworks, many news designs are being proposed to advance the field with rather sophisticated designs. For example, a recent work BIBREF37 proposes to use a pyramid generator and three independent discriminators, blackeach focusing on a different aspect of the images, to lead the generator towards creating images that are photo-realistic on multiple levels. Another recent publication BIBREF38 proposes to use discriminator to measure semantic relevance between image and text instead of class prediction (like most discriminator in GANs does), resulting a new GAN structure outperforming text conditioned auxiliary classifier (TAC-GAN) BIBREF16 and generating diverse, realistic, and relevant to the input text regardless of class.", + "black In the following section, we will first propose a taxonomy that summarizes advanced GAN frameworks for text-to-image synthesis, and review most recent proposed solutions to the challenge of generating photo-realistic images conditioned on natural language text descriptions using GANs. The solutions we discuss are selected based on relevance and quality of contributions. Many publications exist on the subject of image-generation using GANs, but in this paper we focus specifically on models for text-to-image synthesis, with the review emphasizing on the \u201cmodel\u201d and \u201ccontributions\u201d for text-to-image synthesis. At the end of this section, we also briefly review methods using GANs for other image-synthesis applications.", + "black" + ], + [ + "In this section, we propose a taxonomy to summarize advanced GAN based text-to-image synthesis frameworks, as shown in Figure FIGREF24. The taxonomy organizes GAN frameworks into four categories, including Semantic Enhancement GANs, Resolution Enhancement GANs, Diversity Enhancement GANs, and Motion Enhancement GAGs. Following the proposed taxonomy, each subsection will introduce several typical frameworks and address their techniques of using GANS to solve certain aspects of the text-to-mage synthesis challenges.", + "black" + ], + [ + "Although the ultimate goal of Text-to-Image synthesis is to generate images closely related to the textual descriptions, the relevance of the images to the texts are often validated from different perspectives, due to the inherent diversity of human perceptions. For example, when generating images matching to the description \u201crose flowers\u201d, some users many know the exact type of flowers they like and intend to generate rose flowers with similar colors. Other users, may seek to generate high quality rose flowers with a nice background (e.g. garden). The third group of users may be more interested in generating flowers similar to rose but with different colors and visual appearance, e.g. roses, begonia, and peony. The fourth group of users may want to not only generate flower images, but also use them to form a meaningful action, e.g. a video clip showing flower growth, performing a magic show using those flowers, or telling a love story using the flowers.", + "blackFrom the text-to-Image synthesis point of view, the first group of users intend to precisely control the semantic of the generated images, and their goal is to match the texts and images at the semantic level. The second group of users are more focused on the resolutions and the qualify of the images, in addition to the requirement that the images and texts are semantically related. For the third group of users, their goal is to diversify the output images, such that their images carry diversified visual appearances and are also semantically related. The fourth user group adds a new dimension in image synthesis, and aims to generate sequences of images which are coherent in temporal order, i.e. capture the motion information.", + "black Based on the above descriptions, we categorize GAN based Text-to-Image Synthesis into a taxonomy with four major categories, as shown in Fig. FIGREF24.", + "Semantic Enhancement GANs: Semantic enhancement GANs represent pioneer works of GAN frameworks for text-to-image synthesis. The main focus of the GAN frameworks is to ensure that the generated images are semantically related to the input texts. This objective is mainly achieved by using a neural network to encode texts as dense features, which are further fed to a second network to generate images matching to the texts.", + "Resolution Enhancement GANs: Resolution enhancement GANs mainly focus on generating high qualify images which are semantically matched to the texts. This is mainly achieved through a multi-stage GAN framework, where the outputs from earlier stage GANs are fed to the second (or later) stage GAN to generate better qualify images.", + "Diversity Enhancement GANs: Diversity enhancement GANs intend to diversify the output images, such that the generated images are not only semantically related but also have different types and visual appearance. This objective is mainly achieved through an additional component to estimate semantic relevance between generated images and texts, in order to maximize the output diversity.", + "Motion Enhancement GANs: Motion enhancement GANs intend to add a temporal dimension to the output images, such that they can form meaningful actions with respect to the text descriptions. This goal mainly achieved though a two-step process which first generates images matching to the \u201cactions\u201d of the texts, followed by a mapping or alignment procedure to ensure that images are coherent in the temporal order.", + "black In the following, we will introduce how these GAN frameworks evolve for text-to-image synthesis, and will also review some typical methods of each category.", + "black" + ], + [ + "Semantic relevance is one the of most important criteria of the text-to-image synthesis. For most GNAs discussed in this survey, they are required to generate images semantically related to the text descriptions. However, the semantic relevance is a rather subjective measure, and images are inherently rich in terms of its semantics and interpretations. Therefore, many GANs are further proposed to enhance the text-to-image synthesis from different perspectives. In this subsection, we will review several classical approaches which are commonly served as text-to-image synthesis baseline.", + "black" + ], + [ + "Deep convolution generative adversarial network (DC-GAN) BIBREF8 represents the pioneer work for text-to-image synthesis using GANs. Its main goal is to train a deep convolutional generative adversarial network (DC-GAN) on text features. During this process these text features are encoded by another neural network. This neural network is a hybrid convolutional recurrent network at the character level. Concurrently, both neural networks have also feed-forward inference in the way they condition text features. Generating realistic images automatically from natural language text is the motivation of several of the works proposed in this computer vision field. However, actual artificial intelligence (AI) systems are far from achieving this task BIBREF8, BIBREF39, BIBREF40, BIBREF41, BIBREF42, BIBREF22, BIBREF26. Lately, recurrent neural networks led the way to develop frameworks that learn discriminatively on text features. At the same time, generative adversarial networks (GANs) began recently to show some promise on generating compelling images of a whole host of elements including but not limited to faces, birds, flowers, and non-common images such as room interiorsBIBREF8. DC-GAN is a multimodal learning model that attempts to bridge together both of the above mentioned unsupervised machine learning algorithms, the recurrent neural networks (RNN) and generative adversarial networks (GANs), with the sole purpose of speeding the generation of text-to-image synthesis.", + "black Deep learning shed some light to some of the most sophisticated advances in natural language representation, image synthesis BIBREF7, BIBREF8, BIBREF43, BIBREF35, and classification of generic data BIBREF44. However, a bulk of the latest breakthroughs in deep learning and computer vision were related to supervised learning BIBREF8. Even though natural language and image synthesis were part of several contributions on the supervised side of deep learning, unsupervised learning saw recently a tremendous rise in input from the research community specially on two subproblems: text-based natural language and image synthesis BIBREF45, BIBREF14, BIBREF8, BIBREF46, BIBREF47. These subproblems are typically subdivided as focused research areas. DC-GAN's contributions are mainly driven by these two research areas. In order to generate plausible images from natural language, DC-GAN contributions revolve around developing a straightforward yet effective GAN architecture and training strategy that allows natural text to image synthesis. These contributions are primarily tested on the Caltech-UCSD Birds and Oxford-102 Flowers datasets. Each image in these datasets carry five text descriptions. These text descriptions were created by the research team when setting up the evaluation environment. The DC-GANs model is subsequently trained on several subcategories. Subcategories in this research represent the training and testing sub datasets. The performance shown by these experiments display a promising yet effective way to generate images from textual natural language descriptions BIBREF8.", + "black" + ], + [ + "Following the pioneer DC-GAN framework BIBREF8, many researches propose revised network structures (e.g. different discriminaotrs) in order to improve images with better semantic relevance to the texts. Based on the deep convolutional adversarial network (DC-GAN) network architecture, GAN-CLS with image-text matching discriminator, GAN-INT learned with text manifold interpolation and GAN-INT-CLS which combines both are proposed to find semantic match between text and image. Similar to the DC-GAN architecture, an adaptive loss function (i.e. Perceptual Loss BIBREF48) is proposed for semantic image synthesis which can synthesize a realistic image that not only matches the target text description but also keep the irrelavant features(e.g. background) from source images BIBREF49. Regarding to the Perceptual Losses, three loss functions (i.e. Pixel reconstruction loss, Activation reconstruction loss and Texture reconstruction loss) are proposed in BIBREF50 in which they construct the network architectures based on the DC-GAN, i.e. GAN-INT-CLS-Pixel, GAN-INT-CLS-VGG and GAN-INT-CLS-Gram with respect to three losses. In BIBREF49, a residual transformation unit is added in the network to retain similar structure of the source image.", + "black Following the BIBREF49 and considering the features in early layers address background while foreground is obtained in latter layers in CNN, a pair of discriminators with different architectures (i.e. Paired-D GAN) is proposed to synthesize background and foreground from a source image seperately BIBREF51. Meanwhile, the skip-connection in the generator is employed to more precisely retain background information in the source image.", + "black" + ], + [ + "When synthesising images, most text-to-image synthesis methods consider each output image as one single unit to characterize its semantic relevance to the texts. This is likely problematic because most images naturally consist of two crucial components: foreground and background. Without properly separating these two components, it's hard to characterize the semantics of an image if the whole image is treated as a single unit without proper separation.", + "black In order to enhance the semantic relevance of the images, a multi-conditional GAN (MC-GAN) BIBREF52 is proposed to synthesize a target image by combining the background of a source image and a text-described foreground object which does not exist in the source image. A unique feature of MC-GAN is that it proposes a synthesis block in which the background feature is extracted from the given image without non-linear function (i.e. only using convolution and batch normalization) and the foreground feature is the feature map from the previous layer.", + "black Because MC-GAN is able to properly model the background and foreground of the generated images, a unique strength of MC-GAN is that users are able to provide a base image and MC-GAN is able to preserve the background information of the base image to generate new images. black" + ], + [ + "Due to the fact that training GANs will be much difficult when generating high-resolution images, a two stage GAN (i.e. stackGAN) is proposed in which rough images(i.e. low-resolution images) are generated in stage-I and refined in stage-II. To further improve the quality of generated images, the second version of StackGAN (i.e. Stack++) is proposed to use multi-stage GANs to generate multi-scale images. A color-consistency regularization term is also added into the loss to keep the consistency of images in different scales.", + "black While stackGAN and StackGAN++ are both built on the global sentence vector, AttnGAN is proposed to use attention mechanism (i.e. Deep Attentional Multimodal Similarity Model (DAMSM)) to model the multi-level information (i.e. word level and sentence level) into GANs. In the following, StackGAN, StackGAN++ and AttnGAN will be explained in detail.", + "black Recently, Dynamic Memory Generative Adversarial Network (i.e. DM-GAN)BIBREF53 which uses a dynamic memory component is proposed to focus on refiningthe initial generated image which is the key to the success of generating high quality images." + ], + [ + "In 2017, Zhang et al. proposed a model for generating photo-realistic images from text descriptions called StackGAN (Stacked Generative Adversarial Network) BIBREF33. In their work, they define a two-stage model that uses two cascaded GANs, each corresponding to one of the stages. The stage I GAN takes a text description as input, converts the text description to a text embedding containing several conditioning variables, and generates a low-quality 64x64 image with rough shapes and colors based on the computed conditioning variables. The stage II GAN then takes this low-quality stage I image as well as the same text embedding and uses the conditioning variables to correct and add more detail to the stage I result. The output of stage II is a photorealistic 256$times$256 image that resembles the text description with compelling accuracy.", + "One major contribution of StackGAN is the use of cascaded GANs for text-to-image synthesis through a sketch-refinement process. By conditioning the stage II GAN on the image produced by the stage I GAN and text description, the stage II GAN is able to correct defects in the stage I output, resulting in high-quality 256x256 images. Prior works have utilized \u201cstacked\u201d GANs to separate the image generation process into structure and style BIBREF42, multiple stages each generating lower-level representations from higher-level representations of the previous stage BIBREF35, and multiple stages combined with a laplacian pyramid approach BIBREF54, which was introduced for image compression by P. Burt and E. Adelson in 1983 and uses the differences between consecutive down-samples of an original image to reconstruct the original image from its down-sampled version BIBREF55. However, these works did not use text descriptions to condition their generator models.", + "Conditioning Augmentation is the other major contribution of StackGAN. Prior works transformed the natural language text description into a fixed text embedding containing static conditioning variables which were fed to the generator BIBREF8. StackGAN does this and then creates a Gaussian distribution from the text embedding and randomly selects variables from the Gaussian distribution to add to the set of conditioning variables during training. This encourages robustness by introducing small variations to the original text embedding for a particular training image while keeping the training image that the generated output is compared to the same. The result is that the trained model produces more diverse images in the same distribution when using Conditioning Augmentation than the same model using a fixed text embedding BIBREF33." + ], + [ + "Proposed by the same users as StackGAN, StackGAN++ is also a stacked GAN model, but organizes the generators and discriminators in a \u201ctree-like\u201d structure BIBREF47 with multiple stages. The first stage combines a noise vector and conditioning variables (with Conditional Augmentation introduced in BIBREF33) for input to the first generator, which generates a low-resolution image, 64$\\times $64 by default (this can be changed depending on the desired number of stages). Each following stage uses the result from the previous stage and the conditioning variables to produce gradually higher-resolution images. These stages do not use the noise vector again, as the creators assume that the randomness it introduces is already preserved in the output of the first stage. The final stage produces a 256$\\times $256 high-quality image.", + "StackGAN++ introduces the joint conditional and unconditional approximation in their designs BIBREF47. The discriminators are trained to calculate the loss between the image produced by the generator and the conditioning variables (measuring how accurately the image represents the description) as well as the loss between the image and real images (probability of the image being real or fake). The generators then aim to minimize the sum of these losses, improving the final result." + ], + [ + "Attentional Generative Adversarial Network (AttnGAN) BIBREF10 is very similar, in terms of its structure, to StackGAN++ BIBREF47, discussed in the previous section, but some novel components are added. Like previous works BIBREF56, BIBREF8, BIBREF33, BIBREF47, a text encoder generates a text embedding with conditioning variables based on the overall sentence. Additionally, the text encoder generates a separate text embedding with conditioning variables based on individual words. This process is optimized to produce meaningful variables using a bidirectional recurrent neural network (BRNN), more specifically bidirectional Long Short Term Memory (LSTM) BIBREF57, which, for each word in the description, generates conditions based on the previous word as well as the next word (bidirectional). The first stage of AttnGAN generates a low-resolution image based on the sentence-level text embedding and random noise vector. The output is fed along with the word-level text embedding to an \u201cattention model\u201d, which matches the word-level conditioning variables to regions of the stage I image, producing a word-context matrix. This is then fed to the next stage of the model along with the raw previous stage output. Each consecutive stage works in the same manner, but produces gradually higher-resolution images conditioned on the previous stage.", + "Two major contributions were introduced in AttnGAN: the attentional generative network and the Deep Attentional Multimodal Similarity Model (DAMSM) BIBREF47. The attentional generative network matches specific regions of each stage's output image to conditioning variables from the word-level text embedding. This is a very worthy contribution, allowing each consecutive stage to focus on specific regions of the image independently, adding \u201cattentional\u201d details region by region as opposed to the whole image. The DAMSM is also a key feature introduced by AttnGAN, which is used after the result of the final stage to calculate the similarity between the generated image and the text embedding at both the sentence level and the more fine-grained word level. Table TABREF48 shows scores from different metrics for StackGAN, StackGAN++, AttnGAN, and HDGAN on the CUB, Oxford, and COCO datasets. The table shows that AttnGAN outperforms the other models in terms of IS on the CUB dataset by a small amount and greatly outperforms them on the COCO dataset." + ], + [ + "Hierarchically-nested adversarial network (HDGAN) is a method proposed by BIBREF36, and its main objective is to tackle the difficult problem of dealing with photographic images from semantic text descriptions. These semantic text descriptions are applied on images from diverse datasets. This method introduces adversarial objectives nested inside hierarchically oriented networks BIBREF36. Hierarchical networks helps regularize mid-level manifestations. In addition to regularize mid-level manifestations, it assists the training of the generator in order to capture highly complex still media elements. These elements are captured in statistical order to train the generator based on settings extracted directly from the image. The latter is an ideal scenario. However, this paper aims to incorporate a single-stream architecture. This single-stream architecture functions as the generator that will form an optimum adaptability towards the jointed discriminators. Once jointed discriminators are setup in an optimum manner, the single-stream architecture will then advance generated images to achieve a much higher resolution BIBREF36.", + "The main contributions of the HDGANs include the introduction of a visual-semantic similarity measure BIBREF36. This feature will aid in the evaluation of the consistency of generated images. In addition to checking the consistency of generated images, one of the key objectives of this step is to test the logical consistency of the end product BIBREF36. The end product in this case would be images that are semantically mapped from text-based natural language descriptions to each area on the picture e.g. a wing on a bird or petal on a flower. Deep learning has created a multitude of opportunities and challenges for researchers in the computer vision AI field. Coupled with GAN and multimodal learning architectures, this field has seen tremendous growth BIBREF8, BIBREF39, BIBREF40, BIBREF41, BIBREF42, BIBREF22, BIBREF26. Based on these advancements, HDGANs attempt to further extend some desirable and less common features when generating images from textual natural language BIBREF36. In other words, it takes sentences and treats them as a hierarchical structure. This has some positive and negative implications in most cases. For starters, it makes it more complex to generate compelling images. However, one of the key benefits of this elaborate process is the realism obtained once all processes are completed. In addition, one common feature added to this process is the ability to identify parts of sentences with bounding boxes. If a sentence includes common characteristics of a bird, it will surround the attributes of such bird with bounding boxes. In practice, this should happen if the desired image have other elements such as human faces (e.g. eyes, hair, etc), flowers (e.g. petal size, color, etc), or any other inanimate object (e.g. a table, a mug, etc). Finally, HDGANs evaluated some of its claims on common ideal text-to-image datasets such as CUB, COCO, and Oxford-102 BIBREF8, BIBREF36, BIBREF39, BIBREF40, BIBREF41, BIBREF42, BIBREF22, BIBREF26. These datasets were first utilized on earlier works BIBREF8, and most of them sport modified features such image annotations, labels, or descriptions. The qualitative and quantitative results reported by researchers in this study were far superior of earlier works in this same field of computer vision AI.", + "black" + ], + [ + "In this subsection, we introduce text-to-image synthesis methods which try to maximize the diversity of the output images, based on the text descriptions.", + "black" + ], + [ + "Two issues arise in the traditional GANs BIBREF58 for image synthesis: (1) scalabilirty problem: traditional GANs cannot predict a large number of image categories; and (2) diversity problem: images are often subject to one-to-many mapping, so one image could be labeled as different tags or being described using different texts. To address these problems, GAN conditioned on additional information, e.g. cGAN, is an alternative solution. However, although cGAN and many previously introduced approaches are able to generate images with respect to the text descriptions, they often output images with similar types and visual appearance.", + "black Slightly different from the cGAN, auxiliary classifier GANs (AC-GAN) BIBREF27 proposes to improve the diversity of output images by using an auxiliary classifier to control output images. The overall structure of AC-GAN is shown in Fig. FIGREF15(c). In AC-GAN, every generated image is associated with a class label, in addition to the true/fake label which are commonly used in GAN or cGAN. The discriminator of AC-GAN not only outputs a probability distribution over sources (i.e. whether the image is true or fake), it also output a probability distribution over the class label (i.e. predict which class the image belong to).", + "black By using an auxiliary classifier layer to predict the class of the image, AC-GAN is able to use the predicted class labels of the images to ensure that the output consists of images from different classes, resulting in diversified synthesis images. The results show that AC-GAN can generate images with high diversity.", + "black" + ], + [ + "Building on the AC-GAN, TAC-GAN BIBREF59 is proposed to replace the class information with textual descriptions as the input to perform the task of text to image synthesis. The architecture of TAC-GAN is shown in Fig. FIGREF15(d), which is similar to AC-GAN. Overall, the major difference between TAC-GAN and AC-GAN is that TAC-GAN conditions the generated images on text descriptions instead of on a class label. This design makes TAC-GAN more generic for image synthesis.", + "black For TAC-GAN, it imposes restrictions on generated images in both texts and class labels. The input vector of TAC-GAN's generative network is built based on a noise vector and embedded vector representation of textual descriptions. The discriminator of TAC-GAN is similar to that of the AC-GAN, which not only predicts whether the image is fake or not, but also predicts the label of the images. A minor difference of TAC-GAN's discriminator, compared to that of the AC-GAN, is that it also receives text information as input before performing its classification.", + "black The experiments and validations, on the Oxford-102 flowers dataset, show that the results produced by TAC-GAN are \u201cslightly better\u201d that other approaches, including GAN-INT-CLS and StackGAN.", + "black" + ], + [ + "In order to improve the diversity of the output images, both AC-GAN and TAC-GAN's discriminators predict class labels of the synthesised images. This process likely enforces the semantic diversity of the images, but class labels are inherently restrictive in describing image semantics, and images described by text can be matched to multiple labels. Therefore, instead of predicting images' class labels, an alternative solution is to directly quantify their semantic relevance.", + "black The architecture of Text-SeGAN is shown in Fig. FIGREF15(e). In order to directly quantify semantic relevance, Text-SeGAN BIBREF28 adds a regression layer to estimate the semantic relevance between the image and text instead of a classifier layer of predicting labels. The estimated semantic reference is a fractional value ranging between 0 and 1, with a higher value reflecting better semantic relevance between the image and text. Due to this unique design, an inherent advantage of Text-SeGAN is that the generated images are not limited to certain classes and are semantically matching to the text input.", + "black Experiments and validations, on Oxford-102 flower dataset, show that Text-SeGAN can generate diverse images that are semantically relevant to the input text. In addition, the results of Text-SeGAN show improved inception score compared to other approaches, including GAN-INT-CLS, StackGAN, TAC-GAN, and HDGAN.", + "black" + ], + [ + "Due to the inherent complexity of the visual images, and the diversity of text descriptions (i.e. same words could imply different meanings), it is difficulty to precisely match the texts to the visual images at the semantic levels. For most methods we have discussed so far, they employ a direct text to image generation process, but there is no validation about how generated images comply with the text in a reverse fashion.", + "black To ensure the semantic consistency and diversity, MirrorGAN BIBREF60 employs a mirror structure, which reversely learns from generated images to output texts (an image-to-text process) to further validate whether generated are indeed consistent to the input texts. MirrowGAN includes three modules: a semantic text embedding module (STEM), a global-local collaborative attentive module for cascaded image generation (GLAM), and a semantic text regeneration and alignment module (STREAM). The back to back Text-to-Image (T2I) and Image-to-Text (I2T) are combined to progressively enhance the diversity and semantic consistency of the generated images.", + "black In order to enhance the diversity of the output image, Scene Graph GAN BIBREF61 proposes to use visual scene graphs to describe the layout of the objects, allowing users to precisely specific the relationships between objects in the images. In order to convert the visual scene graph as input for GAN to generate images, this method uses graph convolution to process input graphs. It computes a scene layout by predicting bounding boxes and segmentation masks for objects. After that, it converts the computed layout to an image with a cascaded re\ufb01nement network.", + "black" + ], + [ + "Instead of focusing on generating static images, another line of text-to-image synthesis research focuses on generating videos (i.e. sequences of images) from texts. In this context, the synthesised videos are often useful resources for automated assistance or story telling.", + "black" + ], + [ + "One early/interesting work of motion enhancement GANs is to generate spoofed speech and lip-sync videos (or talking face) of Barack Obama (i.e. ObamaNet) based on text input BIBREF62. This framework is consisted of three parts, i.e. text to speech using \u201cChar2Wav\u201d, mouth shape representation synced to the audio using a time-delayed LSTM and \u201cvideo generation\u201d conditioned on the mouth shape using \u201cU-Net\u201d architecture. Although the results seem promising, ObamaNet only models the mouth region and the videos are not generated from noise which can be regarded as video prediction other than video generation.", + "black Another meaningful trial of using synthesised videos for automated assistance is to translate spoken language (e.g. text) into sign language video sequences (i.e. T2S) BIBREF63. This is often achieved through a two step process: converting texts as meaningful units to generate images, followed by a learning component to arrange images into sequential order for best representation. More specifically, using RNN based machine translation methods, texts are translated into sign language gloss sequences. Then, glosses are mapped to skeletal pose sequences using a lookup-table. To generate videos, a conditional DCGAN with the input of concatenation of latent representation of the image for a base pose and skeletal pose information is built.", + "black" + ], + [ + "In BIBREF64, a text-to-video model (T2V) is proposed based on the cGAN in which the input is the isometric Gaussian noise with the text-gist vector served as the generator. A key component of generating videos from text is to train a conditional generative model to extract both static and dynamic information from text, followed by a hybrid framework combining a Variational Autoencoder (VAE) and a Generative Adversarial Network (GAN).", + "black More specifically, T2V relies on two types of features, static features and dynamic features, to generate videos. Static features, called \u201cgist\u201d are used to sketch text-conditioned background color and object layout structure. Dynamic features, on the other hand, are considered by transforming input text into an image filter which eventually forms the video generator which consists of three entangled neural networks. The text-gist vector is generated by a gist generator which maintains static information (e.g. background) and a text2filter which captures the dynamic information (i.e. actions) in the text to generate videos.", + "black As demonstrated in the paper BIBREF64, the generated videos are semantically related to the texts, but have a rather low quality (e.g. only $64 \\times 64$ resolution).", + "black" + ], + [ + "Different from T2V which generates videos from a single text, StoryGAN aims to produce dynamic scenes consistent of specified texts (i.e. story written in a multi-sentence paragraph) using a sequential GAN model BIBREF65. Story encoder, context encoder, and discriminators are the main components of this model. By using stochastic sampling, the story encoder intends to learn an low-dimensional embedding vector for the whole story to keep the continuity of the story. The context encoder is proposed to capture contextual information during sequential image generation based on a deep RNN. Two discriminators of StoryGAN are image discriminator which evaluates the generated images and story discriminator which ensures the global consistency.", + "black The experiments and comparisons, on CLEVR dataset and Pororo cartoon dataset which are originally used for visual question answering, show that StoryGAN improves the generated video qualify in terms of Structural Similarity Index (SSIM), visual qualify, consistence, and relevance (the last three measure are based on human evaluation)." + ], + [ + "Computer vision applications have strong potential for industries including but not limited to the medical, government, military, entertainment, and online social media fields BIBREF7, BIBREF66, BIBREF67, BIBREF68, BIBREF69, BIBREF70. Text-to-image synthesis is one such application in computer vision AI that has become the main focus in recent years due to its potential for providing beneficial properties and opportunities for a wide range of applicable areas.", + "Text-to-image synthesis is an application byproduct of deep convolutional decoder networks in combination with GANs BIBREF7, BIBREF8, BIBREF10. Deep convolutional networks have contributed to several breakthroughs in image, video, speech, and audio processing. This learning method intends, among other possibilities, to help translate sequential text descriptions to images supplemented by one or many additional methods. Algorithms and methods developed in the computer vision field have allowed researchers in recent years to create realistic images from plain sentences. Advances in the computer vision, deep convolutional nets, and semantic units have shined light and redirected focus to this research area of text-to-image synthesis, having as its prime directive: to aid in the generation of compelling images with as much fidelity to text descriptions as possible.", + "To date, models for generating synthetic images from textual natural language in research laboratories at universities and private companies have yielded compelling images of flowers and birds BIBREF8. Though flowers and birds are the most common objects studied thus far, research has been applied to other classes as well. For example, there have been studies focused solely on human faces BIBREF7, BIBREF8, BIBREF71, BIBREF72.", + "It\u2019s a fascinating time for computer vision AI and deep learning researchers and enthusiasts. The consistent advancement in hardware, software, and contemporaneous development of computer vision AI research disrupts multiple industries. These advances in technology allow for the extraction of several data types from a variety of sources. For example, image data captured from a variety of photo-ready devices, such as smart-phones, and online social media services opened the door to the analysis of large amounts of media datasets BIBREF70. The availability of large media datasets allow new frameworks and algorithms to be proposed and tested on real-world data." + ], + [ + "A summary of some reviewed methods and benchmark datasets used for validation is reported in Table TABREF43. In addition, the performance of different GANs with respect to the benchmark datasets and performance metrics is reported in Table TABREF48.", + "In order to synthesize images from text descriptions, many frameworks have taken a minimalistic approach by creating small and background-less images BIBREF73. In most cases, the experiments were conducted on simple datasets, initially containing images of birds and flowers. BIBREF8 contributed to these data sets by adding corresponding natural language text descriptions to subsets of the CUB, MSCOCO, and Oxford-102 datasets, which facilitated the work on text-to-image synthesis for several papers released more recently.", + "While most deep learning algorithms use MNIST BIBREF74 dataset as the benchmark, there are three main datasets that are commonly used for evaluation of proposed GAN models for text-to-image synthesis: CUB BIBREF75, Oxford BIBREF76, COCO BIBREF77, and CIFAR-10 BIBREF78. CUB BIBREF75 contains 200 birds with matching text descriptions and Oxford BIBREF76 contains 102 categories of flowers with 40-258 images each and matching text descriptions. These datasets contain individual objects, with the text description corresponding to that object, making them relatively simple. COCO BIBREF77 is much more complex, containing 328k images with 91 different object types. CIFAI-10 BIBREF78 dataset consists of 60000 32$times$32 colour images in 10 classes, with 6000 images per class. In contrast to CUB and Oxford, whose images each contain an individual object, COCO\u2019s images may contain multiple objects, each with a label, so there are many labels per image. The total number of labels over the 328k images is 2.5 million BIBREF77." + ], + [ + "Several evaluation metrics are used for judging the images produced by text-to-image GANs. Proposed by BIBREF25, Inception Scores (IS) calculates the entropy (randomness) of the conditional distribution, obtained by applying the Inception Model introduced in BIBREF79, and marginal distribution of a large set of generated images, which should be low and high, respectively, for meaningful images. Low entropy of conditional distribution means that the evaluator is confident that the images came from the data distribution, and high entropy of the marginal distribution means that the set of generated images is diverse, which are both desired features. The IS score is then computed as the KL-divergence between the two entropies. FCN-scores BIBREF2 are computed in a similar manner, relying on the intuition that realistic images generated by a GAN should be able to be classified correctly by a classifier trained on real images of the same distribution. Therefore, if the FCN classifier classifies a set of synthetic images accurately, the image is probably realistic, and the corresponding GAN gets a high FCN score. Frechet Inception Distance (FID) BIBREF80 is the other commonly used evaluation metric, and takes a different approach, actually comparing the generated images to real images in the distribution. A high FID means there is little relationship between statistics of the synthetic and real images and vice versa, so lower FIDs are better.", + "black The performance of different GANs with respect to the benchmark datasets and performance metrics is reported in Table TABREF48. In addition, Figure FIGREF49 further lists the performance of 14 GANs with respect to their Inception Scores (IS)." + ], + [ + "While we gathered all the data we could find on scores for each model on the CUB, Oxford, and COCO datasets using IS, FID, FCN, and human classifiers, we unfortunately were unable to find certain data for AttnGAN and HDGAN (missing in Table TABREF48). The best evaluation we can give for those with missing data is our own opinions by looking at examples of generated images provided in their papers. In this regard, we observed that HDGAN produced relatively better visual results on the CUB and Oxford datasets while AttnGAN produced far more impressive results than the rest on the more complex COCO dataset. This is evidence that the attentional model and DAMSM introduced by AttnGAN are very effective in producing high-quality images. Examples of the best results of birds and plates of vegetables generated by each model are presented in Figures FIGREF50 and FIGREF51, respectively.", + "blackIn terms of inception score (IS), which is the metric that was applied to majority models except DC-GAN, the results in Table TABREF48 show that StackGAN++ only showed slight improvement over its predecessor, StackGAN, for text-to-image synthesis. However, StackGAN++ did introduce a very worthy enhancement for unconditional image generation by organizing the generators and discriminators in a \u201ctree-like\u201d structure. This indicates that revising the structures of the discriminators and/or generators can bring a moderate level of improvement in text-to-image synthesis.", + "blackIn addition, the results in Table TABREF48 also show that DM-GAN BIBREF53 has the best performance, followed by Obj-GAN BIBREF81. Notice that both DM-GAN and Obj-GAN are most recently developed methods in the field (both published in 2019), indicating that research in text to image synthesis is continuously improving the results for better visual perception and interception. Technical wise, DM-GAN BIBREF53 is a model using dynamic memory to refine fuzzy image contents initially generated from the GAN networks. A memory writing gate is used for DM-GAN to select important text information and generate images based on he selected text accordingly. On the other hand, Obj-GAN BIBREF81 focuses on object centered text-to-image synthesis. The proposed framework of Obj-GAN consists of a layout generation, including a bounding box generator and a shape generator, and an object-driven attentive image generator. The designs and advancement of DM-GAN and Obj-GAN indicate that research in text-to-image synthesis is advancing to put more emphasis on the image details and text semantics for better understanding and perception." + ], + [ + "It is worth noting that although this survey mainly focuses on text-to-image synthesis, there have been other applications of GANs in broader image synthesis field that we found fascinating and worth dedicating a small section to. For example, BIBREF72 used Sem-Latent GANs to generate images of faces based on facial attributes, producing impressive results that, at a glance, could be mistaken for real faces. BIBREF82, BIBREF70, and BIBREF83 demonstrated great success in generating text descriptions from images (image captioning) with great accuracy, with BIBREF82 using an attention-based model that automatically learns to focus on salient objects and BIBREF83 using deep visual-semantic alignments. Finally, there is a contribution made by StackGAN++ that was not mentioned in the dedicated section due to its relation to unconditional image generation as opposed to conditional, namely a color-regularization term BIBREF47. This additional term aims to keep the samples generated from the same input at different stages more consistent in color, which resulted in significantly better results for the unconditional model." + ], + [ + "The recent advancement in text-to-image synthesis research opens the door to several compelling methods and architectures. The main objective of text-to-image synthesis initially was to create images from simple labels, and this objective later scaled to natural languages. In this paper, we reviewed novel methods that generate, in our opinion, the most visually-rich and photo-realistic images, from text-based natural language. These generated images often rely on generative adversarial networks (GANs), deep convolutional decoder networks, and multimodal learning methods.", + "blackIn the paper, we first proposed a taxonomy to organize GAN based text-to-image synthesis frameworks into four major groups: semantic enhancement GANs, resolution enhancement GANs, diversity enhancement GANs, and motion enhancement GANs. The taxonomy provides a clear roadmap to show the motivations, architectures, and difference of different methods, and also outlines their evolution timeline and relationships. Following the proposed taxonomy, we reviewed important features of each method and their architectures. We indicated the model definition and key contributions from some advanced GAN framworks, including StackGAN, StackGAN++, AttnGAN, DC-GAN, AC-GAN, TAC-GAN, HDGAN, Text-SeGAn, StoryGAN etc. Many of the solutions surveyed in this paper tackled the highly complex challenge of generating photo-realistic images beyond swatch size samples. In other words, beyond the work of BIBREF8 in which images were generated from text in 64$\\times $64 tiny swatches. Lastly, all methods were evaluated on datasets that included birds, flowers, humans, and other miscellaneous elements. We were also able to allocate some important papers that were as impressive as the papers we finally surveyed. Though, these notable papers have yet to contribute directly or indirectly to the expansion of the vast computer vision AI field. Looking into the future, an excellent extension from the works surveyed in this paper would be to give more independence to the several learning methods (e.g. less human intervention) involved in the studies as well as increasing the size of the output images." + ], + [ + "The authors declare that there is no conflict of interest regarding the publication of this article." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0243/instruction.md b/qasper-0243/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..add9a71ac25c5f6552de762a71aa413d7ebc0bfa --- /dev/null +++ b/qasper-0243/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Learning to Rank Scientific Documents from the Crowd + +Question: what crowdsourcing platform is used? \ No newline at end of file diff --git a/qasper-0244/instruction.md b/qasper-0244/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..9567c06f041b8c90e6823026f4e93a363f4fe33c --- /dev/null +++ b/qasper-0244/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Exploiting Deep Learning for Persian Sentiment Analysis + +Question: Which deep learning model performed better? \ No newline at end of file diff --git a/qasper-0272/instruction.md b/qasper-0272/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..e14ab4e3a440d0977bb591c264d72ccd2b6d1452 --- /dev/null +++ b/qasper-0272/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Learning High-order Structural and Attribute information by Knowledge Graph Attention Networks for Enhancing Knowledge Graph Embedding + +Question: How does KANE capture both high-order structural and attribute information of KGs in an efficient, explicit and unified manner? \ No newline at end of file diff --git a/qasper-0275/instruction.md b/qasper-0275/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..9226d0bc9af6ad8adac3fb19e99a12c638f0c43e --- /dev/null +++ b/qasper-0275/instruction.md @@ -0,0 +1,64 @@ +Name of Paper: A Computational Approach to Automatic Prediction of Drunk Texting + +Question: Do the authors mention any confounds to their study? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Motivation", + "Definition and Challenges", + "Dataset Creation", + "Feature Design", + "Evaluation", + "Performance for Datasets 1 and 2", + "Performance for Held-out Dataset H", + "Error Analysis", + "Conclusion & Future Work" + ], + "paragraphs": [ + [ + "The ubiquity of communication devices has made social media highly accessible. The content on these media reflects a user's day-to-day activities. This includes content created under the influence of alcohol. In popular culture, this has been referred to as `drunk-texting'. In this paper, we introduce automatic `drunk-texting prediction' as a computational task. Given a tweet, the goal is to automatically identify if it was written by a drunk user. We refer to tweets written under the influence of alcohol as `drunk tweets', and the opposite as `sober tweets'.", + "A key challenge is to obtain an annotated dataset. We use hashtag-based supervision so that the authors of the tweets mention if they were drunk at the time of posting a tweet. We create three datasets by using different strategies that are related to the use of hashtags. We then present SVM-based classifiers that use N-gram and stylistic features such as capitalisation, spelling errors, etc. Through our experiments, we make subtle points related to: (a) the performance of our features, (b) how our approach compares against human ability to detect drunk-texting, (c) most discriminative stylistic features, and (d) an error analysis that points to future work. To the best of our knowledge, this is a first study that shows the feasibility of text-based analysis for drunk-texting prediction." + ], + [ + "Past studies show the relation between alcohol abuse and unsociable behaviour such as aggression BIBREF0 , crime BIBREF1 , suicide attempts BIBREF2 , drunk driving BIBREF3 , and risky sexual behaviour BIBREF4 . suicide state that \u201cthose responsible for assessing cases of attempted suicide should be adept at detecting alcohol misuse\u201d. Thus, a drunk-texting prediction system can be used to identify individuals susceptible to these behaviours, or for investigative purposes after an incident.", + "Drunk-texting may also cause regret. Mail Goggles prompts a user to solve math questions before sending an email on weekend evenings. Some Android applications avoid drunk-texting by blocking outgoing texts at the click of a button. However, to the best of our knowledge, these tools require a user command to begin blocking. An ongoing text-based analysis will be more helpful, especially since it offers a more natural setting by monitoring stream of social media text and not explicitly seeking user input. Thus, automatic drunk-texting prediction will improve systems aimed to avoid regrettable drunk-texting. To the best of our knowledge, ours is the first study that does a quantitative analysis, in terms of prediction of the drunk state by using textual clues.", + "Several studies have studied linguistic traits associated with emotion expression and mental health issues, suicidal nature, criminal status, etc. BIBREF5 , BIBREF6 . NLP techniques have been used in the past to address social safety and mental health issues BIBREF7 ." + ], + [ + "Drunk-texting prediction is the task of classifying a text as drunk or sober. For example, a tweet `Feeling buzzed. Can't remember how the evening went' must be predicted as `drunk', whereas, `Returned from work late today, the traffic was bad' must be predicted as `sober'. The challenges are:" + ], + [ + "We use hashtag-based supervision to create our datasets, similar to tasks like emotion classification BIBREF8 . The tweets are downloaded using Twitter API (https://dev.twitter.com/). We remove non-Unicode characters, and eliminate tweets that contain hyperlinks and also tweets that are shorter than 6 words in length. Finally, hashtags used to indicate drunk or sober tweets are removed so that they provide labels, but do not act as features. The dataset is available on request. As a result, we create three datasets, each using a different strategy for sober tweets, as follows:", + "The drunk tweets for Datasets 1 and 2 are the same. Figure FIGREF9 shows a word-cloud for these drunk tweets (with stop words and forms of the word `drunk' removed), created using WordItOut. The size of a word indicates its frequency. In addition to topical words such as `bar', `bottle' and `wine', the word-cloud shows sentiment words such as `love' or `damn', along with profane words.", + "Heuristics other than these hashtags could have been used for dataset creation. For example, timestamps were a good option to account for time at which a tweet was posted. However, this could not be used because user's local times was not available, since very few users had geolocation enabled." + ], + [ + "The complete set of features is shown in Table TABREF7 . There are two sets of features: (a) N-gram features, and (b) Stylistic features. We use unigrams and bigrams as N-gram features- considering both presence and count.", + "Table TABREF7 shows the complete set of stylistic features of our prediction system. POS ratios are a set of features that record the proportion of each POS tag in the dataset (for example, the proportion of nouns/adjectives, etc.). The POS tags and named entity mentions are obtained from NLTK BIBREF9 . Discourse connectors are identified based on a manually created list. Spelling errors are identified using a spell checker by enchant. The repeated characters feature captures a situation in which a word contains a letter that is repeated three or more times, as in the case of happpy. Since drunk-texting is often associated with emotional expression, we also incorporate a set of sentiment-based features. These features include: count/presence of emoticons and sentiment ratio. Sentiment ratio is the proportion of positive and negative words in the tweet. To determine positive and negative words, we use the sentiment lexicon in mpqa. To identify a more refined set of words that correspond to the two classes, we also estimated 20 topics for the dataset by estimating an LDA model BIBREF10 . We then consider top 10 words per topic, for both classes. This results in 400 LDA-specific unigrams that are then used as features." + ], + [ + "Using the two sets of features, we train SVM classifiers BIBREF11 . We show the five-fold cross-validation performance of our features on Datasets 1 and 2, in Section SECREF17 , and on Dataset H in Section SECREF21 . Section SECREF22 presents an error analysis. Accuracy, positive/negative precision and positive/negative recall are shown as A, PP/NP and PR/NR respectively. `Drunk' forms the positive class, while `Sober' forms the negative class." + ], + [ + "Table TABREF14 shows the performance for five-fold cross-validation for Datasets 1 and 2. In case of Dataset 1, we observe that N-gram features achieve an accuracy of 85.5%. We see that our stylistic features alone exhibit degraded performance, with an accuracy of 75.6%, in the case of Dataset 1. Table TABREF16 shows top stylistic features, when trained on the two datasets. Spelling errors, POS ratios for nouns (POS_NOUN), length and sentiment ratios appear in both lists, in addition to LDA-based unigrams. However, negative recall reduces to a mere 3.2%. This degradation implies that our features capture a subset of drunk tweets and that there are properties of drunk tweets that may be more subtle. When both N-gram and stylistic features are used, there is negligible improvement. The accuracy for Dataset 2 increases from 77.9% to 78.1%. Precision/Recall metrics do not change significantly either. The best accuracy of our classifier is 78.1% for all features, and 75.6% for stylistic features. This shows that text-based clues can indeed be used for drunk-texting prediction." + ], + [ + "Using held-out dataset H, we evaluate how our system performs in comparison to humans. Three annotators, A1-A3, mark each tweet in the Dataset H as drunk or sober. Table TABREF19 shows a moderate agreement between our annotators (for example, it is 0.42 for A1 and A2). Table TABREF20 compares our classifier with humans. Our human annotators perform the task with an average accuracy of 68.8%, while our classifier (with all features) trained on Dataset 2 reaches 64%. The classifier trained on Dataset 2 is better than which is trained on Dataset 1." + ], + [ + "Some categories of errors that occur are:", + "Incorrect hashtag supervision: The tweet `Can't believe I lost my bag last night, literally had everything in! Thanks god the bar man found it' was marked with`#Drunk'. However, this tweet is not likely to be a drunk tweet, but describes a drunk episode in retrospective. Our classifier predicts it as sober.", + "Seemingly sober tweets: Human annotators as well as our classifier could not identify whether `Will you take her on a date? But really she does like you' was drunk, although the author of the tweet had marked it so. This example also highlights the difficulty of drunk-texting prediction.", + "Pragmatic difficulty: The tweet `National dress of Ireland is one's one vomit.. my family is lovely' was correctly identified by our human annotators as a drunk tweet. This tweet contains an element of humour and topic change, but our classifier could not capture it." + ], + [ + "In this paper, we introduce automatic drunk-texting prediction as the task of predicting a tweet as drunk or sober. First, we justify the need for drunk-texting prediction as means of identifying risky social behavior arising out of alcohol abuse, and the need to build tools that avoid privacy leaks due to drunk-texting. We then highlight the challenges of drunk-texting prediction: one of the challenges is selection of negative examples (sober tweets). Using hashtag-based supervision, we create three datasets annotated with drunk or sober labels. We then present SVM-based classifiers which use two sets of features: N-gram and stylistic features. Our drunk prediction system obtains a best accuracy of 78.1%. We observe that our stylistic features add negligible value to N-gram features. We use our heldout dataset to compare how our system performs against human annotators. While human annotators achieve an accuracy of 68.8%, our system reaches reasonably close and performs with a best accuracy of 64%.", + "Our analysis of the task and experimental findings make a case for drunk-texting prediction as a useful and feasible NLP application." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0281/instruction.md b/qasper-0281/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..3c9b7add046874745a9d3b31f63ec59addc58f14 --- /dev/null +++ b/qasper-0281/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Answering Complex Questions Using Open Information Extraction + +Question: What corpus was the source of the OpenIE extractions? \ No newline at end of file diff --git a/qasper-0286/instruction.md b/qasper-0286/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..efa520222857b4ecd643e74dfc6607505fc424b1 --- /dev/null +++ b/qasper-0286/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Answering Complex Questions Using Open Information Extraction + +Question: Can the method answer multi-hop questions? \ No newline at end of file diff --git a/qasper-0288/instruction.md b/qasper-0288/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..ee91001aa9f9c5b6c5f7118650283eeb953b0112 --- /dev/null +++ b/qasper-0288/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Answering Complex Questions Using Open Information Extraction + +Question: What OpenIE method was used to generate the extractions? \ No newline at end of file diff --git a/qasper-0300/instruction.md b/qasper-0300/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..96f6be04cacee11faf1131d1f920d074c7d894d4 --- /dev/null +++ b/qasper-0300/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Recurrent Neural Network Encoder with Attention for Community Question Answering + +Question: How much performance gap between their approach and the strong handcrafted method? \ No newline at end of file diff --git a/qasper-0301/instruction.md b/qasper-0301/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..abdd859f24d3f75be0cb20af630e8e0f5fbbd044 --- /dev/null +++ b/qasper-0301/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Recurrent Neural Network Encoder with Attention for Community Question Answering + +Question: What is a strong feature-based method? \ No newline at end of file diff --git a/qasper-0306/instruction.md b/qasper-0306/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..c9846fed4c6cc88d14bb7b89318f51707ddc7f9c --- /dev/null +++ b/qasper-0306/instruction.md @@ -0,0 +1,37 @@ +Name of Paper: ThisIsCompetition at SemEval-2019 Task 9: BERT is unstable for out-of-domain samples + +Question: What datasets were used? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Joint Encoders for Stable Suggestion Inference", + "Experiments", + "Conclusion", + "Acknowledgement" + ], + "paragraphs": [ + [ + "Opinion mining BIBREF0 is a huge field that covers many NLP tasks ranging from sentiment analysis BIBREF1 , aspect extraction BIBREF2 , and opinion summarization BIBREF3 , among others. Despite the vast literature on opinion mining, the task on suggestion mining has given little attention. Suggestion mining BIBREF4 is the task of collecting and categorizing suggestions about a certain product. This is important because while opinions indirectly give hints on how to improve a product (e.g. analyzing reviews), suggestions are direct improvement requests (e.g. tips, advice, recommendations) from people who have used the product.", + "To this end, BIBREF5 organized a shared task specifically on suggestion mining called SemEval 2019 Task 9: Suggestion Mining from Online Reviews and Forums. The shared task is composed of two subtasks, Subtask A and B. In Subtask A, systems are tasked to predict whether a sentence of a certain domain (i.e. electronics) entails a suggestion or not given a training data of the same domain. In Subtask B, systems are tasked to do suggestion prediction of a sentence from another domain (i.e. hotels). Organizers observed four main challenges: (a) sparse occurrences of suggestions; (b) figurative expressions; (c) different domains; and (d) complex sentences. While previous attempts BIBREF6 , BIBREF4 , BIBREF7 made use of human-engineered features to solve this problem, the goal of the shared task is to leverage the advancements seen on neural networks, by providing a larger dataset to be used on data-intensive models to achieve better performance.", + "This paper describes our system JESSI (Joint Encoders for Stable Suggestion Inference). JESSI is built as a combination of two neural-based encoders using multiple pre-trained word embeddings, including BERT BIBREF8 , a pre-trained deep bidirectional transformer that is recently reported to perform exceptionally well across several tasks. The main intuition behind JESSI comes from our finding that although BERT gives exceptional performance gains when applied to in-domain samples, it becomes unstable when applied to out-of-domain samples, even when using a domain adversarial training BIBREF9 module. This problem is mitigated using two tricks: (1) jointly training BERT with a CNN-based encoder, and (2) using an RNN-based encoder on top of BERT before feeding to the classifier.", + "JESSI is trained using only the datasets given on the shared task, without using any additional external data. Despite this, JESSI performs second on Subtask A with an F1 score of 77.78% among 33 other team submissions. It also performs well on Subtask B with an F1 score of 79.59%." + ], + [ + "We present our model JESSI, which stands for Joint Encoders for Stable Suggestion Inference, shown in Figure FIGREF4 . Given a sentence INLINEFORM0 , JESSI returns a binary suggestion label INLINEFORM1 . JESSI consists of four important components: (1) A BERT-based encoder that leverages general knowledge acquired from a large pre-trained language model, (2) A CNN-based encoder that learns task-specific sentence representations, (3) an MLP classifier that predicts the label given the joint encodings, and (4) a domain adversarial training module that prevents the model to distinguish between the two domains." + ], + [ + "In this section, we show our results and experiments. We denote JESSI-A as our model for Subtask A (i.e., BERT INLINEFORM0 CNN+CNN INLINEFORM1 Att), and JESSI-B as our model for Subtask B (i.e., BERT INLINEFORM2 BiSRU+CNN INLINEFORM3 Att+DomAdv). The performance of the models is measured and compared using the F1-score." + ], + [ + "We presented JESSI (Joint Encoders for Stable Suggestion Inference), our system for the SemEval 2019 Task 9: Suggestion Mining from Online Reviews and Forums. JESSI builds upon jointly combined encoders, borrowing pre-trained knowledge from a language model BERT and a translation model CoVe. We found that BERT alone performs bad and unstably when tested on out-of-domain samples. We mitigate the problem by appending an RNN-based sentence encoder above BERT, and jointly combining a CNN-based encoder. Results from the shared task show that JESSI performs competitively among participating models, obtaining second place on Subtask A with an F-Score of 77.78%. It also performs well on Subtask B, with an F-Score of 79.59%, even without using any additional external data." + ], + [ + "This research was supported by the MSIT (Ministry of Science ICT), Korea, under (National Program for Excellence in SW) (2015-0-00910) and (Artificial Intelligence Contact Center Solution) (2018-0-00605) supervised by the IITP(Institute for Information & Communications Technology Planning & Evaluation) " + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0307/instruction.md b/qasper-0307/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..c57113953a62536b90cc4ca663dd709dafb6b41c --- /dev/null +++ b/qasper-0307/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: ThisIsCompetition at SemEval-2019 Task 9: BERT is unstable for out-of-domain samples + +Question: How did they do compared to other teams? \ No newline at end of file diff --git a/qasper-0308/instruction.md b/qasper-0308/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..59d9f0b41e9f6988850d35789f3c6ad1a7dc7bcf --- /dev/null +++ b/qasper-0308/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: DENS: A Dataset for Multi-class Emotion Analysis + +Question: Which tested technique was the worst performer? \ No newline at end of file diff --git a/qasper-0330/instruction.md b/qasper-0330/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..8d7b011e95f80418dc2016ce08de849e4433c460 --- /dev/null +++ b/qasper-0330/instruction.md @@ -0,0 +1,119 @@ +Name of Paper: Transfer Learning Between Related Tasks Using Expected Label Proportions + +Question: How accurate is the aspect based sentiment classifier trained only using the XR loss? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Lightly Supervised Learning", + "Expectation Regularization (XR)", + "Aspect-based Sentiment Classification", + "Transfer-training between related tasks with XR", + "Stochastic Batched Training for Deep XR", + "Application to Aspect-based Sentiment", + "Relating the classification tasks", + "Classification Architecture", + "Main Results", + "Further experiments", + "Pre-training, Bert", + "Discussion", + "Acknowledgements" + ], + "paragraphs": [ + [ + "Data annotation is a key bottleneck in many data driven algorithms. Specifically, deep learning models, which became a prominent tool in many data driven tasks in recent years, require large datasets to work well. However, many tasks require manual annotations which are relatively hard to obtain at scale. An attractive alternative is lightly supervised learning BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , in which the objective function is supplemented by a set of domain-specific soft-constraints over the model's predictions on unlabeled data. For example, in label regularization BIBREF0 the model is trained to fit the true label proportions of an unlabeled dataset. Label regularization is special case of expectation regularization (XR) BIBREF0 , in which the model is trained to fit the conditional probabilities of labels given features.", + "In this work we consider the case of correlated tasks, in the sense that knowing the labels for task A provides information on the expected label composition of task B. We demonstrate the approach using sentence-level and aspect-level sentiment analysis, which we use as a running example: knowing that a sentence has positive sentiment label (task A), we can expect that most aspects within this sentence (task B) will also have positive label. While this expectation may be noisy on the individual example level, it holds well in aggregate: given a set of positively-labeled sentences, we can robustly estimate the proportion of positively-labeled aspects within this set. For example, in a random set of positive sentences, we expect to find 90% positive aspects, while in a set of negative sentences, we expect to find 70% negative aspects. These proportions can be easily either guessed or estimated from a small set.", + "We propose a novel application of the XR framework for transfer learning in this setup. We present an algorithm (Sec SECREF12 ) that, given a corpus labeled for task A (sentence-level sentiment), learns a classifier for performing task B (aspect-level sentiment) instead, without a direct supervision signal for task B. We note that the label information for task A is only used at training time. Furthermore, due to the stochastic nature of the estimation, the task A labels need not be fully accurate, allowing us to make use of noisy predictions which are assigned by an automatic classifier (Sections SECREF12 and SECREF4 ). In other words, given a medium-sized sentiment corpus with sentence-level labels, and a large collection of un-annotated text from the same distribution, we can train an accurate aspect-level sentiment classifier.", + "The XR loss allows us to use task A labels for training task B predictors. This ability seamlessly integrates into other semi-supervised schemes: we can use the XR loss on top of a pre-trained model to fine-tune the pre-trained representation to the target task, and we can also take the model trained using XR loss and plentiful data and fine-tune it to the target task using the available small-scale annotated data. In Section SECREF56 we explore these options and show that our XR framework improves the results also when applied on top of a pre-trained Bert-based model BIBREF9 .", + "Finally, to make the XR framework applicable to large-scale deep-learning setups, we propose a stochastic batched approximation procedure (Section SECREF19 ). Source code is available at https://github.com/MatanBN/XRTransfer." + ], + [ + "An effective way to supplement small annotated datasets is to use lightly supervised learning, in which the objective function is supplemented by a set of domain-specific soft-constraints over the model's predictions on unlabeled data. Previous work in lightly-supervised learning focused on training classifiers by using prior knowledge of label proportions BIBREF2 , BIBREF3 , BIBREF10 , BIBREF0 , BIBREF11 , BIBREF12 , BIBREF7 , BIBREF13 , BIBREF14 , BIBREF15 , BIBREF16 , BIBREF8 or prior knowledge of features label associations BIBREF1 , BIBREF17 , BIBREF18 , BIBREF19 , BIBREF20 . In the context of NLP, BIBREF17 suggested to use distributional similarities of words to train sequence models for part-of-speech tagging and a classified ads information extraction task. BIBREF19 used background lexical information in terms of word-class associations to train a sentiment classifier. BIBREF21 , BIBREF22 suggested to exploit the bilingual correlations between a resource rich language and a resource poor language to train a classifier for the resource poor language in a lightly supervised manner." + ], + [ + "Expectation Regularization (XR) BIBREF0 is a lightly supervised learning method, in which the model is trained to fit the conditional probabilities of labels given features. In the context of NLP, XR was used by BIBREF20 to train twitter-user attribute prediction using hundreds of noisy distributional expectations based on census demographics. Here, we suggest using XR to train a target task (aspect-level sentiment) based on the output of a related source-task classifier (sentence-level sentiment).", + "The main idea of XR is moving from a fully supervised situation in which each data-point INLINEFORM0 has an associated label INLINEFORM1 , to a setup in which sets of data points INLINEFORM2 are associated with corresponding label proportions INLINEFORM3 over that set.", + "Formally, let INLINEFORM0 be a set of data points, INLINEFORM1 be a set of INLINEFORM2 class labels, INLINEFORM3 be a set of sets where INLINEFORM4 for every INLINEFORM5 , and let INLINEFORM6 be the label distribution of set INLINEFORM7 . For example, INLINEFORM8 would indicate that 70% of data points in INLINEFORM9 are expected to have class 0, 20% are expected to have class 1 and 10% are expected to have class 2. Let INLINEFORM10 be a parameterized function with parameters INLINEFORM11 from INLINEFORM12 to a vector of conditional probabilities over labels in INLINEFORM13 . We write INLINEFORM14 to denote the probability assigned to the INLINEFORM15 th event (the conditional probability of INLINEFORM16 given INLINEFORM17 ).", + "A typically objective when training on fully labeled data of INLINEFORM0 pairs is to maximize likelihood of labeled data using the cross entropy loss, INLINEFORM1 ", + "Instead, in XR our data comes in the form of pairs INLINEFORM0 of sets and their corresponding expected label proportions, and we aim to optimize INLINEFORM1 to fit the label distribution INLINEFORM2 over INLINEFORM3 , for all INLINEFORM4 .", + "As counting the number of predicted class labels over a set INLINEFORM0 leads to a non-differentiable objective, BIBREF0 suggest to relax it and use instead the model's posterior distribution INLINEFORM1 over the set: DISPLAYFORM0 DISPLAYFORM1 ", + "where INLINEFORM0 indicates the INLINEFORM1 th entry in INLINEFORM2 . Then, we would like to set INLINEFORM3 such that INLINEFORM4 and INLINEFORM5 are close. BIBREF0 suggest to use KL-divergence for this. KL-divergence is composed of two parts: INLINEFORM6 INLINEFORM7 ", + "Since INLINEFORM0 is constant, we only need to minimize INLINEFORM1 , therefore the loss function becomes: DISPLAYFORM0 ", + "Notice that computing INLINEFORM0 requires summation over INLINEFORM1 for the entire set INLINEFORM2 , which can be prohibitive. We present batched approximation (Section SECREF19 ) to overcome this.", + " BIBREF0 find that XR might find a degenerate solution. For example, in a three class classification task, where INLINEFORM0 , it might find a solution such that INLINEFORM1 for every instance, as a result, every instance will be classified the same. To avoid this, BIBREF0 suggest to penalize flat distributions by using a temperature coefficient T likewise: DISPLAYFORM0 ", + "Where z is a feature vector and W and b are the linear classifier parameters." + ], + [ + "In the aspect-based sentiment classification (ABSC) task, we are given a sentence and an aspect, and need to determine the sentiment that is expressed towards the aspect. For example the sentence \u201cExcellent food, although the interior could use some help.\u201c has two aspects: food and interior, a positive sentiment is expressed about the food, but a negative sentiment is expressed about the interior. A sentence INLINEFORM0 , may contain 0 or more aspects INLINEFORM1 , where each aspect corresponds to a sub-sequence of the original sentence, and has an associated sentiment label (Neg, Pos, or Neu). Concretely, we follow the task definition in the SemEval-2015 and SemEval-2016 shared tasks BIBREF23 , BIBREF24 , in which the relevant aspects are given and the task focuses on finding the sentiment label of the aspects.", + "While sentence-level sentiment labels are relatively easy to obtain, aspect-level annotation are much more scarce, as demonstrated in the small datasets of the SemEval shared tasks." + ], + [ + "[t!] Inputs: A dataset INLINEFORM0 , batch size INLINEFORM1 , differentiable classifier INLINEFORM2 [H] not converged INLINEFORM3 random( INLINEFORM4 ) INLINEFORM5 random-choice( INLINEFORM6 , INLINEFORM7 ) INLINEFORM8 INLINEFORM9 INLINEFORM10 INLINEFORM11 Compute loss INLINEFORM12 (eq (4)) Compute gradients and update INLINEFORM13 INLINEFORM14 Stochastic Batched XR", + "Consider two classification tasks over a shared input space, a source task INLINEFORM0 from INLINEFORM1 to INLINEFORM2 and a target task INLINEFORM3 from INLINEFORM4 to INLINEFORM5 , which are related through a conditional distribution INLINEFORM6 . In other words, a labeling decision for task INLINEFORM7 induces an expected label distribution over the task INLINEFORM8 . For a set of datapoints INLINEFORM9 that share a source label INLINEFORM10 , we expect to see a target label distribution of INLINEFORM11 .", + "Given a large unlabeled dataset INLINEFORM0 , a small labeled dataset for the target task INLINEFORM1 , classifier INLINEFORM2 (or sufficient training data to train one) for the source task, we wish to use INLINEFORM3 and INLINEFORM4 to train a good classifier INLINEFORM5 for the target task. This can be achieved using the following procedure.", + "Apply INLINEFORM0 to INLINEFORM1 , resulting in a noisy source-side labels INLINEFORM2 for the target task.", + "Estimate the conditional probability INLINEFORM0 table using MLE estimates over INLINEFORM1 INLINEFORM2 ", + "where INLINEFORM0 is a counting function over INLINEFORM1 .", + "Apply INLINEFORM0 to the unlabeled data INLINEFORM1 resulting in labels INLINEFORM2 . Split INLINEFORM3 into INLINEFORM4 sets INLINEFORM5 according to the labeling induced by INLINEFORM6 : INLINEFORM7 ", + "Use Algorithm SECREF12 to train a classifier for the target task using input pairs INLINEFORM0 and the XR loss.", + "In words, by using XR training, we use the expected label proportions over the target task given predicted labels of the source task, to train a target-class classifier." + ], + [ + " BIBREF0 and following work take the base classifier INLINEFORM0 to be a logistic regression classifier, for which they manually derive gradients for the XR loss and train with LBFGs BIBREF25 . However, nothing precludes us from using an arbitrary neural network instead, as long as it culminates in a softmax layer.", + "One complicating factor is that the computation of INLINEFORM0 in equation ( EQREF5 ) requires a summation over INLINEFORM1 for the entire set INLINEFORM2 , which in our setup may contain hundreds of thousands of examples, making gradient computation and optimization impractical. We instead proposed a stochastic batched approximation in which, instead of requiring that the full constraint set INLINEFORM3 will match the expected label posterior distribution, we require that sufficiently large random subsets of it will match the distribution. At each training step we compute the loss and update the gradient with respect to a different random subset. Specifically, in each training step we sample a random pair INLINEFORM4 , sample a random subset INLINEFORM5 of INLINEFORM6 of size INLINEFORM7 , and compute the local XR loss of set INLINEFORM8 : DISPLAYFORM0 ", + "where INLINEFORM0 is computed by summing over the elements of INLINEFORM1 rather than of INLINEFORM2 in equations ( EQREF5 \u20132). The stochastic batched XR training algorithm is given in Algorithm SECREF12 . For large enough INLINEFORM3 , the expected label distribution of the subset is the same as that of the complete set." + ], + [ + "We demonstrate the procedure given above by training Aspect-based Sentiment Classifier (ABSC) using sentence-level sentiment signals." + ], + [ + "We observe that while the sentence-level sentiment does not determine the sentiment of individual aspects (a positive sentence may contain negative remarks about some aspects), it is very predictive of the proportion of sentiment labels of the fragments within a sentence. Positively labeled sentences are likely to have more positive aspects and fewer negative ones, and vice-versa for negatively-labeled sentences. While these proportions may vary on the individual sentence level, we expect them to be stable when aggregating fragments from several sentences: when considering a large enough sample of fragments that all come from positively labeled sentences, we expect the different samples to have roughly similar label proportions to each other. This situation is idealy suited for performing XR training, as described in section SECREF12 .", + "The application to ABSC is almost straightforward, but is complicated a bit by the decomposition of sentences into fragments: each sentence level decision now corresponds to multiple fragment-level decisions. Thus, we apply the sentence-level (task A) classifier INLINEFORM0 on the aspect-level corpus INLINEFORM1 by applying it on the sentence level and then associating the predicted sentence labels with each of the fragments, resulting in fragment-level labeling. Similarly, when we apply INLINEFORM2 to the unlabeled data INLINEFORM3 we again do it at the sentence level, but the sets INLINEFORM4 are composed of fragments, not sentences: INLINEFORM5 ", + "We then apply algorithm SECREF12 as is: at each step of training we sample a source label INLINEFORM0 Pos,Neg,Neu INLINEFORM1 , sample INLINEFORM2 fragments from INLINEFORM3 , and use the XR loss to fit the expected fragment-label proportions over these INLINEFORM4 fragments to INLINEFORM5 . Figure FIGREF21 illustrates the procedure." + ], + [ + "We model the ABSC problem by associating each (sentence,aspect) pair with a sentence-fragment, and constructing a neural classifier from fragments to sentiment labels. We heuristically decompose a sentence into fragments. We use the same BiLSTM based neural architecture for both sentence classification and fragment classification.", + "We now describe the procedure we use to associate a sentence fragment with each (sentence,aspect) pairs. The shared tasks data associates each aspect with a pivot-phrase INLINEFORM0 , where pivot phrase INLINEFORM1 is defined as a pre-determined sequence of words that is contained within the sentence. For a sentence INLINEFORM2 , a set of pivot phrases INLINEFORM3 and a specific pivot phrase INLINEFORM4 , we consult the constituency parse tree of INLINEFORM5 and look for tree nodes that satisfy the following conditions:", + "The node governs the desired pivot phrase INLINEFORM0 .", + "The node governs either a verb (VB, VBD, VBN, VBG, VBP, VBZ) or an adjective (JJ, JJR, JJS), which is different than any INLINEFORM0 .", + "The node governs a minimal number of pivot phrases from INLINEFORM0 , ideally only INLINEFORM1 .", + "We then select the highest node in the tree that satisfies all conditions. The span governed by this node is taken as the fragment associated with aspect INLINEFORM0 . The decomposition procedure is demonstrated in Figure FIGREF22 .", + "When aspect-level information is given, we take the pivot-phrases to be the requested aspects. When aspect-level information is not available, we take each noun in the sentence to be a pivot-phrase.", + "Our classification model is a simple 1-layer BiLSTM encoder (a concatenation of the last states of a forward and a backward running LSTMs) followed by a linear-predictor. The encoder is fed either a complete sentence or a sentence fragment." + ], + [ + "Table TABREF44 compares these baselines to three XR conditions.", + "The first condition, BiLSTM-XR-Dev, performs XR training on the automatically-labeled sentence-level dataset. The only access it has to aspect-level annotation is for estimating the proportions of labels for each sentence-level label, which is done based on the validation set of SemEval-2015 (i.e., 20% of the train set). The XR setting is very effective: without using any in-task data, this model already surpasses all other models, both supervised and semi-supervised, except for the BIBREF35 , BIBREF34 models which achieve higher F1 scores. We note that in contrast to XR, the competing models have complete access to the supervised aspect-based labels. The second condition, BiLSTM-XR, is similar but now the model is allowed to estimate the conditional label proportions based on the entire aspect-based training set (the classifier still does not have direct access to the labels beyond the aggregate proportion information). This improves results further, showing the importance of accurately estimating the proportions. Finally, in BiLSTM-XR+Finetuning, we follow the XR training with fully supervised fine-tuning on the small labeled dataset, using the attention-based model of BIBREF35 . This achieves the best results, and surpasses also the semi-supervised BIBREF35 baseline on accuracy, and matching it on F1.", + "We report significance tests for the robustness of the method under random parameter initialization. Our reported numbers are averaged over five random initialization. Since the datasets are unbalanced w.r.t the label distribution, we report both accuracy and macro-F1.", + "The XR training is also more stable than the other semi-supervised baselines, achieving substantially lower standard deviations across different runs." + ], + [ + "In each experiment in this section we estimate the proportions using the SemEval-2015 train set.", + "How does the XR training scale with the amount of unlabeled data? Figure FIGREF54 a shows the macro-F1 scores on the entire SemEval-2016 dataset, with different unlabeled corpus sizes (measured in number of sentences). An unannotated corpus of INLINEFORM0 sentences is sufficient to surpass the results of the INLINEFORM1 sentence-level trained classifier, and more unannotated data further improves the results.", + "Our method requires a sentence level classifier INLINEFORM0 to label both the target-task corpus and the unlabeled corpus. How does the quality of this classifier affect the overall XR training? We vary the amount of supervision used to train INLINEFORM1 from 0 sentences (assigning the same label to all sentences), to 100, 1000, 5000 and 10000 sentences. We again measure macro-F1 on the entire SemEval 2016 corpus.", + "The results in Figure FIGREF54 b show that when using the prior distributions of aspects (0), the model struggles to learn from this signal, it learns mostly to predict the majority class, and hence reaches very low F1 scores of 35.28. The more data given to the sentence level classifier, the better the potential results will be when training with our method using the classifier labels, with a classifiers trained on 100,1000,5000 and 10000 labeled sentences, we get a F1 scores of 53.81, 58.84, 61.81, 65.58 respectively. Improvements in the source task classifier's quality clearly contribute to the target task accuracy.", + "The Stochastic Batched XR algorithm (Algorithm SECREF12 ) samples a batch of INLINEFORM0 examples at each step to estimate the posterior label distribution used in the loss computation. How does the size of INLINEFORM1 affect the results? We use INLINEFORM2 fragments in our main experiments, but smaller values of INLINEFORM3 reduce GPU memory load and may train better in practice. We tested our method with varying values of INLINEFORM4 on a sample of INLINEFORM5 , using batches that are composed of fragments of 5, 25, 100, 450, 1000 and 4500 sentences. The results are shown in Figure FIGREF54 c. Setting INLINEFORM6 result in low scores. Setting INLINEFORM7 yields better F1 score but with high variance across runs. For INLINEFORM8 fragments the results begin to stabilize, we also see a slight decrease in F1-scores with larger batch sizes. We attribute this drop despite having better estimation of the gradients to the general trend of larger batch sizes being harder to train with stochastic gradient methods." + ], + [ + "The XR training can be performed also over pre-trained representations. We experiment with two pre-training methods: (1) pre-training by training the BiLSTM model to predict the noisy sentence-level predictions. (2) Using the pre-trained Bert representation BIBREF9 . For (1), we compare the effect of pre-train on unlabeled corpora of sizes of INLINEFORM0 , INLINEFORM1 and INLINEFORM2 sentences. Results in Figure FIGREF54 d show that this form of pre-training is effective for smaller unlabeled corpora but evens out for larger ones.", + "For the Bert experiments, we experiment with the Bert-base model with INLINEFORM1 sets, 30 epochs for XR training or sentence level fine-tuning and 15 epochs for aspect based fine-tuning, on each training method we evaluated the model on the dev set after each epoch and the best model was chosen. We compare the following setups:", + "-Bert INLINEFORM0 Aspect Based Finetuning: pretrained bert model finetuned to the aspect based task.", + "-Bert INLINEFORM0 : A pretrained bert model finetuned to the sentence level task on the INLINEFORM1 sentences, and tested by predicting fragment-level sentiment.", + "-Bert INLINEFORM0 INLINEFORM1 INLINEFORM2 Aspect Based Finetuning: pretrained bert model finetuned to the sentence level task, and finetuned again to the aspect based one.", + "-Bert INLINEFORM0 XR: pretrained bert model followed by XR training using our method.", + "-Bert INLINEFORM0 XR INLINEFORM1 Aspect Based Finetuning: pretrained bert followed by XR training and then fine-tuned to the aspect level task.", + "The results are presented in Table TABREF55 . As before, aspect-based fine-tuning is beneficial for both SemEval-16 and SemEval-15. Training a BiLSTM with XR surpasses pre-trained bert models and using XR training on top of the pre-trained Bert models substantially increases the results even further." + ], + [ + "We presented a transfer learning method based on expectation regularization (XR), and demonstrated its effectiveness for training aspect-based sentiment classifiers using sentence-level supervision. The method achieves state-of-the-art results for the task, and is also effective for improving on top of a strong pre-trained Bert model. The proposed method provides an additional data-efficient tool in the modeling arsenal, which can be applied on its own or together with another training method, in situations where there is a conditional relations between the labels of a source task for which we have supervision, and a target task for which we don't.", + "While we demonstrated the approach on the sentiment domain, the required conditional dependence between task labels is present in many situations. Other possible application of the method includes training language identification of tweets given geo-location supervision (knowing the geographical region gives a prior on languages spoken), training predictors for renal failure from textual medical records given classifier for diabetes (there is a strong correlation between the two conditions), training a political affiliation classifier from social media tweets based on age-group classifiers, zip-code information, or social-status classifiers (there are known correlations between all of these to political affiliation), training hate-speech detection based on emotion detection, and so on." + ], + [ + "The work was supported in part by The Israeli Science Foundation (grant number 1555/15)." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0331/instruction.md b/qasper-0331/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..d3339be348d84869210c7905fda0a7d63aa7226a --- /dev/null +++ b/qasper-0331/instruction.md @@ -0,0 +1,119 @@ +Name of Paper: Transfer Learning Between Related Tasks Using Expected Label Proportions + +Question: How is the expectation regularization loss defined? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Lightly Supervised Learning", + "Expectation Regularization (XR)", + "Aspect-based Sentiment Classification", + "Transfer-training between related tasks with XR", + "Stochastic Batched Training for Deep XR", + "Application to Aspect-based Sentiment", + "Relating the classification tasks", + "Classification Architecture", + "Main Results", + "Further experiments", + "Pre-training, Bert", + "Discussion", + "Acknowledgements" + ], + "paragraphs": [ + [ + "Data annotation is a key bottleneck in many data driven algorithms. Specifically, deep learning models, which became a prominent tool in many data driven tasks in recent years, require large datasets to work well. However, many tasks require manual annotations which are relatively hard to obtain at scale. An attractive alternative is lightly supervised learning BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , in which the objective function is supplemented by a set of domain-specific soft-constraints over the model's predictions on unlabeled data. For example, in label regularization BIBREF0 the model is trained to fit the true label proportions of an unlabeled dataset. Label regularization is special case of expectation regularization (XR) BIBREF0 , in which the model is trained to fit the conditional probabilities of labels given features.", + "In this work we consider the case of correlated tasks, in the sense that knowing the labels for task A provides information on the expected label composition of task B. We demonstrate the approach using sentence-level and aspect-level sentiment analysis, which we use as a running example: knowing that a sentence has positive sentiment label (task A), we can expect that most aspects within this sentence (task B) will also have positive label. While this expectation may be noisy on the individual example level, it holds well in aggregate: given a set of positively-labeled sentences, we can robustly estimate the proportion of positively-labeled aspects within this set. For example, in a random set of positive sentences, we expect to find 90% positive aspects, while in a set of negative sentences, we expect to find 70% negative aspects. These proportions can be easily either guessed or estimated from a small set.", + "We propose a novel application of the XR framework for transfer learning in this setup. We present an algorithm (Sec SECREF12 ) that, given a corpus labeled for task A (sentence-level sentiment), learns a classifier for performing task B (aspect-level sentiment) instead, without a direct supervision signal for task B. We note that the label information for task A is only used at training time. Furthermore, due to the stochastic nature of the estimation, the task A labels need not be fully accurate, allowing us to make use of noisy predictions which are assigned by an automatic classifier (Sections SECREF12 and SECREF4 ). In other words, given a medium-sized sentiment corpus with sentence-level labels, and a large collection of un-annotated text from the same distribution, we can train an accurate aspect-level sentiment classifier.", + "The XR loss allows us to use task A labels for training task B predictors. This ability seamlessly integrates into other semi-supervised schemes: we can use the XR loss on top of a pre-trained model to fine-tune the pre-trained representation to the target task, and we can also take the model trained using XR loss and plentiful data and fine-tune it to the target task using the available small-scale annotated data. In Section SECREF56 we explore these options and show that our XR framework improves the results also when applied on top of a pre-trained Bert-based model BIBREF9 .", + "Finally, to make the XR framework applicable to large-scale deep-learning setups, we propose a stochastic batched approximation procedure (Section SECREF19 ). Source code is available at https://github.com/MatanBN/XRTransfer." + ], + [ + "An effective way to supplement small annotated datasets is to use lightly supervised learning, in which the objective function is supplemented by a set of domain-specific soft-constraints over the model's predictions on unlabeled data. Previous work in lightly-supervised learning focused on training classifiers by using prior knowledge of label proportions BIBREF2 , BIBREF3 , BIBREF10 , BIBREF0 , BIBREF11 , BIBREF12 , BIBREF7 , BIBREF13 , BIBREF14 , BIBREF15 , BIBREF16 , BIBREF8 or prior knowledge of features label associations BIBREF1 , BIBREF17 , BIBREF18 , BIBREF19 , BIBREF20 . In the context of NLP, BIBREF17 suggested to use distributional similarities of words to train sequence models for part-of-speech tagging and a classified ads information extraction task. BIBREF19 used background lexical information in terms of word-class associations to train a sentiment classifier. BIBREF21 , BIBREF22 suggested to exploit the bilingual correlations between a resource rich language and a resource poor language to train a classifier for the resource poor language in a lightly supervised manner." + ], + [ + "Expectation Regularization (XR) BIBREF0 is a lightly supervised learning method, in which the model is trained to fit the conditional probabilities of labels given features. In the context of NLP, XR was used by BIBREF20 to train twitter-user attribute prediction using hundreds of noisy distributional expectations based on census demographics. Here, we suggest using XR to train a target task (aspect-level sentiment) based on the output of a related source-task classifier (sentence-level sentiment).", + "The main idea of XR is moving from a fully supervised situation in which each data-point INLINEFORM0 has an associated label INLINEFORM1 , to a setup in which sets of data points INLINEFORM2 are associated with corresponding label proportions INLINEFORM3 over that set.", + "Formally, let INLINEFORM0 be a set of data points, INLINEFORM1 be a set of INLINEFORM2 class labels, INLINEFORM3 be a set of sets where INLINEFORM4 for every INLINEFORM5 , and let INLINEFORM6 be the label distribution of set INLINEFORM7 . For example, INLINEFORM8 would indicate that 70% of data points in INLINEFORM9 are expected to have class 0, 20% are expected to have class 1 and 10% are expected to have class 2. Let INLINEFORM10 be a parameterized function with parameters INLINEFORM11 from INLINEFORM12 to a vector of conditional probabilities over labels in INLINEFORM13 . We write INLINEFORM14 to denote the probability assigned to the INLINEFORM15 th event (the conditional probability of INLINEFORM16 given INLINEFORM17 ).", + "A typically objective when training on fully labeled data of INLINEFORM0 pairs is to maximize likelihood of labeled data using the cross entropy loss, INLINEFORM1 ", + "Instead, in XR our data comes in the form of pairs INLINEFORM0 of sets and their corresponding expected label proportions, and we aim to optimize INLINEFORM1 to fit the label distribution INLINEFORM2 over INLINEFORM3 , for all INLINEFORM4 .", + "As counting the number of predicted class labels over a set INLINEFORM0 leads to a non-differentiable objective, BIBREF0 suggest to relax it and use instead the model's posterior distribution INLINEFORM1 over the set: DISPLAYFORM0 DISPLAYFORM1 ", + "where INLINEFORM0 indicates the INLINEFORM1 th entry in INLINEFORM2 . Then, we would like to set INLINEFORM3 such that INLINEFORM4 and INLINEFORM5 are close. BIBREF0 suggest to use KL-divergence for this. KL-divergence is composed of two parts: INLINEFORM6 INLINEFORM7 ", + "Since INLINEFORM0 is constant, we only need to minimize INLINEFORM1 , therefore the loss function becomes: DISPLAYFORM0 ", + "Notice that computing INLINEFORM0 requires summation over INLINEFORM1 for the entire set INLINEFORM2 , which can be prohibitive. We present batched approximation (Section SECREF19 ) to overcome this.", + " BIBREF0 find that XR might find a degenerate solution. For example, in a three class classification task, where INLINEFORM0 , it might find a solution such that INLINEFORM1 for every instance, as a result, every instance will be classified the same. To avoid this, BIBREF0 suggest to penalize flat distributions by using a temperature coefficient T likewise: DISPLAYFORM0 ", + "Where z is a feature vector and W and b are the linear classifier parameters." + ], + [ + "In the aspect-based sentiment classification (ABSC) task, we are given a sentence and an aspect, and need to determine the sentiment that is expressed towards the aspect. For example the sentence \u201cExcellent food, although the interior could use some help.\u201c has two aspects: food and interior, a positive sentiment is expressed about the food, but a negative sentiment is expressed about the interior. A sentence INLINEFORM0 , may contain 0 or more aspects INLINEFORM1 , where each aspect corresponds to a sub-sequence of the original sentence, and has an associated sentiment label (Neg, Pos, or Neu). Concretely, we follow the task definition in the SemEval-2015 and SemEval-2016 shared tasks BIBREF23 , BIBREF24 , in which the relevant aspects are given and the task focuses on finding the sentiment label of the aspects.", + "While sentence-level sentiment labels are relatively easy to obtain, aspect-level annotation are much more scarce, as demonstrated in the small datasets of the SemEval shared tasks." + ], + [ + "[t!] Inputs: A dataset INLINEFORM0 , batch size INLINEFORM1 , differentiable classifier INLINEFORM2 [H] not converged INLINEFORM3 random( INLINEFORM4 ) INLINEFORM5 random-choice( INLINEFORM6 , INLINEFORM7 ) INLINEFORM8 INLINEFORM9 INLINEFORM10 INLINEFORM11 Compute loss INLINEFORM12 (eq (4)) Compute gradients and update INLINEFORM13 INLINEFORM14 Stochastic Batched XR", + "Consider two classification tasks over a shared input space, a source task INLINEFORM0 from INLINEFORM1 to INLINEFORM2 and a target task INLINEFORM3 from INLINEFORM4 to INLINEFORM5 , which are related through a conditional distribution INLINEFORM6 . In other words, a labeling decision for task INLINEFORM7 induces an expected label distribution over the task INLINEFORM8 . For a set of datapoints INLINEFORM9 that share a source label INLINEFORM10 , we expect to see a target label distribution of INLINEFORM11 .", + "Given a large unlabeled dataset INLINEFORM0 , a small labeled dataset for the target task INLINEFORM1 , classifier INLINEFORM2 (or sufficient training data to train one) for the source task, we wish to use INLINEFORM3 and INLINEFORM4 to train a good classifier INLINEFORM5 for the target task. This can be achieved using the following procedure.", + "Apply INLINEFORM0 to INLINEFORM1 , resulting in a noisy source-side labels INLINEFORM2 for the target task.", + "Estimate the conditional probability INLINEFORM0 table using MLE estimates over INLINEFORM1 INLINEFORM2 ", + "where INLINEFORM0 is a counting function over INLINEFORM1 .", + "Apply INLINEFORM0 to the unlabeled data INLINEFORM1 resulting in labels INLINEFORM2 . Split INLINEFORM3 into INLINEFORM4 sets INLINEFORM5 according to the labeling induced by INLINEFORM6 : INLINEFORM7 ", + "Use Algorithm SECREF12 to train a classifier for the target task using input pairs INLINEFORM0 and the XR loss.", + "In words, by using XR training, we use the expected label proportions over the target task given predicted labels of the source task, to train a target-class classifier." + ], + [ + " BIBREF0 and following work take the base classifier INLINEFORM0 to be a logistic regression classifier, for which they manually derive gradients for the XR loss and train with LBFGs BIBREF25 . However, nothing precludes us from using an arbitrary neural network instead, as long as it culminates in a softmax layer.", + "One complicating factor is that the computation of INLINEFORM0 in equation ( EQREF5 ) requires a summation over INLINEFORM1 for the entire set INLINEFORM2 , which in our setup may contain hundreds of thousands of examples, making gradient computation and optimization impractical. We instead proposed a stochastic batched approximation in which, instead of requiring that the full constraint set INLINEFORM3 will match the expected label posterior distribution, we require that sufficiently large random subsets of it will match the distribution. At each training step we compute the loss and update the gradient with respect to a different random subset. Specifically, in each training step we sample a random pair INLINEFORM4 , sample a random subset INLINEFORM5 of INLINEFORM6 of size INLINEFORM7 , and compute the local XR loss of set INLINEFORM8 : DISPLAYFORM0 ", + "where INLINEFORM0 is computed by summing over the elements of INLINEFORM1 rather than of INLINEFORM2 in equations ( EQREF5 \u20132). The stochastic batched XR training algorithm is given in Algorithm SECREF12 . For large enough INLINEFORM3 , the expected label distribution of the subset is the same as that of the complete set." + ], + [ + "We demonstrate the procedure given above by training Aspect-based Sentiment Classifier (ABSC) using sentence-level sentiment signals." + ], + [ + "We observe that while the sentence-level sentiment does not determine the sentiment of individual aspects (a positive sentence may contain negative remarks about some aspects), it is very predictive of the proportion of sentiment labels of the fragments within a sentence. Positively labeled sentences are likely to have more positive aspects and fewer negative ones, and vice-versa for negatively-labeled sentences. While these proportions may vary on the individual sentence level, we expect them to be stable when aggregating fragments from several sentences: when considering a large enough sample of fragments that all come from positively labeled sentences, we expect the different samples to have roughly similar label proportions to each other. This situation is idealy suited for performing XR training, as described in section SECREF12 .", + "The application to ABSC is almost straightforward, but is complicated a bit by the decomposition of sentences into fragments: each sentence level decision now corresponds to multiple fragment-level decisions. Thus, we apply the sentence-level (task A) classifier INLINEFORM0 on the aspect-level corpus INLINEFORM1 by applying it on the sentence level and then associating the predicted sentence labels with each of the fragments, resulting in fragment-level labeling. Similarly, when we apply INLINEFORM2 to the unlabeled data INLINEFORM3 we again do it at the sentence level, but the sets INLINEFORM4 are composed of fragments, not sentences: INLINEFORM5 ", + "We then apply algorithm SECREF12 as is: at each step of training we sample a source label INLINEFORM0 Pos,Neg,Neu INLINEFORM1 , sample INLINEFORM2 fragments from INLINEFORM3 , and use the XR loss to fit the expected fragment-label proportions over these INLINEFORM4 fragments to INLINEFORM5 . Figure FIGREF21 illustrates the procedure." + ], + [ + "We model the ABSC problem by associating each (sentence,aspect) pair with a sentence-fragment, and constructing a neural classifier from fragments to sentiment labels. We heuristically decompose a sentence into fragments. We use the same BiLSTM based neural architecture for both sentence classification and fragment classification.", + "We now describe the procedure we use to associate a sentence fragment with each (sentence,aspect) pairs. The shared tasks data associates each aspect with a pivot-phrase INLINEFORM0 , where pivot phrase INLINEFORM1 is defined as a pre-determined sequence of words that is contained within the sentence. For a sentence INLINEFORM2 , a set of pivot phrases INLINEFORM3 and a specific pivot phrase INLINEFORM4 , we consult the constituency parse tree of INLINEFORM5 and look for tree nodes that satisfy the following conditions:", + "The node governs the desired pivot phrase INLINEFORM0 .", + "The node governs either a verb (VB, VBD, VBN, VBG, VBP, VBZ) or an adjective (JJ, JJR, JJS), which is different than any INLINEFORM0 .", + "The node governs a minimal number of pivot phrases from INLINEFORM0 , ideally only INLINEFORM1 .", + "We then select the highest node in the tree that satisfies all conditions. The span governed by this node is taken as the fragment associated with aspect INLINEFORM0 . The decomposition procedure is demonstrated in Figure FIGREF22 .", + "When aspect-level information is given, we take the pivot-phrases to be the requested aspects. When aspect-level information is not available, we take each noun in the sentence to be a pivot-phrase.", + "Our classification model is a simple 1-layer BiLSTM encoder (a concatenation of the last states of a forward and a backward running LSTMs) followed by a linear-predictor. The encoder is fed either a complete sentence or a sentence fragment." + ], + [ + "Table TABREF44 compares these baselines to three XR conditions.", + "The first condition, BiLSTM-XR-Dev, performs XR training on the automatically-labeled sentence-level dataset. The only access it has to aspect-level annotation is for estimating the proportions of labels for each sentence-level label, which is done based on the validation set of SemEval-2015 (i.e., 20% of the train set). The XR setting is very effective: without using any in-task data, this model already surpasses all other models, both supervised and semi-supervised, except for the BIBREF35 , BIBREF34 models which achieve higher F1 scores. We note that in contrast to XR, the competing models have complete access to the supervised aspect-based labels. The second condition, BiLSTM-XR, is similar but now the model is allowed to estimate the conditional label proportions based on the entire aspect-based training set (the classifier still does not have direct access to the labels beyond the aggregate proportion information). This improves results further, showing the importance of accurately estimating the proportions. Finally, in BiLSTM-XR+Finetuning, we follow the XR training with fully supervised fine-tuning on the small labeled dataset, using the attention-based model of BIBREF35 . This achieves the best results, and surpasses also the semi-supervised BIBREF35 baseline on accuracy, and matching it on F1.", + "We report significance tests for the robustness of the method under random parameter initialization. Our reported numbers are averaged over five random initialization. Since the datasets are unbalanced w.r.t the label distribution, we report both accuracy and macro-F1.", + "The XR training is also more stable than the other semi-supervised baselines, achieving substantially lower standard deviations across different runs." + ], + [ + "In each experiment in this section we estimate the proportions using the SemEval-2015 train set.", + "How does the XR training scale with the amount of unlabeled data? Figure FIGREF54 a shows the macro-F1 scores on the entire SemEval-2016 dataset, with different unlabeled corpus sizes (measured in number of sentences). An unannotated corpus of INLINEFORM0 sentences is sufficient to surpass the results of the INLINEFORM1 sentence-level trained classifier, and more unannotated data further improves the results.", + "Our method requires a sentence level classifier INLINEFORM0 to label both the target-task corpus and the unlabeled corpus. How does the quality of this classifier affect the overall XR training? We vary the amount of supervision used to train INLINEFORM1 from 0 sentences (assigning the same label to all sentences), to 100, 1000, 5000 and 10000 sentences. We again measure macro-F1 on the entire SemEval 2016 corpus.", + "The results in Figure FIGREF54 b show that when using the prior distributions of aspects (0), the model struggles to learn from this signal, it learns mostly to predict the majority class, and hence reaches very low F1 scores of 35.28. The more data given to the sentence level classifier, the better the potential results will be when training with our method using the classifier labels, with a classifiers trained on 100,1000,5000 and 10000 labeled sentences, we get a F1 scores of 53.81, 58.84, 61.81, 65.58 respectively. Improvements in the source task classifier's quality clearly contribute to the target task accuracy.", + "The Stochastic Batched XR algorithm (Algorithm SECREF12 ) samples a batch of INLINEFORM0 examples at each step to estimate the posterior label distribution used in the loss computation. How does the size of INLINEFORM1 affect the results? We use INLINEFORM2 fragments in our main experiments, but smaller values of INLINEFORM3 reduce GPU memory load and may train better in practice. We tested our method with varying values of INLINEFORM4 on a sample of INLINEFORM5 , using batches that are composed of fragments of 5, 25, 100, 450, 1000 and 4500 sentences. The results are shown in Figure FIGREF54 c. Setting INLINEFORM6 result in low scores. Setting INLINEFORM7 yields better F1 score but with high variance across runs. For INLINEFORM8 fragments the results begin to stabilize, we also see a slight decrease in F1-scores with larger batch sizes. We attribute this drop despite having better estimation of the gradients to the general trend of larger batch sizes being harder to train with stochastic gradient methods." + ], + [ + "The XR training can be performed also over pre-trained representations. We experiment with two pre-training methods: (1) pre-training by training the BiLSTM model to predict the noisy sentence-level predictions. (2) Using the pre-trained Bert representation BIBREF9 . For (1), we compare the effect of pre-train on unlabeled corpora of sizes of INLINEFORM0 , INLINEFORM1 and INLINEFORM2 sentences. Results in Figure FIGREF54 d show that this form of pre-training is effective for smaller unlabeled corpora but evens out for larger ones.", + "For the Bert experiments, we experiment with the Bert-base model with INLINEFORM1 sets, 30 epochs for XR training or sentence level fine-tuning and 15 epochs for aspect based fine-tuning, on each training method we evaluated the model on the dev set after each epoch and the best model was chosen. We compare the following setups:", + "-Bert INLINEFORM0 Aspect Based Finetuning: pretrained bert model finetuned to the aspect based task.", + "-Bert INLINEFORM0 : A pretrained bert model finetuned to the sentence level task on the INLINEFORM1 sentences, and tested by predicting fragment-level sentiment.", + "-Bert INLINEFORM0 INLINEFORM1 INLINEFORM2 Aspect Based Finetuning: pretrained bert model finetuned to the sentence level task, and finetuned again to the aspect based one.", + "-Bert INLINEFORM0 XR: pretrained bert model followed by XR training using our method.", + "-Bert INLINEFORM0 XR INLINEFORM1 Aspect Based Finetuning: pretrained bert followed by XR training and then fine-tuned to the aspect level task.", + "The results are presented in Table TABREF55 . As before, aspect-based fine-tuning is beneficial for both SemEval-16 and SemEval-15. Training a BiLSTM with XR surpasses pre-trained bert models and using XR training on top of the pre-trained Bert models substantially increases the results even further." + ], + [ + "We presented a transfer learning method based on expectation regularization (XR), and demonstrated its effectiveness for training aspect-based sentiment classifiers using sentence-level supervision. The method achieves state-of-the-art results for the task, and is also effective for improving on top of a strong pre-trained Bert model. The proposed method provides an additional data-efficient tool in the modeling arsenal, which can be applied on its own or together with another training method, in situations where there is a conditional relations between the labels of a source task for which we have supervision, and a target task for which we don't.", + "While we demonstrated the approach on the sentiment domain, the required conditional dependence between task labels is present in many situations. Other possible application of the method includes training language identification of tweets given geo-location supervision (knowing the geographical region gives a prior on languages spoken), training predictors for renal failure from textual medical records given classifier for diabetes (there is a strong correlation between the two conditions), training a political affiliation classifier from social media tweets based on age-group classifiers, zip-code information, or social-status classifiers (there are known correlations between all of these to political affiliation), training hate-speech detection based on emotion detection, and so on." + ], + [ + "The work was supported in part by The Israeli Science Foundation (grant number 1555/15)." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0337/instruction.md b/qasper-0337/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..edc4b67110f805a8d903f48591ac239b43f360c4 --- /dev/null +++ b/qasper-0337/instruction.md @@ -0,0 +1,161 @@ +Name of Paper: Interactive Machine Comprehension with Information Seeking Agents + +Question: How do they train models in this setup? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Works", + "iMRC: Making MRC Interactive", + "iMRC: Making MRC Interactive ::: Interactive MRC as a POMDP", + "iMRC: Making MRC Interactive ::: Action Space", + "iMRC: Making MRC Interactive ::: Query Types", + "iMRC: Making MRC Interactive ::: Evaluation Metric", + "Baseline Agent", + "Baseline Agent ::: Model Structure", + "Baseline Agent ::: Model Structure ::: Encoder", + "Baseline Agent ::: Model Structure ::: Action Generator", + "Baseline Agent ::: Model Structure ::: Question Answerer", + "Baseline Agent ::: Memory and Reward Shaping ::: Memory", + "Baseline Agent ::: Memory and Reward Shaping ::: Reward Shaping", + "Baseline Agent ::: Memory and Reward Shaping ::: Ctrl+F Only Mode", + "Baseline Agent ::: Training Strategy", + "Baseline Agent ::: Training Strategy ::: Action Generation", + "Baseline Agent ::: Training Strategy ::: Question Answering", + "Experimental Results", + "Experimental Results ::: Mastering Training Games", + "Experimental Results ::: Generalizing to Test Set", + "Discussion and Future Work" + ], + "paragraphs": [ + [ + "Many machine reading comprehension (MRC) datasets have been released in recent years BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4 to benchmark a system's ability to understand and reason over natural language. Typically, these datasets require an MRC model to read through a document to answer a question about information contained therein.", + "The supporting document is, more often than not, static and fully observable. This raises concerns, since models may find answers simply through shallow pattern matching; e.g., syntactic similarity between the words in questions and documents. As pointed out by BIBREF5, for questions starting with when, models tend to predict the only date/time answer in the supporting document. Such behavior limits the generality and usefulness of MRC models, and suggests that they do not learn a proper `understanding' of the intended task. In this paper, to address this problem, we shift the focus of MRC data away from `spoon-feeding' models with sufficient information in fully observable, static documents. Instead, we propose interactive versions of existing MRC tasks, whereby the information needed to answer a question must be gathered sequentially.", + "The key idea behind our proposed interactive MRC (iMRC) is to restrict the document context that a model observes at one time. Concretely, we split a supporting document into its component sentences and withhold these sentences from the model. Given a question, the model must issue commands to observe sentences in the withheld set; we equip models with actions such as Ctrl+F (search for token) and stop for searching through partially observed documents. A model searches iteratively, conditioning each command on the input question and the sentences it has observed previously. Thus, our task requires models to `feed themselves' rather than spoon-feeding them with information. This casts MRC as a sequential decision-making problem amenable to reinforcement learning (RL).", + "As an initial case study, we repurpose two well known, related corpora with different difficulty levels for our interactive MRC task: SQuAD and NewsQA. Table TABREF2 shows some examples of a model performing interactive MRC on these datasets. Naturally, our reframing makes the MRC problem harder; however, we believe the added demands of iMRC more closely match web-level QA and may lead to deeper comprehension of documents' content.", + "The main contributions of this work are as follows:", + "We describe a method to make MRC datasets interactive and formulate the new task as an RL problem.", + "We develop a baseline agent that combines a top performing MRC model and a state-of-the-art RL optimization algorithm and test it on our iMRC tasks.", + "We conduct experiments on several variants of iMRC and discuss the significant challenges posed by our setting." + ], + [ + "Skip-reading BIBREF6, BIBREF7, BIBREF8 is an existing setting in which MRC models read partial documents. Concretely, these methods assume that not all tokens in the input sequence are useful, and therefore learn to skip irrelevant tokens based on the current input and their internal memory. Since skipping decisions are discrete, the models are often optimized by the REINFORCE algorithm BIBREF9. For example, the structural-jump-LSTM proposed in BIBREF10 learns to skip and jump over chunks of text. In a similar vein, BIBREF11 designed a QA task where the model reads streaming data unidirectionally, without knowing when the question will be provided. Skip-reading approaches are limited in that they only consider jumping over a few consecutive tokens and the skipping operations are usually unidirectional. Based on the assumption that a single pass of reading may not provide sufficient information, multi-pass reading methods have also been studied BIBREF12, BIBREF13.", + "Compared to skip-reading and multi-turn reading, our work enables an agent to jump through a document in a more dynamic manner, in some sense combining aspects of skip-reading and re-reading. For example, it can jump forward, backward, or to an arbitrary position, depending on the query. This also distinguishes the model we develop in this work from ReasoNet BIBREF13, where an agent decides when to stop unidirectional reading.", + "Recently, BIBREF14 propose DocQN, which is a DQN-based agent that leverages the (tree) structure of documents and navigates across sentences and paragraphs. The proposed method has been shown to outperform vanilla DQN and IR baselines on TriviaQA dataset. The main differences between our work and DocQA include: iMRC does not depend on extra meta information of documents (e.g., title, paragraph title) for building document trees as in DocQN; our proposed environment is partially-observable, and thus an agent is required to explore and memorize the environment via interaction; the action space in our setting (especially for the Ctrl+F command as defined in later section) is arguably larger than the tree sampling action space in DocQN.", + "Closely related to iMRC is work by BIBREF15, in which the authors introduce a collection of synthetic tasks to train and test information-seeking capabilities in neural models. We extend that work by developing a realistic and challenging text-based task.", + "Broadly speaking, our approach is also linked to the optimal stopping problem in the literature Markov decision processes (MDP) BIBREF16, where at each time-step the agent either continues or stops and accumulates reward. Here, we reformulate conventional QA tasks through the lens of optimal stopping, in hopes of improving over the shallow matching behaviors exhibited by many MRC systems." + ], + [ + "We build the iSQuAD and iNewsQA datasets based on SQuAD v1.1 BIBREF0 and NewsQA BIBREF1. Both original datasets share similar properties. Specifically, every data-point consists of a tuple, $\\lbrace p, q, a\\rbrace $, where $p$ represents a paragraph, $q$ a question, and $a$ is the answer. The answer is a word span defined by head and tail positions in $p$. NewsQA is more difficult than SQuAD because it has a larger vocabulary, more difficult questions, and longer source documents.", + "We first split every paragraph $p$ into a list of sentences $\\mathcal {S} = \\lbrace s_1, s_2, ..., s_n\\rbrace $, where $n$ stands for number of sentences in $p$. Given a question $q$, rather than showing the entire paragraph $p$, we only show an agent the first sentence $s_1$ and withhold the rest. The agent must issue commands to reveal the hidden sentences progressively and thereby gather the information needed to answer question $q$.", + "An agent decides when to stop interacting and output an answer, but the number of interaction steps is limited. Once an agent has exhausted its step budget, it is forced to answer the question." + ], + [ + "As described in the previous section, we convert MRC tasks into sequential decision-making problems (which we will refer to as games). These can be described naturally within the reinforcement learning (RL) framework. Formally, tasks in iMRC are partially observable Markov decision processes (POMDP) BIBREF17. An iMRC data-point is a discrete-time POMDP defined by $(S, T, A, \\Omega , O, R, \\gamma )$, where $\\gamma \\in [0, 1]$ is the discount factor and the other elements are described in detail below.", + "Environment States ($S$): The environment state at turn $t$ in the game is $s_t \\in S$. It contains the complete internal information of the game, much of which is hidden from the agent. When an agent issues an action $a_t$, the environment transitions to state $s_{t+1}$ with probability $T(s_{t+1} | s_t, a_t)$). In this work, transition probabilities are either 0 or 1 (i.e., deterministic environment).", + "Actions ($A$): At each game turn $t$, the agent issues an action $a_t \\in A$. We will elaborate on the action space of iMRC in the action space section.", + "Observations ($\\Omega $): The text information perceived by the agent at a given game turn $t$ is the agent's observation, $o_t \\in \\Omega $, which depends on the environment state and the previous action with probability $O(o_t|s_t)$. In this work, observation probabilities are either 0 or 1 (i.e., noiseless observation). Reward Function ($R$): Based on its actions, the agent receives rewards $r_t = R(s_t, a_t)$. Its objective is to maximize the expected discounted sum of rewards $E \\left[\\sum _t \\gamma ^t r_t \\right]$." + ], + [ + "To better describe the action space of iMRC, we split an agent's actions into two phases: information gathering and question answering. During the information gathering phase, the agent interacts with the environment to collect knowledge. It answers questions with its accumulated knowledge in the question answering phase.", + "Information Gathering: At step $t$ of the information gathering phase, the agent can issue one of the following four actions to interact with the paragraph $p$, where $p$ consists of $n$ sentences and where the current observation corresponds to sentence $s_k,~1 \\le k \\le n$:", + "previous: jump to $ \\small {\\left\\lbrace \\begin{array}{ll} s_n & \\text{if $k = 1$,}\\\\ s_{k-1} & \\text{otherwise;} \\end{array}\\right.} $", + "next: jump to $ \\small {\\left\\lbrace \\begin{array}{ll} s_1 & \\text{if $k = n$,}\\\\ s_{k+1} & \\text{otherwise;} \\end{array}\\right.} $", + "Ctrl+F $<$query$>$: jump to the sentence that contains the next occurrence of \u201cquery\u201d;", + "stop: terminate information gathering phase.", + "Question Answering: We follow the output format of both SQuAD and NewsQA, where an agent is required to point to the head and tail positions of an answer span within $p$. Assume that at step $t$ the agent stops interacting and the observation $o_t$ is $s_k$. The agent points to a head-tail position pair in $s_k$." + ], + [ + "Given the question \u201cWhen is the deadline of AAAI?\u201d, as a human, one might try searching \u201cAAAI\u201d on a search engine, follow the link to the official AAAI website, then search for keywords \u201cdeadline\u201d or \u201cdue date\u201d on the website to jump to a specific paragraph. Humans have a deep understanding of questions because of their significant background knowledge. As a result, the keywords they use to search are not limited to what appears in the question.", + "Inspired by this observation, we study 3 query types for the Ctrl+F $<$query$>$ command.", + "One token from the question: the setting with smallest action space. Because iMRC deals with Ctrl+F commands by exact string matching, there is no guarantee that all sentences are accessible from question tokens only.", + "One token from the union of the question and the current observation: an intermediate level where the action space is larger.", + "One token from the dataset vocabulary: the action space is huge (see Table TABREF16 for statistics of SQuAD and NewsQA). It is guaranteed that all sentences in all documents are accessible through these tokens." + ], + [ + "Since iMRC involves both MRC and RL, we adopt evaluation metrics from both settings. First, as a question answering task, we use $\\text{F}_1$ score to compare predicted answers against ground-truth, as in previous works. When there exist multiple ground-truth answers, we report the max $\\text{F}_1$ score. Second, mastering multiple games remains quite challenging for RL agents. Therefore, we evaluate an agent's performance during both its training and testing phases. During training, we report training curves averaged over 3 random seeds. During test, we follow common practice in supervised learning tasks where we report the agent's test performance corresponding to its best validation performance ." + ], + [ + "As a baseline, we propose QA-DQN, an agent that adopts components from QANet BIBREF18 and adds an extra command generation module inspired by LSTM-DQN BIBREF19.", + "As illustrated in Figure FIGREF6, the agent consists of three components: an encoder, an action generator, and a question answerer. More precisely, at a game step $t$, the encoder reads observation string $o_t$ and question string $q$ to generate attention aggregated hidden representations $M_t$. Using $M_t$, the action generator outputs commands (defined in previous sections) to interact with iMRC. If the generated command is stop or the agent is forced to stop, the question answerer takes the current information at game step $t$ to generate head and tail pointers for answering the question; otherwise, the information gathering procedure continues.", + "In this section, we describe the high-level model structure and training strategies of QA-DQN. We refer readers to BIBREF18 for detailed information. We will release datasets and code in the near future." + ], + [ + "In this section, we use game step $t$ to denote one round of interaction between an agent with the iMRC environment. We use $o_t$ to denote text observation at game step $t$ and $q$ to denote question text. We use $L$ to refer to a linear transformation. $[\\cdot ;\\cdot ]$ denotes vector concatenation." + ], + [ + "The encoder consists of an embedding layer, two stacks of transformer blocks (denoted as encoder transformer blocks and aggregation transformer blocks), and an attention layer.", + "In the embedding layer, we aggregate both word- and character-level embeddings. Word embeddings are initialized by the 300-dimension fastText BIBREF20 vectors trained on Common Crawl (600B tokens), and are fixed during training. Character embeddings are initialized by 200-dimension random vectors. A convolutional layer with 96 kernels of size 5 is used to aggregate the sequence of characters. We use a max pooling layer on the character dimension, then a multi-layer perceptron (MLP) of size 96 is used to aggregate the concatenation of word- and character-level representations. A highway network BIBREF21 is used on top of this MLP. The resulting vectors are used as input to the encoding transformer blocks.", + "Each encoding transformer block consists of four convolutional layers (with shared weights), a self-attention layer, and an MLP. Each convolutional layer has 96 filters, each kernel's size is 7. In the self-attention layer, we use a block hidden size of 96 and a single head attention mechanism. Layer normalization and dropout are applied after each component inside the block. We add positional encoding into each block's input. We use one layer of such an encoding block.", + "At a game step $t$, the encoder processes text observation $o_t$ and question $q$ to generate context-aware encodings $h_{o_t} \\in \\mathbb {R}^{L^{o_t} \\times H_1}$ and $h_q \\in \\mathbb {R}^{L^{q} \\times H_1}$, where $L^{o_t}$ and $L^{q}$ denote length of $o_t$ and $q$ respectively, $H_1$ is 96.", + "Following BIBREF18, we use a context-query attention layer to aggregate the two representations $h_{o_t}$ and $h_q$. Specifically, the attention layer first uses two MLPs to map $h_{o_t}$ and $h_q$ into the same space, with the resulting representations denoted as $h_{o_t}^{\\prime } \\in \\mathbb {R}^{L^{o_t} \\times H_2}$ and $h_q^{\\prime } \\in \\mathbb {R}^{L^{q} \\times H_2}$, in which, $H_2$ is 96.", + "Then, a tri-linear similarity function is used to compute the similarities between each pair of $h_{o_t}^{\\prime }$ and $h_q^{\\prime }$ items:", + "where $\\odot $ indicates element-wise multiplication and $w$ is trainable parameter vector of size 96.", + "We apply softmax to the resulting similarity matrix $S$ along both dimensions, producing $S^A$ and $S^B$. Information in the two representations are then aggregated as", + "where $h_{oq}$ is aggregated observation representation.", + "On top of the attention layer, a stack of aggregation transformer blocks is used to further map the observation representations to action representations and answer representations. The configuration parameters are the same as the encoder transformer blocks, except there are two convolution layers (with shared weights), and the number of blocks is 7.", + "Let $M_t \\in \\mathbb {R}^{L^{o_t} \\times H_3}$ denote the output of the stack of aggregation transformer blocks, in which $H_3$ is 96." + ], + [ + "The action generator takes $M_t$ as input and estimates Q-values for all possible actions. As described in previous section, when an action is a Ctrl+F command, it is composed of two tokens (the token \u201cCtrl+F\u201d and the query token). Therefore, the action generator consists of three MLPs:", + "Here, the size of $L_{shared} \\in \\mathbb {R}^{95 \\times 150}$; $L_{action}$ has an output size of 4 or 2 depending on the number of actions available; the size of $L_{ctrlf}$ is the same as the size of a dataset's vocabulary size (depending on different query type settings, we mask out words in the vocabulary that are not query candidates). The overall Q-value is simply the sum of the two components:" + ], + [ + "Following BIBREF18, we append two extra stacks of aggregation transformer blocks on top of the encoder to compute head and tail positions:", + "Here, $M_{head}$ and $M_{tail}$ are outputs of the two extra transformer stacks, $L_0$, $L_1$, $L_2$ and $L_3$ are trainable parameters with output size 150, 150, 1 and 1, respectively." + ], + [ + "In iMRC, some questions may not be easily answerable based only on observation of a single sentence. To overcome this limitation, we provide an explicit memory mechanism to QA-DQN. Specifically, we use a queue to store strings that have been observed recently. The queue has a limited size of slots (we use queues of size [1, 3, 5] in this work). This prevents the agent from issuing next commands until the environment has been observed fully, in which case our task would degenerate to the standard MRC setting. The memory slots are reset episodically." + ], + [ + "Because the question answerer in QA-DQN is a pointing model, its performance relies heavily on whether the agent can find and stop at the sentence that contains the answer. We design a heuristic reward to encourage and guide this behavior. In particular, we assign a reward if the agent halts at game step $k$ and the answer is a sub-string of $o_k$ (if larger memory slots are used, we assign this reward if the answer is a sub-string of the memory at game step $k$). We denote this reward as the sufficient information reward, since, if an agent sees the answer, it should have a good chance of having gathered sufficient information for the question (although this is not guaranteed).", + "Note this sufficient information reward is part of the design of QA-DQN, whereas the question answering score is the only metric used to evaluate an agent's performance on the iMRC task." + ], + [ + "As mentioned above, an agent might bypass Ctrl+F actions and explore an iMRC game only via next commands. We study this possibility in an ablation study, where we limit the agent to the Ctrl+F and stop commands. In this setting, an agent is forced to explore by means of search a queries." + ], + [ + "In this section, we describe our training strategy. We split the training pipeline into two parts for easy comprehension. We use Adam BIBREF22 as the step rule for optimization in both parts, with the learning rate set to 0.00025." + ], + [ + "iMRC games are interactive environments. We use an RL training algorithm to train the interactive information-gathering behavior of QA-DQN. We adopt the Rainbow algorithm proposed by BIBREF23, which integrates several extensions to the original Deep Q-Learning algorithm BIBREF24. Rainbox exhibits state-of-the-art performance on several RL benchmark tasks (e.g., Atari games).", + "During game playing, we use a mini-batch of size 10 and push all transitions (observation string, question string, generated command, reward) into a replay buffer of size 500,000. We do not compute losses directly using these transitions. After every 5 game steps, we randomly sample a mini-batch of 64 transitions from the replay buffer, compute loss, and update the network.", + "Detailed hyper-parameter settings for action generation are shown in Table TABREF38." + ], + [ + "Similarly, we use another replay buffer to store question answering transitions (observation string when interaction stops, question string, ground-truth answer).", + "Because both iSQuAD and iNewsQA are converted from datasets that provide ground-truth answer positions, we can leverage this information and train the question answerer with supervised learning. Specifically, we only push question answering transitions when the ground-truth answer is in the observation string. For each transition, we convert the ground-truth answer head- and tail-positions from the SQuAD and NewsQA datasets to positions in the current observation string. After every 5 game steps, we randomly sample a mini-batch of 64 transitions from the replay buffer and train the question answerer using the Negative Log-Likelihood (NLL) loss. We use a dropout rate of 0.1." + ], + [ + "In this study, we focus on three factors and their effects on iMRC and the performance of the QA-DQN agent:", + "different Ctrl+F strategies, as described in the action space section;", + "enabled vs. disabled next and previous actions;", + "different memory slot sizes.", + "Below we report the baseline agent's training performance followed by its generalization performance on test data." + ], + [ + "It remains difficult for RL agents to master multiple games at the same time. In our case, each document-question pair can be considered a unique game, and there are hundred of thousands of them. Therefore, as is common practice in the RL literature, we study an agent's training curves.", + "Due to the space limitations, we select several representative settings to discuss in this section and provide QA-DQN's training and evaluation curves for all experimental settings in the Appendix. We provide the agent's sufficient information rewards (i.e., if the agent stopped at a state where the observation contains the answer) during training in Appendix as well.", + "Figure FIGREF36 shows QA-DQN's training performance ($\\text{F}_1$ score) when next and previous actions are available. Figure FIGREF40 shows QA-DQN's training performance ($\\text{F}_1$ score) when next and previous actions are disabled. Note that all training curves are averaged over 3 runs with different random seeds and all evaluation curves show the one run with max validation performance among the three.", + "From Figure FIGREF36, we can see that the three Ctrl+F strategies show similar difficulty levels when next and previous are available, although QA-DQN works slightly better when selecting a word from the question as query (especially on iNewsQA). However, from Figure FIGREF40 we observe that when next and previous are disabled, QA-DQN shows significant advantage when selecting a word from the question as query. This may due to the fact that when an agent must use Ctrl+F to navigate within documents, the set of question words is a much smaller action space in contrast to the other two settings. In the 4-action setting, an agent can rely on issuing next and previous actions to reach any sentence in a document.", + "The effect of action space size on model performance is particularly clear when using a datasets' entire vocabulary as query candidates in the 2-action setting. From Figure FIGREF40 (and figures with sufficient information rewards in the Appendix) we see QA-DQN has a hard time learning in this setting. As shown in Table TABREF16, both datasets have a vocabulary size of more than 100k. This is much larger than in the other two settings, where on average the length of questions is around 10. This suggests that the methods with better sample efficiency are needed to act in more realistic problem settings with huge action spaces.", + "Experiments also show that a larger memory slot size always helps. Intuitively, with a memory mechanism (either implicit or explicit), an agent could make the environment closer to fully observed by exploring and memorizing observations. Presumably, a larger memory may further improve QA-DQN's performance, but considering the average number of sentences in each iSQuAD game is 5, a memory with more than 5 slots will defeat the purpose of our study of partially observable text environments.", + "Not surprisingly, QA-DQN performs worse in general on iNewsQA, in all experiments. As shown in Table TABREF16, the average number of sentences per document in iNewsQA is about 6 times more than in iSQuAD. This is analogous to games with larger maps in the RL literature, where the environment is partially observable. A better exploration (in our case, jumping) strategy may help QA-DQN to master such harder games." + ], + [ + "To study QA-DQN's ability to generalize, we select the best performing agent in each experimental setting on the validation set and report their performance on the test set. The agent's test performance is reported in Table TABREF41. In addition, to support our claim that the challenging part of iMRC tasks is information seeking rather than answering questions given sufficient information, we also report the $\\text{F}_1$ score of an agent when it has reached the piece of text that contains the answer, which we denote as $\\text{F}_{1\\text{info}}$.", + "From Table TABREF41 (and validation curves provided in appendix) we can observe that QA-DQN's performance during evaluation matches its training performance in most settings. $\\text{F}_{1\\text{info}}$ scores are consistently higher than the overall $\\text{F}_1$ scores, and they have much less variance across different settings. This supports our hypothesis that information seeking play an important role in solving iMRC tasks, whereas question answering given necessary information is relatively straightforward. This also suggests that an interactive agent that can better navigate to important sentences is very likely to achieve better performance on iMRC tasks." + ], + [ + "In this work, we propose and explore the direction of converting MRC datasets into interactive environments. We believe interactive, information-seeking behavior is desirable for neural MRC systems when knowledge sources are partially observable and/or too large to encode in their entirety \u2014 for instance, when searching for information on the internet, where knowledge is by design easily accessible to humans through interaction.", + "Despite being restricted, our proposed task presents major challenges to existing techniques. iMRC lies at the intersection of NLP and RL, which is arguably less studied in existing literature. We hope to encourage researchers from both NLP and RL communities to work toward solving this task.", + "For our baseline, we adopted an off-the-shelf, top-performing MRC model and RL method. Either component can be replaced straightforwardly with other methods (e.g., to utilize a large-scale pretrained language model).", + "Our proposed setup and baseline agent presently use only a single word with the query command. However, a host of other options should be considered in future work. For example, multi-word queries with fuzzy matching are more realistic. It would also be interesting for an agent to generate a vector representation of the query in some latent space. This vector could then be compared with precomputed document representations (e.g., in an open domain QA dataset) to determine what text to observe next, with such behavior tantamount to learning to do IR.", + "As mentioned, our idea for reformulating existing MRC datasets as partially observable and interactive environments is straightforward and general. Almost all MRC datasets can be used to study interactive, information-seeking behavior through similar modifications. We hypothesize that such behavior can, in turn, help in solving real-world MRC problems involving search." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0338/instruction.md b/qasper-0338/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..c9b94f206b51da286c47aa9c0f084ec6a83f3a04 --- /dev/null +++ b/qasper-0338/instruction.md @@ -0,0 +1,161 @@ +Name of Paper: Interactive Machine Comprehension with Information Seeking Agents + +Question: What commands does their setup provide to models seeking information? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Works", + "iMRC: Making MRC Interactive", + "iMRC: Making MRC Interactive ::: Interactive MRC as a POMDP", + "iMRC: Making MRC Interactive ::: Action Space", + "iMRC: Making MRC Interactive ::: Query Types", + "iMRC: Making MRC Interactive ::: Evaluation Metric", + "Baseline Agent", + "Baseline Agent ::: Model Structure", + "Baseline Agent ::: Model Structure ::: Encoder", + "Baseline Agent ::: Model Structure ::: Action Generator", + "Baseline Agent ::: Model Structure ::: Question Answerer", + "Baseline Agent ::: Memory and Reward Shaping ::: Memory", + "Baseline Agent ::: Memory and Reward Shaping ::: Reward Shaping", + "Baseline Agent ::: Memory and Reward Shaping ::: Ctrl+F Only Mode", + "Baseline Agent ::: Training Strategy", + "Baseline Agent ::: Training Strategy ::: Action Generation", + "Baseline Agent ::: Training Strategy ::: Question Answering", + "Experimental Results", + "Experimental Results ::: Mastering Training Games", + "Experimental Results ::: Generalizing to Test Set", + "Discussion and Future Work" + ], + "paragraphs": [ + [ + "Many machine reading comprehension (MRC) datasets have been released in recent years BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4 to benchmark a system's ability to understand and reason over natural language. Typically, these datasets require an MRC model to read through a document to answer a question about information contained therein.", + "The supporting document is, more often than not, static and fully observable. This raises concerns, since models may find answers simply through shallow pattern matching; e.g., syntactic similarity between the words in questions and documents. As pointed out by BIBREF5, for questions starting with when, models tend to predict the only date/time answer in the supporting document. Such behavior limits the generality and usefulness of MRC models, and suggests that they do not learn a proper `understanding' of the intended task. In this paper, to address this problem, we shift the focus of MRC data away from `spoon-feeding' models with sufficient information in fully observable, static documents. Instead, we propose interactive versions of existing MRC tasks, whereby the information needed to answer a question must be gathered sequentially.", + "The key idea behind our proposed interactive MRC (iMRC) is to restrict the document context that a model observes at one time. Concretely, we split a supporting document into its component sentences and withhold these sentences from the model. Given a question, the model must issue commands to observe sentences in the withheld set; we equip models with actions such as Ctrl+F (search for token) and stop for searching through partially observed documents. A model searches iteratively, conditioning each command on the input question and the sentences it has observed previously. Thus, our task requires models to `feed themselves' rather than spoon-feeding them with information. This casts MRC as a sequential decision-making problem amenable to reinforcement learning (RL).", + "As an initial case study, we repurpose two well known, related corpora with different difficulty levels for our interactive MRC task: SQuAD and NewsQA. Table TABREF2 shows some examples of a model performing interactive MRC on these datasets. Naturally, our reframing makes the MRC problem harder; however, we believe the added demands of iMRC more closely match web-level QA and may lead to deeper comprehension of documents' content.", + "The main contributions of this work are as follows:", + "We describe a method to make MRC datasets interactive and formulate the new task as an RL problem.", + "We develop a baseline agent that combines a top performing MRC model and a state-of-the-art RL optimization algorithm and test it on our iMRC tasks.", + "We conduct experiments on several variants of iMRC and discuss the significant challenges posed by our setting." + ], + [ + "Skip-reading BIBREF6, BIBREF7, BIBREF8 is an existing setting in which MRC models read partial documents. Concretely, these methods assume that not all tokens in the input sequence are useful, and therefore learn to skip irrelevant tokens based on the current input and their internal memory. Since skipping decisions are discrete, the models are often optimized by the REINFORCE algorithm BIBREF9. For example, the structural-jump-LSTM proposed in BIBREF10 learns to skip and jump over chunks of text. In a similar vein, BIBREF11 designed a QA task where the model reads streaming data unidirectionally, without knowing when the question will be provided. Skip-reading approaches are limited in that they only consider jumping over a few consecutive tokens and the skipping operations are usually unidirectional. Based on the assumption that a single pass of reading may not provide sufficient information, multi-pass reading methods have also been studied BIBREF12, BIBREF13.", + "Compared to skip-reading and multi-turn reading, our work enables an agent to jump through a document in a more dynamic manner, in some sense combining aspects of skip-reading and re-reading. For example, it can jump forward, backward, or to an arbitrary position, depending on the query. This also distinguishes the model we develop in this work from ReasoNet BIBREF13, where an agent decides when to stop unidirectional reading.", + "Recently, BIBREF14 propose DocQN, which is a DQN-based agent that leverages the (tree) structure of documents and navigates across sentences and paragraphs. The proposed method has been shown to outperform vanilla DQN and IR baselines on TriviaQA dataset. The main differences between our work and DocQA include: iMRC does not depend on extra meta information of documents (e.g., title, paragraph title) for building document trees as in DocQN; our proposed environment is partially-observable, and thus an agent is required to explore and memorize the environment via interaction; the action space in our setting (especially for the Ctrl+F command as defined in later section) is arguably larger than the tree sampling action space in DocQN.", + "Closely related to iMRC is work by BIBREF15, in which the authors introduce a collection of synthetic tasks to train and test information-seeking capabilities in neural models. We extend that work by developing a realistic and challenging text-based task.", + "Broadly speaking, our approach is also linked to the optimal stopping problem in the literature Markov decision processes (MDP) BIBREF16, where at each time-step the agent either continues or stops and accumulates reward. Here, we reformulate conventional QA tasks through the lens of optimal stopping, in hopes of improving over the shallow matching behaviors exhibited by many MRC systems." + ], + [ + "We build the iSQuAD and iNewsQA datasets based on SQuAD v1.1 BIBREF0 and NewsQA BIBREF1. Both original datasets share similar properties. Specifically, every data-point consists of a tuple, $\\lbrace p, q, a\\rbrace $, where $p$ represents a paragraph, $q$ a question, and $a$ is the answer. The answer is a word span defined by head and tail positions in $p$. NewsQA is more difficult than SQuAD because it has a larger vocabulary, more difficult questions, and longer source documents.", + "We first split every paragraph $p$ into a list of sentences $\\mathcal {S} = \\lbrace s_1, s_2, ..., s_n\\rbrace $, where $n$ stands for number of sentences in $p$. Given a question $q$, rather than showing the entire paragraph $p$, we only show an agent the first sentence $s_1$ and withhold the rest. The agent must issue commands to reveal the hidden sentences progressively and thereby gather the information needed to answer question $q$.", + "An agent decides when to stop interacting and output an answer, but the number of interaction steps is limited. Once an agent has exhausted its step budget, it is forced to answer the question." + ], + [ + "As described in the previous section, we convert MRC tasks into sequential decision-making problems (which we will refer to as games). These can be described naturally within the reinforcement learning (RL) framework. Formally, tasks in iMRC are partially observable Markov decision processes (POMDP) BIBREF17. An iMRC data-point is a discrete-time POMDP defined by $(S, T, A, \\Omega , O, R, \\gamma )$, where $\\gamma \\in [0, 1]$ is the discount factor and the other elements are described in detail below.", + "Environment States ($S$): The environment state at turn $t$ in the game is $s_t \\in S$. It contains the complete internal information of the game, much of which is hidden from the agent. When an agent issues an action $a_t$, the environment transitions to state $s_{t+1}$ with probability $T(s_{t+1} | s_t, a_t)$). In this work, transition probabilities are either 0 or 1 (i.e., deterministic environment).", + "Actions ($A$): At each game turn $t$, the agent issues an action $a_t \\in A$. We will elaborate on the action space of iMRC in the action space section.", + "Observations ($\\Omega $): The text information perceived by the agent at a given game turn $t$ is the agent's observation, $o_t \\in \\Omega $, which depends on the environment state and the previous action with probability $O(o_t|s_t)$. In this work, observation probabilities are either 0 or 1 (i.e., noiseless observation). Reward Function ($R$): Based on its actions, the agent receives rewards $r_t = R(s_t, a_t)$. Its objective is to maximize the expected discounted sum of rewards $E \\left[\\sum _t \\gamma ^t r_t \\right]$." + ], + [ + "To better describe the action space of iMRC, we split an agent's actions into two phases: information gathering and question answering. During the information gathering phase, the agent interacts with the environment to collect knowledge. It answers questions with its accumulated knowledge in the question answering phase.", + "Information Gathering: At step $t$ of the information gathering phase, the agent can issue one of the following four actions to interact with the paragraph $p$, where $p$ consists of $n$ sentences and where the current observation corresponds to sentence $s_k,~1 \\le k \\le n$:", + "previous: jump to $ \\small {\\left\\lbrace \\begin{array}{ll} s_n & \\text{if $k = 1$,}\\\\ s_{k-1} & \\text{otherwise;} \\end{array}\\right.} $", + "next: jump to $ \\small {\\left\\lbrace \\begin{array}{ll} s_1 & \\text{if $k = n$,}\\\\ s_{k+1} & \\text{otherwise;} \\end{array}\\right.} $", + "Ctrl+F $<$query$>$: jump to the sentence that contains the next occurrence of \u201cquery\u201d;", + "stop: terminate information gathering phase.", + "Question Answering: We follow the output format of both SQuAD and NewsQA, where an agent is required to point to the head and tail positions of an answer span within $p$. Assume that at step $t$ the agent stops interacting and the observation $o_t$ is $s_k$. The agent points to a head-tail position pair in $s_k$." + ], + [ + "Given the question \u201cWhen is the deadline of AAAI?\u201d, as a human, one might try searching \u201cAAAI\u201d on a search engine, follow the link to the official AAAI website, then search for keywords \u201cdeadline\u201d or \u201cdue date\u201d on the website to jump to a specific paragraph. Humans have a deep understanding of questions because of their significant background knowledge. As a result, the keywords they use to search are not limited to what appears in the question.", + "Inspired by this observation, we study 3 query types for the Ctrl+F $<$query$>$ command.", + "One token from the question: the setting with smallest action space. Because iMRC deals with Ctrl+F commands by exact string matching, there is no guarantee that all sentences are accessible from question tokens only.", + "One token from the union of the question and the current observation: an intermediate level where the action space is larger.", + "One token from the dataset vocabulary: the action space is huge (see Table TABREF16 for statistics of SQuAD and NewsQA). It is guaranteed that all sentences in all documents are accessible through these tokens." + ], + [ + "Since iMRC involves both MRC and RL, we adopt evaluation metrics from both settings. First, as a question answering task, we use $\\text{F}_1$ score to compare predicted answers against ground-truth, as in previous works. When there exist multiple ground-truth answers, we report the max $\\text{F}_1$ score. Second, mastering multiple games remains quite challenging for RL agents. Therefore, we evaluate an agent's performance during both its training and testing phases. During training, we report training curves averaged over 3 random seeds. During test, we follow common practice in supervised learning tasks where we report the agent's test performance corresponding to its best validation performance ." + ], + [ + "As a baseline, we propose QA-DQN, an agent that adopts components from QANet BIBREF18 and adds an extra command generation module inspired by LSTM-DQN BIBREF19.", + "As illustrated in Figure FIGREF6, the agent consists of three components: an encoder, an action generator, and a question answerer. More precisely, at a game step $t$, the encoder reads observation string $o_t$ and question string $q$ to generate attention aggregated hidden representations $M_t$. Using $M_t$, the action generator outputs commands (defined in previous sections) to interact with iMRC. If the generated command is stop or the agent is forced to stop, the question answerer takes the current information at game step $t$ to generate head and tail pointers for answering the question; otherwise, the information gathering procedure continues.", + "In this section, we describe the high-level model structure and training strategies of QA-DQN. We refer readers to BIBREF18 for detailed information. We will release datasets and code in the near future." + ], + [ + "In this section, we use game step $t$ to denote one round of interaction between an agent with the iMRC environment. We use $o_t$ to denote text observation at game step $t$ and $q$ to denote question text. We use $L$ to refer to a linear transformation. $[\\cdot ;\\cdot ]$ denotes vector concatenation." + ], + [ + "The encoder consists of an embedding layer, two stacks of transformer blocks (denoted as encoder transformer blocks and aggregation transformer blocks), and an attention layer.", + "In the embedding layer, we aggregate both word- and character-level embeddings. Word embeddings are initialized by the 300-dimension fastText BIBREF20 vectors trained on Common Crawl (600B tokens), and are fixed during training. Character embeddings are initialized by 200-dimension random vectors. A convolutional layer with 96 kernels of size 5 is used to aggregate the sequence of characters. We use a max pooling layer on the character dimension, then a multi-layer perceptron (MLP) of size 96 is used to aggregate the concatenation of word- and character-level representations. A highway network BIBREF21 is used on top of this MLP. The resulting vectors are used as input to the encoding transformer blocks.", + "Each encoding transformer block consists of four convolutional layers (with shared weights), a self-attention layer, and an MLP. Each convolutional layer has 96 filters, each kernel's size is 7. In the self-attention layer, we use a block hidden size of 96 and a single head attention mechanism. Layer normalization and dropout are applied after each component inside the block. We add positional encoding into each block's input. We use one layer of such an encoding block.", + "At a game step $t$, the encoder processes text observation $o_t$ and question $q$ to generate context-aware encodings $h_{o_t} \\in \\mathbb {R}^{L^{o_t} \\times H_1}$ and $h_q \\in \\mathbb {R}^{L^{q} \\times H_1}$, where $L^{o_t}$ and $L^{q}$ denote length of $o_t$ and $q$ respectively, $H_1$ is 96.", + "Following BIBREF18, we use a context-query attention layer to aggregate the two representations $h_{o_t}$ and $h_q$. Specifically, the attention layer first uses two MLPs to map $h_{o_t}$ and $h_q$ into the same space, with the resulting representations denoted as $h_{o_t}^{\\prime } \\in \\mathbb {R}^{L^{o_t} \\times H_2}$ and $h_q^{\\prime } \\in \\mathbb {R}^{L^{q} \\times H_2}$, in which, $H_2$ is 96.", + "Then, a tri-linear similarity function is used to compute the similarities between each pair of $h_{o_t}^{\\prime }$ and $h_q^{\\prime }$ items:", + "where $\\odot $ indicates element-wise multiplication and $w$ is trainable parameter vector of size 96.", + "We apply softmax to the resulting similarity matrix $S$ along both dimensions, producing $S^A$ and $S^B$. Information in the two representations are then aggregated as", + "where $h_{oq}$ is aggregated observation representation.", + "On top of the attention layer, a stack of aggregation transformer blocks is used to further map the observation representations to action representations and answer representations. The configuration parameters are the same as the encoder transformer blocks, except there are two convolution layers (with shared weights), and the number of blocks is 7.", + "Let $M_t \\in \\mathbb {R}^{L^{o_t} \\times H_3}$ denote the output of the stack of aggregation transformer blocks, in which $H_3$ is 96." + ], + [ + "The action generator takes $M_t$ as input and estimates Q-values for all possible actions. As described in previous section, when an action is a Ctrl+F command, it is composed of two tokens (the token \u201cCtrl+F\u201d and the query token). Therefore, the action generator consists of three MLPs:", + "Here, the size of $L_{shared} \\in \\mathbb {R}^{95 \\times 150}$; $L_{action}$ has an output size of 4 or 2 depending on the number of actions available; the size of $L_{ctrlf}$ is the same as the size of a dataset's vocabulary size (depending on different query type settings, we mask out words in the vocabulary that are not query candidates). The overall Q-value is simply the sum of the two components:" + ], + [ + "Following BIBREF18, we append two extra stacks of aggregation transformer blocks on top of the encoder to compute head and tail positions:", + "Here, $M_{head}$ and $M_{tail}$ are outputs of the two extra transformer stacks, $L_0$, $L_1$, $L_2$ and $L_3$ are trainable parameters with output size 150, 150, 1 and 1, respectively." + ], + [ + "In iMRC, some questions may not be easily answerable based only on observation of a single sentence. To overcome this limitation, we provide an explicit memory mechanism to QA-DQN. Specifically, we use a queue to store strings that have been observed recently. The queue has a limited size of slots (we use queues of size [1, 3, 5] in this work). This prevents the agent from issuing next commands until the environment has been observed fully, in which case our task would degenerate to the standard MRC setting. The memory slots are reset episodically." + ], + [ + "Because the question answerer in QA-DQN is a pointing model, its performance relies heavily on whether the agent can find and stop at the sentence that contains the answer. We design a heuristic reward to encourage and guide this behavior. In particular, we assign a reward if the agent halts at game step $k$ and the answer is a sub-string of $o_k$ (if larger memory slots are used, we assign this reward if the answer is a sub-string of the memory at game step $k$). We denote this reward as the sufficient information reward, since, if an agent sees the answer, it should have a good chance of having gathered sufficient information for the question (although this is not guaranteed).", + "Note this sufficient information reward is part of the design of QA-DQN, whereas the question answering score is the only metric used to evaluate an agent's performance on the iMRC task." + ], + [ + "As mentioned above, an agent might bypass Ctrl+F actions and explore an iMRC game only via next commands. We study this possibility in an ablation study, where we limit the agent to the Ctrl+F and stop commands. In this setting, an agent is forced to explore by means of search a queries." + ], + [ + "In this section, we describe our training strategy. We split the training pipeline into two parts for easy comprehension. We use Adam BIBREF22 as the step rule for optimization in both parts, with the learning rate set to 0.00025." + ], + [ + "iMRC games are interactive environments. We use an RL training algorithm to train the interactive information-gathering behavior of QA-DQN. We adopt the Rainbow algorithm proposed by BIBREF23, which integrates several extensions to the original Deep Q-Learning algorithm BIBREF24. Rainbox exhibits state-of-the-art performance on several RL benchmark tasks (e.g., Atari games).", + "During game playing, we use a mini-batch of size 10 and push all transitions (observation string, question string, generated command, reward) into a replay buffer of size 500,000. We do not compute losses directly using these transitions. After every 5 game steps, we randomly sample a mini-batch of 64 transitions from the replay buffer, compute loss, and update the network.", + "Detailed hyper-parameter settings for action generation are shown in Table TABREF38." + ], + [ + "Similarly, we use another replay buffer to store question answering transitions (observation string when interaction stops, question string, ground-truth answer).", + "Because both iSQuAD and iNewsQA are converted from datasets that provide ground-truth answer positions, we can leverage this information and train the question answerer with supervised learning. Specifically, we only push question answering transitions when the ground-truth answer is in the observation string. For each transition, we convert the ground-truth answer head- and tail-positions from the SQuAD and NewsQA datasets to positions in the current observation string. After every 5 game steps, we randomly sample a mini-batch of 64 transitions from the replay buffer and train the question answerer using the Negative Log-Likelihood (NLL) loss. We use a dropout rate of 0.1." + ], + [ + "In this study, we focus on three factors and their effects on iMRC and the performance of the QA-DQN agent:", + "different Ctrl+F strategies, as described in the action space section;", + "enabled vs. disabled next and previous actions;", + "different memory slot sizes.", + "Below we report the baseline agent's training performance followed by its generalization performance on test data." + ], + [ + "It remains difficult for RL agents to master multiple games at the same time. In our case, each document-question pair can be considered a unique game, and there are hundred of thousands of them. Therefore, as is common practice in the RL literature, we study an agent's training curves.", + "Due to the space limitations, we select several representative settings to discuss in this section and provide QA-DQN's training and evaluation curves for all experimental settings in the Appendix. We provide the agent's sufficient information rewards (i.e., if the agent stopped at a state where the observation contains the answer) during training in Appendix as well.", + "Figure FIGREF36 shows QA-DQN's training performance ($\\text{F}_1$ score) when next and previous actions are available. Figure FIGREF40 shows QA-DQN's training performance ($\\text{F}_1$ score) when next and previous actions are disabled. Note that all training curves are averaged over 3 runs with different random seeds and all evaluation curves show the one run with max validation performance among the three.", + "From Figure FIGREF36, we can see that the three Ctrl+F strategies show similar difficulty levels when next and previous are available, although QA-DQN works slightly better when selecting a word from the question as query (especially on iNewsQA). However, from Figure FIGREF40 we observe that when next and previous are disabled, QA-DQN shows significant advantage when selecting a word from the question as query. This may due to the fact that when an agent must use Ctrl+F to navigate within documents, the set of question words is a much smaller action space in contrast to the other two settings. In the 4-action setting, an agent can rely on issuing next and previous actions to reach any sentence in a document.", + "The effect of action space size on model performance is particularly clear when using a datasets' entire vocabulary as query candidates in the 2-action setting. From Figure FIGREF40 (and figures with sufficient information rewards in the Appendix) we see QA-DQN has a hard time learning in this setting. As shown in Table TABREF16, both datasets have a vocabulary size of more than 100k. This is much larger than in the other two settings, where on average the length of questions is around 10. This suggests that the methods with better sample efficiency are needed to act in more realistic problem settings with huge action spaces.", + "Experiments also show that a larger memory slot size always helps. Intuitively, with a memory mechanism (either implicit or explicit), an agent could make the environment closer to fully observed by exploring and memorizing observations. Presumably, a larger memory may further improve QA-DQN's performance, but considering the average number of sentences in each iSQuAD game is 5, a memory with more than 5 slots will defeat the purpose of our study of partially observable text environments.", + "Not surprisingly, QA-DQN performs worse in general on iNewsQA, in all experiments. As shown in Table TABREF16, the average number of sentences per document in iNewsQA is about 6 times more than in iSQuAD. This is analogous to games with larger maps in the RL literature, where the environment is partially observable. A better exploration (in our case, jumping) strategy may help QA-DQN to master such harder games." + ], + [ + "To study QA-DQN's ability to generalize, we select the best performing agent in each experimental setting on the validation set and report their performance on the test set. The agent's test performance is reported in Table TABREF41. In addition, to support our claim that the challenging part of iMRC tasks is information seeking rather than answering questions given sufficient information, we also report the $\\text{F}_1$ score of an agent when it has reached the piece of text that contains the answer, which we denote as $\\text{F}_{1\\text{info}}$.", + "From Table TABREF41 (and validation curves provided in appendix) we can observe that QA-DQN's performance during evaluation matches its training performance in most settings. $\\text{F}_{1\\text{info}}$ scores are consistently higher than the overall $\\text{F}_1$ scores, and they have much less variance across different settings. This supports our hypothesis that information seeking play an important role in solving iMRC tasks, whereas question answering given necessary information is relatively straightforward. This also suggests that an interactive agent that can better navigate to important sentences is very likely to achieve better performance on iMRC tasks." + ], + [ + "In this work, we propose and explore the direction of converting MRC datasets into interactive environments. We believe interactive, information-seeking behavior is desirable for neural MRC systems when knowledge sources are partially observable and/or too large to encode in their entirety \u2014 for instance, when searching for information on the internet, where knowledge is by design easily accessible to humans through interaction.", + "Despite being restricted, our proposed task presents major challenges to existing techniques. iMRC lies at the intersection of NLP and RL, which is arguably less studied in existing literature. We hope to encourage researchers from both NLP and RL communities to work toward solving this task.", + "For our baseline, we adopted an off-the-shelf, top-performing MRC model and RL method. Either component can be replaced straightforwardly with other methods (e.g., to utilize a large-scale pretrained language model).", + "Our proposed setup and baseline agent presently use only a single word with the query command. However, a host of other options should be considered in future work. For example, multi-word queries with fuzzy matching are more realistic. It would also be interesting for an agent to generate a vector representation of the query in some latent space. This vector could then be compared with precomputed document representations (e.g., in an open domain QA dataset) to determine what text to observe next, with such behavior tantamount to learning to do IR.", + "As mentioned, our idea for reformulating existing MRC datasets as partially observable and interactive environments is straightforward and general. Almost all MRC datasets can be used to study interactive, information-seeking behavior through similar modifications. We hypothesize that such behavior can, in turn, help in solving real-world MRC problems involving search." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0339/instruction.md b/qasper-0339/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..6878c365f17b6b685fe9c387c83ea6f44f409bdc --- /dev/null +++ b/qasper-0339/instruction.md @@ -0,0 +1,106 @@ +Name of Paper: Exploring Hate Speech Detection in Multimodal Publications + +Question: What models do they propose? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work ::: Hate Speech Detection", + "Related Work ::: Visual and Textual Data Fusion", + "The MMHS150K dataset", + "The MMHS150K dataset ::: Tweets Gathering", + "The MMHS150K dataset ::: Textual Image Filtering", + "The MMHS150K dataset ::: Annotation", + "Methodology ::: Unimodal Treatment ::: Images.", + "Methodology ::: Unimodal Treatment ::: Tweet Text.", + "Methodology ::: Unimodal Treatment ::: Image Text.", + "Methodology ::: Multimodal Architectures", + "Methodology ::: Multimodal Architectures ::: Feature Concatenation Model (FCM)", + "Methodology ::: Multimodal Architectures ::: Spatial Concatenation Model (SCM)", + "Methodology ::: Multimodal Architectures ::: Textual Kernels Model (TKM)", + "Methodology ::: Multimodal Architectures ::: Training", + "Results", + "Conclusions" + ], + "paragraphs": [ + [ + "Social Media platforms such as Facebook, Twitter or Reddit have empowered individuals' voices and facilitated freedom of expression. However they have also been a breeding ground for hate speech and other types of online harassment. Hate speech is defined in legal literature as speech (or any form of expression) that expresses (or seeks to promote, or has the capacity to increase) hatred against a person or a group of people because of a characteristic they share, or a group to which they belong BIBREF0. Twitter develops this definition in its hateful conduct policy as violence against or directly attack or threaten other people on the basis of race, ethnicity, national origin, sexual orientation, gender, gender identity, religious affiliation, age, disability, or serious disease.", + "In this work we focus on hate speech detection. Due to the inherent complexity of this task, it is important to distinguish hate speech from other types of online harassment. In particular, although it might be offensive to many people, the sole presence of insulting terms does not itself signify or convey hate speech. And, the other way around, hate speech may denigrate or threaten an individual or a group of people without the use of any profanities. People from the african-american community, for example, often use the term nigga online, in everyday language, without malicious intentions to refer to folks within their community, and the word cunt is often used in non hate speech publications and without any sexist purpose. The goal of this work is not to discuss if racial slur, such as nigga, should be pursued. The goal is to distinguish between publications using offensive terms and publications attacking communities, which we call hate speech.", + "Modern social media content usually include images and text. Some of these multimodal publications are only hate speech because of the combination of the text with a certain image. That is because, as we have stated, the presence of offensive terms does not itself signify hate speech, and the presence of hate speech is often determined by the context of a publication. Moreover, users authoring hate speech tend to intentionally construct publications where the text is not enough to determine they are hate speech. This happens especially in Twitter, where multimodal tweets are formed by an image and a short text, which in many cases is not enough to judge them. In those cases, the image might give extra context to make a proper judgement. Fig. FIGREF5 shows some of such examples in MMHS150K.", + "The contributions of this work are as follows:", + "[noitemsep,leftmargin=*]", + "We propose the novel task of hate speech detection in multimodal publications, collect, annotate and publish a large scale dataset.", + "We evaluate state of the art multimodal models on this specific task and compare their performance with unimodal detection. Even though images are proved to be useful for hate speech detection, the proposed multimodal models do not outperform unimodal textual models.", + "We study the challenges of the proposed task, and open the field for future research." + ], + [ + "The literature on detecting hate speech on online textual publications is extensive. Schmidt and Wiegand BIBREF1 recently provided a good survey of it, where they review the terminology used over time, the features used, the existing datasets and the different approaches. However, the field lacks a consistent dataset and evaluation protocol to compare proposed methods. Saleem et al. BIBREF2 compare different classification methods detecting hate speech in Reddit and other forums. Wassem and Hovy BIBREF3 worked on hate speech detection on twitter, published a manually annotated dataset and studied its hate distribution. Later Wassem BIBREF4 extended the previous published dataset and compared amateur and expert annotations, concluding that amateur annotators are more likely than expert annotators to label items as hate speech. Park and Fung BIBREF5 worked on Wassem datasets and proposed a classification method using a CNN over Word2Vec BIBREF6 word embeddings, showing also classification results on racism and sexism hate sub-classes. Davidson et al. BIBREF7 also worked on hate speech detection on twitter, publishing another manually annotated dataset. They test different classifiers such as SVMs and decision trees and provide a performance comparison. Malmasi and Zampieri BIBREF8 worked on Davidson's dataset improving his results using more elaborated features. ElSherief et al. BIBREF9 studied hate speech on twitter and selected the most frequent terms in hate tweets based on Hatebase, a hate expression repository. They propose a big hate dataset but it lacks manual annotations, and all the tweets containing certain hate expressions are considered hate speech. Zhang et al. BIBREF10 recently proposed a more sophisticated approach for hate speech detection, using a CNN and a GRU BIBREF11 over Word2Vec BIBREF6 word embeddings. They show experiments in different datasets outperforming previous methods. Next, we summarize existing hate speech datasets:", + "[noitemsep,leftmargin=*]", + "RM BIBREF10: Formed by $2,435$ tweets discussing Refugees and Muslims, annotated as hate or non-hate.", + "DT BIBREF7: Formed by $24,783$ tweets annotated as hate, offensive language or neither. In our work, offensive language tweets are considered as non-hate.", + "WZ-LS BIBREF5: A combination of Wassem datasets BIBREF4, BIBREF3 labeled as racism, sexism, neither or both that make a total of $18,624$ tweets.", + "Semi-Supervised BIBREF9: Contains $27,330$ general hate speech Twitter tweets crawled in a semi-supervised manner.", + "Although often modern social media publications include images, not too many contributions exist that exploit visual information. Zhong et al. BIBREF12 worked on classifying Instagram images as potential cyberbullying targets, exploiting both the image content, the image caption and the comments. However, their visual information processing is limited to the use of features extracted by a pre-trained CNN, the use of which does not achieve any improvement. Hosseinmardi et al. BIBREF13 also address the problem of detecting cyberbullying incidents on Instagram exploiting both textual and image content. But, again, their visual information processing is limited to use the features of a pre-trained CNN, and the improvement when using visual features on cyberbullying classification is only of 0.01%." + ], + [ + "A typical task in multimodal visual and textual analysis is to learn an alignment between feature spaces. To do that, usually a CNN and a RNN are trained jointly to learn a joint embedding space from aligned multimodal data. This approach is applied in tasks such as image captioning BIBREF14, BIBREF15 and multimodal image retrieval BIBREF16, BIBREF17. On the other hand, instead of explicitly learning an alignment between two spaces, the goal of Visual Question Answering (VQA) is to merge both data modalities in order to decide which answer is correct. This problem requires modeling very precise correlations between the image and the question representations. The VQA task requirements are similar to our hate speech detection problem in multimodal publications, where we have a visual and a textual input and we need to combine both sources of information to understand the global context and make a decision. We thus take inspiration from the VQA literature for the tested models. Early VQA methods BIBREF18 fuse textual and visual information by feature concatenation. Later methods, such as Multimodal Compact Bilinear pooling BIBREF19, utilize bilinear pooling to learn multimodal features. An important limitation of these methods is that the multimodal features are fused in the latter model stage, so the textual and visual relationships are modeled only in the last layers. Another limitation is that the visual features are obtained by representing the output of the CNN as a one dimensional vector, which losses the spatial information of the input images. In a recent work, Gao et al. BIBREF20 propose a feature fusion scheme to overcome these limitations. They learn convolution kernels from the textual information \u2013which they call question-guided kernels\u2013 and convolve them with the visual information in an earlier stage to get the multimodal features. Margffoy-Tuay et al. BIBREF21 use a similar approach to combine visual and textual information, but they address a different task: instance segmentation guided by natural language queries. We inspire in these latest feature fusion works to build the models for hate speech detection." + ], + [ + "Existing hate speech datasets contain only textual data. Moreover, a reference benchmark does not exists. Most of the published datasets are crawled from Twitter and distributed as tweet IDs but, since Twitter removes reported user accounts, an important amount of their hate tweets is no longer accessible. We create a new manually annotated multimodal hate speech dataset formed by $150,000$ tweets, each one of them containing text and an image. We call the dataset MMHS150K, and made it available online . In this section, we explain the dataset creation steps." + ], + [ + "We used the Twitter API to gather real-time tweets from September 2018 until February 2019, selecting the ones containing any of the 51 Hatebase terms that are more common in hate speech tweets, as studied in BIBREF9. We filtered out retweets, tweets containing less than three words and tweets containing porn related terms. From that selection, we kept the ones that included images and downloaded them. Twitter applies hate speech filters and other kinds of content control based on its policy, although the supervision is based on users' reports. Therefore, as we are gathering tweets from real-time posting, the content we get has not yet passed any filter." + ], + [ + "We aim to create a multimodal hate speech database where all the instances contain visual and textual information that we can later process to determine if a tweet is hate speech or not. But a considerable amount of the images of the selected tweets contain only textual information, such as screenshots of other tweets. To ensure that all the dataset instances contain both visual and textual information, we remove those tweets. To do that, we use TextFCN BIBREF22, BIBREF23 , a Fully Convolutional Network that produces a pixel wise text probability map of an image. We set empirical thresholds to discard images that have a substantial total text probability, filtering out $23\\%$ of the collected tweets." + ], + [ + "We annotate the gathered tweets using the crowdsourcing platform Amazon Mechanical Turk. There, we give the workers the definition of hate speech and show some examples to make the task clearer. We then show the tweet text and image and we ask them to classify it in one of 6 categories: No attacks to any community, racist, sexist, homophobic, religion based attacks or attacks to other communities. Each one of the $150,000$ tweets is labeled by 3 different workers to palliate discrepancies among workers.", + "We received a lot of valuable feedback from the annotators. Most of them had understood the task correctly, but they were worried because of its subjectivity. This is indeed a subjective task, highly dependent on the annotator convictions and sensitivity. However, we expect to get cleaner annotations the more strong the attack is, which are the publications we are more interested on detecting. We also detected that several users annotate tweets for hate speech just by spotting slur. As already said previously, just the use of particular words can be offensive to many people, but this is not the task we aim to solve. We have not included in our experiments those hits that were made in less than 3 seconds, understanding that it takes more time to grasp the multimodal context and make a decision.", + "We do a majority voting between the three annotations to get the tweets category. At the end, we obtain $112,845$ not hate tweets and $36,978$ hate tweets. The latest are divided in $11,925$ racist, $3,495$ sexist, $3,870$ homophobic, 163 religion-based hate and $5,811$ other hate tweets (Fig. FIGREF17). In this work, we do not use hate sub-categories, and stick to the hate / not hate split. We separate balanced validation ($5,000$) and test ($10,000$) sets. The remaining tweets are used for training.", + "We also experimented using hate scores for each tweet computed given the different votes by the three annotators instead of binary labels. The results did not present significant differences to those shown in the experimental part of this work, but the raw annotations will be published nonetheless for further research.", + "As far as we know, this dataset is the biggest hate speech dataset to date, and the first multimodal hate speech dataset. One of its challenges is to distinguish between tweets using the same key offensive words that constitute or not an attack to a community (hate speech). Fig. FIGREF18 shows the percentage of hate and not hate tweets of the top keywords." + ], + [ + "All images are resized such that their shortest size has 500 pixels. During training, online data augmentation is applied as random cropping of $299\\times 299$ patches and mirroring. We use a CNN as the image features extractor which is an Imagenet BIBREF24 pre-trained Google Inception v3 architecture BIBREF25. The fine-tuning process of the Inception v3 layers aims to modify its weights to extract the features that, combined with the textual information, are optimal for hate speech detection." + ], + [ + "We train a single layer LSTM with a 150-dimensional hidden state for hate / not hate classification. The input dimensionality is set to 100 and GloVe BIBREF26 embeddings are used as word input representations. Since our dataset is not big enough to train a GloVe word embedding model, we used a pre-trained model that has been trained in two billion tweets. This ensures that the model will be able to produce word embeddings for slang and other words typically used in Twitter. To process the tweets text before generating the word embeddings, we use the same pipeline as the model authors, which includes generating symbols to encode Twitter special interactions such as user mentions (@user) or hashtags (#hashtag). To encode the tweet text and input it later to multimodal models, we use the LSTM hidden state after processing the last tweet word. Since the LSTM has been trained for hate speech classification, it extracts the most useful information for this task from the text, which is encoded in the hidden state after inputting the last tweet word." + ], + [ + "The text in the image can also contain important information to decide if a publication is hate speech or not, so we extract it and also input it to our model. To do so, we use Google Vision API Text Detection module BIBREF27. We input the tweet text and the text from the image separately to the multimodal models, so it might learn different relations between them and between them and the image. For instance, the model could learn to relate the image text with the area in the image where the text appears, so it could learn to interpret the text in a different way depending on the location where it is written in the image. The image text is also encoded by the LSTM as the hidden state after processing its last word." + ], + [ + "The objective of this work is to build a hate speech detector that leverages both textual and visual data and detects hate speech publications based on the context given by both data modalities. To study how the multimodal context can boost the performance compared to an unimodal context we evaluate different models: a Feature Concatenation Model (FCM), a Spatial Concatenation Model (SCM) and a Textual Kernels Model (TKM). All of them are CNN+RNN models with three inputs: the tweet image, the tweet text and the text appearing in the image (if any)." + ], + [ + "The image is fed to the Inception v3 architecture and the 2048 dimensional feature vector after the last average pooling layer is used as the visual representation. This vector is then concatenated with the 150 dimension vectors of the LSTM last word hidden states of the image text and the tweet text, resulting in a 2348 feature vector. This vector is then processed by three fully connected layers of decreasing dimensionality $(2348, 1024, 512)$ with following corresponding batch normalization and ReLu layers until the dimensions are reduced to two, the number of classes, in the last classification layer. The FCM architecture is illustrated in Fig. FIGREF26." + ], + [ + "Instead of using the latest feature vector before classification of the Inception v3 as the visual representation, in the SCM we use the $8\\times 8\\times 2048$ feature map after the last Inception module. Then we concatenate the 150 dimension vectors encoding the tweet text and the tweet image text at each spatial location of that feature map. The resulting multimodal feature map is processed by two Inception-E blocks BIBREF28. After that, dropout and average pooling are applied and, as in the FCM model, three fully connected layers are used to reduce the dimensionality until the classification layer." + ], + [ + "The TKM design, inspired by BIBREF20 and BIBREF21, aims to capture interactions between the two modalities more expressively than concatenation models. As in SCM we use the $8\\times 8\\times 2048$ feature map after the last Inception module as the visual representation. From the 150 dimension vector encoding the tweet text, we learn $K_t$ text dependent kernels using independent fully connected layers that are trained together with the rest of the model. The resulting $K_t$ text dependent kernels will have dimensionality of $1\\times 1\\times 2048$. We do the same with the feature vector encoding the image text, learning $K_{it}$ kernels. The textual kernels are convolved with the visual feature map in the channel dimension at each spatial location, resulting in a $8\\times 8\\times (K_i+K_{it})$ multimodal feature map, and batch normalization is applied. Then, as in the SCM, the 150 dimension vectors encoding the tweet text and the tweet image text are concatenated at each spatial dimension. The rest of the architecture is the same as in SCM: two Inception-E blocks, dropout, average pooling and three fully connected layers until the classification layer. The number of tweet textual kernels $K_t$ and tweet image textual kernels $K_it$ is set to $K_t = 10$ and $K_it = 5$. The TKM architecture is illustrated in Fig. FIGREF29." + ], + [ + "We train the multimodal models with a Cross-Entropy loss with Softmax activations and an ADAM optimizer with an initial learning rate of $1e-4$. Our dataset suffers from a high class imbalance, so we weight the contribution to the loss of the samples to totally compensate for it. One of the goals of this work is to explore how every one of the inputs contributes to the classification and to prove that the proposed model can learn concurrences between visual and textual data useful to improve the hate speech classification results on multimodal data. To do that we train different models where all or only some inputs are available. When an input is not available, we set it to zeros, and we do the same when an image has no text." + ], + [ + "Table TABREF31 shows the F-score, the Area Under the ROC Curve (AUC) and the mean accuracy (ACC) of the proposed models when different inputs are available. $TT$ refers to the tweet text, $IT$ to the image text and $I$ to the image. It also shows results for the LSTM, for the Davison method proposed in BIBREF7 trained with MMHS150K, and for random scores. Fig. FIGREF32 shows the Precision vs Recall plot and the ROC curve (which plots the True Positive Rate vs the False Positive Rate) of the different models.", + "First, notice that given the subjectivity of the task and the discrepancies between annotators, getting optimal scores in the evaluation metrics is virtually impossible. However, a system with relatively low metric scores can still be very useful for hate speech detection in a real application: it will fire on publications for which most annotators agree they are hate, which are often the stronger attacks. The proposed LSTM to detect hate speech when only text is available, gets similar results as the method presented in BIBREF7, which we trained with MMHS150K and the same splits. However, more than substantially advancing the state of the art on hate speech detection in textual publications, our key purpose in this work is to introduce and work on its detection on multimodal publications. We use LSTM because it provides a strong representation of the tweet texts.", + "The FCM trained only with images gets decent results, considering that in many publications the images might not give any useful information for the task. Fig. FIGREF33 shows some representative examples of the top hate and not hate scored images of this model. Many hate tweets are accompanied by demeaning nudity images, being sexist or homophobic. Other racist tweets are accompanied by images caricaturing black people. Finally, MEMES are also typically used in hate speech publications. The top scored images for not hate are portraits of people belonging to minorities. This is due to the use of slur inside these communities without an offensive intention, such as the word nigga inside the afro-american community or the word dyke inside the lesbian community. These results show that images can be effectively used to discriminate between offensive and non-offensive uses of those words.", + "Despite the model trained only with images proves that they are useful for hate speech detection, the proposed multimodal models are not able to improve the detection compared to the textual models. Besides the different architectures, we have tried different training strategies, such as initializing the CNN weights with a model already trained solely with MMHS150K images or using dropout to force the multimodal models to use the visual information. Eventually, though, these models end up using almost only the text input for the prediction and producing very similar results to those of the textual models. The proposed multimodal models, such as TKM, have shown good performance in other tasks, such as VQA. Next, we analyze why they do not perform well in this task and with this data:", + "[noitemsep,leftmargin=*]", + "Noisy data. A major challenge of this task is the discrepancy between annotations due to subjective judgement. Although this affects also detection using only text, its repercussion is bigger in more complex tasks, such as detection using images or multimodal detection.", + "Complexity and diversity of multimodal relations. Hate speech multimodal publications employ a lot of background knowledge which makes the relations between visual and textual elements they use very complex and diverse, and therefore difficult to learn by a neural network.", + "Small set of multimodal examples. Fig. FIGREF5 shows some of the challenging multimodal hate examples that we aimed to detect. But although we have collected a big dataset of $150K$ tweets, the subset of multimodal hate there is still too small to learn the complex multimodal relations needed to identify multimodal hate." + ], + [ + "In this work we have explored the task of hate speech detection on multimodal publications. We have created MMHS150K, to our knowledge the biggest available hate speech dataset, and the first one composed of multimodal data, namely tweets formed by image and text. We have trained different textual, visual and multimodal models with that data, and found out that, despite the fact that images are useful for hate speech detection, the multimodal models do not outperform the textual models. Finally, we have analyzed the challenges of the proposed task and dataset. Given that most of the content in Social Media nowadays is multimodal, we truly believe on the importance of pushing forward this research. The code used in this work is available in ." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0353/instruction.md b/qasper-0353/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..1aace4831a69a8b68b427e28d23863ad9c6a61b7 --- /dev/null +++ b/qasper-0353/instruction.md @@ -0,0 +1,134 @@ +Name of Paper: Self-Taught Convolutional Neural Networks for Short Text Clustering + +Question: Which popular clustering methods did they experiment with? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work", + "Short Text Clustering", + "Deep Neural Networks", + "Methodology", + "Deep Convolutional Neural Networks", + "Unsupervised Dimensionality Reduction", + "Learning", + "K-means for Clustering", + "Datasets", + "Pre-trained Word Vectors", + "Comparisons", + "Evaluation Metrics", + "Hyperparameter Settings", + "Results and Analysis", + "Conclusions", + "Acknowledgments" + ], + "paragraphs": [ + [ + "Short text clustering is of great importance due to its various applications, such as user profiling BIBREF0 and recommendation BIBREF1 , for nowaday's social media dataset emerged day by day. However, short text clustering has the data sparsity problem and most words only occur once in each short text BIBREF2 . As a result, the Term Frequency-Inverse Document Frequency (TF-IDF) measure cannot work well in short text setting. In order to address this problem, some researchers work on expanding and enriching the context of data from Wikipedia BIBREF3 or an ontology BIBREF4 . However, these methods involve solid Natural Language Processing (NLP) knowledge and still use high-dimensional representation which may result in a waste of both memory and computation time. Another way to overcome these issues is to explore some sophisticated models to cluster short texts. For example, Yin and Wang BIBREF5 proposed a Dirichlet multinomial mixture model-based approach for short text clustering. Yet how to design an effective model is an open question, and most of these methods directly trained based on Bag-of-Words (BoW) are shallow structures which cannot preserve the accurate semantic similarities.", + "Recently, with the help of word embedding, neural networks demonstrate their great performance in terms of constructing text representation, such as Recursive Neural Network (RecNN) BIBREF6 , BIBREF7 and Recurrent Neural Network (RNN) BIBREF8 . However, RecNN exhibits high time complexity to construct the textual tree, and RNN, using the hidden layer computed at the last word to represent the text, is a biased model where later words are more dominant than earlier words BIBREF9 . Whereas for the non-biased models, the learned representation of one text can be extracted from all the words in the text with non-dominant learned weights. More recently, Convolution Neural Network (CNN), as the most popular non-biased model and applying convolutional filters to capture local features, has achieved a better performance in many NLP applications, such as sentence modeling BIBREF10 , relation classification BIBREF11 , and other traditional NLP tasks BIBREF12 . Most of the previous works focus CNN on solving supervised NLP tasks, while in this paper we aim to explore the power of CNN on one unsupervised NLP task, short text clustering.", + "We systematically introduce a simple yet surprisingly powerful Self-Taught Convolutional neural network framework for Short Text Clustering, called STC INLINEFORM0 . An overall architecture of our proposed approach is illustrated in Figure FIGREF5 . We, inspired by BIBREF13 , BIBREF14 , utilize a self-taught learning framework into our task. In particular, the original raw text features are first embedded into compact binary codes INLINEFORM1 with the help of one traditional unsupervised dimensionality reduction function. Then text matrix INLINEFORM2 projected from word embeddings are fed into CNN model to learn the deep feature representation INLINEFORM3 and the output units are used to fit the pre-trained binary codes INLINEFORM4 . After obtaining the learned features, K-means algorithm is employed on them to cluster texts into clusters INLINEFORM5 . Obviously, we call our approach \u201cself-taught\u201d because the CNN model is learnt from the pseudo labels generated from the previous stage, which is quite different from the term \u201cself-taught\u201d in BIBREF15 . Our main contributions can be summarized as follows:", + "This work is an extension of our conference paper BIBREF16 , and they differ in the following aspects. First, we put forward a general a self-taught CNN framework in this paper which can flexibly couple various semantic features, whereas the conference version can be seen as a specific example of this work. Second, in this paper we use a new short text dataset, Biomedical, in the experiment to verify the effectiveness of our approach. Third, we put much effort on studying the influence of various different semantic features integrated in our self-taught CNN framework, which is not involved in the conference paper.", + "For the purpose of reproducibility, we make the datasets and software used in our experiments publicly available at the website.", + "The remainder of this paper is organized as follows: In Section SECREF2 , we first briefly survey several related works. In Section SECREF3 , we describe the proposed approach STC INLINEFORM0 and implementation details. Experimental results and analyses are presented in Section SECREF4 . Finally, conclusions are given in the last Section." + ], + [ + "In this section, we review the related work from the following two perspectives: short text clustering and deep neural networks." + ], + [ + "There have been several studies that attempted to overcome the sparseness of short text representation. One way is to expand and enrich the context of data. For example, Banerjee et al. BIBREF3 proposed a method of improving the accuracy of short text clustering by enriching their representation with additional features from Wikipedia, and Fodeh et al. BIBREF4 incorporate semantic knowledge from an ontology into text clustering. However, these works need solid NLP knowledge and still use high-dimensional representation which may result in a waste of both memory and computation time. Another direction is to map the original features into reduced space, such as Latent Semantic Analysis (LSA) BIBREF17 , Laplacian Eigenmaps (LE) BIBREF18 , and Locality Preserving Indexing (LPI) BIBREF19 . Even some researchers explored some sophisticated models to cluster short texts. For example, Yin and Wang BIBREF5 proposed a Dirichlet multinomial mixture model-based approach for short text clustering. Moreover, some studies even focus the above both two streams. For example, Tang et al. BIBREF20 proposed a novel framework which enrich the text features by employing machine translation and reduce the original features simultaneously through matrix factorization techniques.", + "Despite the above clustering methods can alleviate sparseness of short text representation to some extent, most of them ignore word order in the text and belong to shallow structures which can not fully capture accurate semantic similarities." + ], + [ + "Recently, there is a revival of interest in DNN and many researchers have concentrated on using Deep Learning to learn features. Hinton and Salakhutdinov BIBREF21 use DAE to learn text representation. During the fine-tuning procedure, they use backpropagation to find codes that are good at reconstructing the word-count vector.", + "More recently, researchers propose to use external corpus to learn a distributed representation for each word, called word embedding BIBREF22 , to improve DNN performance on NLP tasks. The Skip-gram and continuous bag-of-words models of Word2vec BIBREF23 propose a simple single-layer architecture based on the inner product between two word vectors, and Pennington et al. BIBREF24 introduce a new model for word representation, called GloVe, which captures the global corpus statistics.", + "In order to learn the compact representation vectors of sentences, Le and Mikolov BIBREF25 directly extend the previous Word2vec BIBREF23 by predicting words in the sentence, which is named Paragraph Vector (Para2vec). Para2vec is still a shallow window-based method and need a larger corpus to yield better performance. More neural networks utilize word embedding to capture true meaningful syntactic and semantic regularities, such as RecNN BIBREF6 , BIBREF7 and RNN BIBREF8 . However, RecNN exhibits high time complexity to construct the textual tree, and RNN, using the layer computed at the last word to represent the text, is a biased model. Recently, Long Short-Term Memory (LSTM) BIBREF26 and Gated Recurrent Unit (GRU) BIBREF27 , as sophisticated recurrent hidden units of RNN, has presented its advantages in many sequence generation problem, such as machine translation BIBREF28 , speech recognition BIBREF29 , and text conversation BIBREF30 . While, CNN is better to learn non-biased implicit features which has been successfully exploited for many supervised NLP learning tasks as described in Section SECREF1 , and various CNN based variants are proposed in the recent works, such as Dynamic Convolutional Neural Network (DCNN) BIBREF10 , Gated Recursive Convolutional Neural Network (grConv) BIBREF31 and Self-Adaptive Hierarchical Sentence model (AdaSent) BIBREF32 .", + "In the past few days, Visin et al. BIBREF33 have attempted to replace convolutional layer in CNN to learn non-biased features for object recognition with four RNNs, called ReNet, that sweep over lower-layer features in different directions: (1) bottom to top, (2) top to bottom, (3) left to right and (4) right to left. However, ReNet does not outperform state-of-the-art convolutional neural networks on any of the three benchmark datasets, and it is also a supervised learning model for classification. Inspired by Skip-gram of word2vec BIBREF34 , BIBREF23 , Skip-thought model BIBREF35 describe an approach for unsupervised learning of a generic, distributed sentence encoder. Similar as Skip-gram model, Skip-thought model trains an encoder-decoder model that tries to reconstruct the surrounding sentences of an encoded sentence and released an off-the-shelf encoder to extract sentence representation. Even some researchers introduce continuous Skip-gram and negative sampling to CNN for learning visual representation in an unsupervised manner BIBREF36 . This paper, from a new perspective, puts forward a general self-taught CNN framework which can flexibly couple various semantic features and achieve a good performance on one unsupervised learning task, short text clustering." + ], + [ + "Assume that we are given a dataset of INLINEFORM0 training texts denoted as: INLINEFORM1 , where INLINEFORM2 is the dimensionality of the original BoW representation. Denote its tag set as INLINEFORM3 and the pre-trained word embedding set as INLINEFORM4 , where INLINEFORM5 is the dimensionality of word vectors and INLINEFORM6 is the vocabulary size. In order to learn the INLINEFORM7 -dimensional deep feature representation INLINEFORM8 from CNN in an unsupervised manner, some unsupervised dimensionality reduction methods INLINEFORM9 are employed to guide the learning of CNN model. Our goal is to cluster these texts INLINEFORM10 into clusters INLINEFORM11 based on the learned deep feature representation while preserving the semantic consistency.", + "As depicted in Figure FIGREF5 , the proposed framework consist of three components, deep convolutional neural network (CNN), unsupervised dimensionality reduction function and K-means module. In the rest sections, we first present the first two components respectively, and then give the trainable parameters and the objective function to learn the deep feature representation. Finally, the last section describe how to perform clustering on the learned features." + ], + [ + "In this section, we briefly review one popular deep convolutional neural network, Dynamic Convolutional Neural Network (DCNN) BIBREF10 as an instance of CNN in the following sections, which as the foundation of our proposed method has been successfully proposed for the completely supervised learning task, text classification.", + "Taking a neural network with two convolutional layers in Figure FIGREF9 as an example, the network transforms raw input text to a powerful representation. Particularly, each raw text vector INLINEFORM0 is projected into a matrix representation INLINEFORM1 by looking up a word embedding INLINEFORM2 , where INLINEFORM3 is the length of one text. We also let INLINEFORM4 and INLINEFORM5 denote the weights of the neural networks. The network defines a transformation INLINEFORM6 INLINEFORM7 which transforms an input raw text INLINEFORM8 to a INLINEFORM9 -dimensional deep representation INLINEFORM10 . There are three basic operations described as follows:", + "Wide one-dimensional convolution This operation INLINEFORM0 is applied to an individual row of the sentence matrix INLINEFORM1 , and yields a resulting matrix INLINEFORM2 , where INLINEFORM3 is the width of convolutional filter.", + "Folding In this operation, every two rows in a feature map are simply summed component-wisely. For a map of INLINEFORM0 rows, folding returns a map of INLINEFORM1 rows, thus halving the size of the representation and yielding a matrix feature INLINEFORM2 . Note that folding operation does not introduce any additional parameters.", + "Dynamic INLINEFORM0 -max pooling Assuming the pooling parameter as INLINEFORM1 , INLINEFORM2 -max pooling selects the sub-matrix INLINEFORM3 of the INLINEFORM4 highest values in each row of the matrix INLINEFORM5 . For dynamic INLINEFORM6 -max pooling, the pooling parameter INLINEFORM7 is dynamically selected in order to allow for a smooth extraction of higher-order and longer-range features BIBREF10 . Given a fixed pooling parameter INLINEFORM8 for the topmost convolutional layer, the parameter INLINEFORM9 of INLINEFORM10 -max pooling in the INLINEFORM11 -th convolutional layer can be computed as follows: DISPLAYFORM0 ", + "where INLINEFORM0 is the total number of convolutional layers in the network." + ], + [ + "As described in Figure FIGREF5 , the dimensionality reduction function is defined as follows: DISPLAYFORM0 ", + "where, INLINEFORM0 are the INLINEFORM1 -dimensional reduced latent space representations. Here, we take four popular dimensionality reduction methods as examples in our framework.", + "Average Embedding (AE): This method directly averages the word embeddings which are respectively weighted with TF and TF-IDF. Huang et al. BIBREF37 used this strategy as the global context in their task, and Socher et al. BIBREF7 and Lai et al. BIBREF9 used this method for text classification. The weighted average of all word vectors in one text can be computed as follows: DISPLAYFORM0 ", + "where INLINEFORM0 can be any weighting function that captures the importance of word INLINEFORM1 in the text INLINEFORM2 .", + "Latent Semantic Analysis (LSA): LSA BIBREF17 is the most popular global matrix factorization method, which applies a dimension reducing linear projection, Singular Value Decomposition (SVD), of the corresponding term/document matrix. Suppose the rank of INLINEFORM0 is INLINEFORM1 , LSA decompose INLINEFORM2 into the product of three other matrices: DISPLAYFORM0 ", + "where INLINEFORM0 and INLINEFORM1 are the singular values of INLINEFORM2 , INLINEFORM3 is a set of left singular vectors and INLINEFORM4 is a set of right singular vectors. LSA uses the top INLINEFORM5 vectors in INLINEFORM6 as the transformation matrix to embed the original text features into a INLINEFORM7 -dimensional subspace INLINEFORM8 BIBREF17 .", + "Laplacian Eigenmaps (LE): The top eigenvectors of graph Laplacian, defined on the similarity matrix of texts, are used in the method, which can discover the manifold structure of the text space BIBREF18 . In order to avoid storing the dense similarity matrix, many approximation techniques are proposed to reduce the memory usage and computational complexity for LE. There are two representative approximation methods, sparse similarity matrix and Nystr INLINEFORM0 m approximation. Following previous studies BIBREF38 , BIBREF13 , we select the former technique to construct the INLINEFORM1 local similarity matrix INLINEFORM2 by using heat kernel as follows: DISPLAYFORM0 ", + "where, INLINEFORM0 is a tuning parameter (default is 1) and INLINEFORM1 represents the set of INLINEFORM2 -nearest-neighbors of INLINEFORM3 . By introducing a diagonal INLINEFORM4 matrix INLINEFORM5 whose entries are given by INLINEFORM6 , the graph Laplacian INLINEFORM7 can be computed by ( INLINEFORM8 ). The optimal INLINEFORM9 real-valued matrix INLINEFORM10 can be obtained by solving the following objective function: DISPLAYFORM0 ", + "where INLINEFORM0 is the trace function, INLINEFORM1 requires the different dimensions to be uncorrelated, and INLINEFORM2 requires each dimension to achieve equal probability as positive or negative).", + "Locality Preserving Indexing (LPI): This method extends LE to deal with unseen texts by approximating the linear function INLINEFORM0 BIBREF13 , and the subspace vectors are obtained by finding the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the Riemannian manifold BIBREF19 . Similar as LE, we first construct the local similarity matrix INLINEFORM1 , then the graph Laplacian INLINEFORM2 can be computed by ( INLINEFORM3 ), where INLINEFORM4 measures the local density around INLINEFORM5 and is equal to INLINEFORM6 . Compute the eigenvectors INLINEFORM7 and eigenvalues INLINEFORM8 of the following generalized eigen-problem: DISPLAYFORM0 ", + "The mapping function INLINEFORM0 can be obtained and applied to the unseen data BIBREF38 .", + "All of the above methods claim a better performance in capturing semantic similarity between texts in the reduced latent space representation INLINEFORM0 than in the original representation INLINEFORM1 , while the performance of short text clustering can be further enhanced with the help of our framework, self-taught CNN." + ], + [ + "The last layer of CNN is an output layer as follows: DISPLAYFORM0 ", + "where, INLINEFORM0 is the deep feature representation, INLINEFORM1 is the output vector and INLINEFORM2 is weight matrix.", + "In order to incorporate the latent semantic features INLINEFORM0 , we first binary the real-valued vectors INLINEFORM1 to the binary codes INLINEFORM2 by setting the threshold to be the media vector INLINEFORM3 . Then, the output vector INLINEFORM4 is used to fit the binary codes INLINEFORM5 via INLINEFORM6 logistic operations as follows: DISPLAYFORM0 ", + "All parameters to be trained are defined as INLINEFORM0 . DISPLAYFORM0 ", + "Given the training text collection INLINEFORM0 , and the pre-trained binary codes INLINEFORM1 , the log likelihood of the parameters can be written down as follows: DISPLAYFORM0 ", + "Following the previous work BIBREF10 , we train the network with mini-batches by back-propagation and perform the gradient-based optimization using the Adagrad update rule BIBREF39 . For regularization, we employ dropout with 50% rate to the penultimate layer BIBREF10 , BIBREF40 ." + ], + [ + "With the given short texts, we first utilize the trained deep neural network to obtain the semantic representations INLINEFORM0 , and then employ traditional K-means algorithm to perform clustering." + ], + [ + "We test our proposed approach on three public short text datasets. The summary statistics and semantic topics of these datasets are described in Table TABREF24 and Table TABREF25 .", + "SearchSnippets. This dataset was selected from the results of web search transaction using predefined phrases of 8 different domains by Phan et al. BIBREF41 .", + "StackOverflow. We use the challenge data published in Kaggle.com. The raw dataset consists 3,370,528 samples through July 31st, 2012 to August 14, 2012. In our experiments, we randomly select 20,000 question titles from 20 different tags as in Table TABREF25 .", + "Biomedical. We use the challenge data published in BioASQ's official website. In our experiments, we randomly select 20, 000 paper titles from 20 different MeSH major topics as in Table TABREF25 . As described in Table TABREF24 , the max length of selected paper titles is 53.", + "For these datasets, we randomly select 10% of data as the development set. Since SearchSnippets has been pre-processed by Phan et al. BIBREF41 , we do not further process this dataset. In StackOverflow, texts contain lots of computer terminology, and symbols and capital letters are meaningful, thus we do not do any pre-processed procedures. For Biomedical, we remove the symbols and convert letters into lower case." + ], + [ + "We use the publicly available word2vec tool to train word embeddings, and the most parameters are set as same as Mikolov et al. BIBREF23 to train word vectors on Google News setting, except of vector dimensionality using 48 and minimize count using 5. For SearchSnippets, we train word vectors on Wikipedia dumps. For StackOverflow, we train word vectors on the whole corpus of the StackOverflow dataset described above which includes the question titles and post contents. For Biomedical, we train word vectors on all titles and abstracts of 2014 training articles. The coverage of these learned vectors on three datasets are listed in Table TABREF32 , and the words not present in the set of pre-trained words are initialized randomly." + ], + [ + "In our experiment, some widely used text clustering methods are compared with our approach. Besides K-means, Skip-thought Vectors, Recursive Neural Network and Paragraph Vector based clustering methods, four baseline clustering methods are directly based on the popular unsupervised dimensionality reduction methods as described in Section SECREF11 . We further compare our approach with some other non-biased neural networks, such as bidirectional RNN. More details are listed as follows:", + "K-means K-means BIBREF42 on original keyword features which are respectively weighted with term frequency (TF) and term frequency-inverse document frequency (TF-IDF).", + "Skip-thought Vectors (SkipVec) This baseline BIBREF35 gives an off-the-shelf encoder to produce highly generic sentence representations. The encoder is trained using a large collection of novels and provides three encoder modes, that are unidirectional encoder (SkipVec (Uni)) with 2,400 dimensions, bidirectional encoder (SkipVec (Bi)) with 2,400 dimensions and combined encoder (SkipVec (Combine)) with SkipVec (Uni) and SkipVec (Bi) of 2,400 dimensions each. K-means is employed on the these vector representations respectively.", + "Recursive Neural Network (RecNN) In BIBREF6 , the tree structure is firstly greedy approximated via unsupervised recursive autoencoder. Then, semi-supervised recursive autoencoders are used to capture the semantics of texts based on the predicted structure. In order to make this recursive-based method completely unsupervised, we remove the cross-entropy error in the second phrase to learn vector representation and subsequently employ K-means on the learned vectors of the top tree node and the average of all vectors in the tree.", + "Paragraph Vector (Para2vec) K-means on the fixed size feature vectors generated by Paragraph Vector (Para2vec) BIBREF25 which is an unsupervised method to learn distributed representation of words and paragraphs. In our experiments, we use the open source software released by Mesnil et al. BIBREF43 .", + "Average Embedding (AE) K-means on the weighted average vectors of the word embeddings which are respectively weighted with TF and TF-IDF. The dimension of average vectors is equal to and decided by the dimension of word vectors used in our experiments.", + "Latent Semantic Analysis (LSA) K-means on the reduced subspace vectors generated by Singular Value Decomposition (SVD) method. The dimension of subspace is default set to the number of clusters, we also iterate the dimensions ranging from 10:10:200 to get the best performance, that is 10 on SearchSnippets, 20 on StackOverflow and 20 on Biomedical in our experiments.", + "Laplacian Eigenmaps (LE) This baseline, using Laplacian Eigenmaps and subsequently employing K-means algorithm, is well known as spectral clustering BIBREF44 . The dimension of subspace is default set to the number of clusters BIBREF18 , BIBREF38 , we also iterate the dimensions ranging from 10:10:200 to get the best performance, that is 20 on SearchSnippets, 70 on StackOverflow and 30 on Biomedical in our experiments.", + "Locality Preserving Indexing (LPI) This baseline, projecting the texts into a lower dimensional semantic space, can discover both the geometric and discriminating structures of the original feature space BIBREF38 . The dimension of subspace is default set to the number of clusters BIBREF38 , we also iterate the dimensions ranging from 10:10:200 to get the best performance, that is 20 on SearchSnippets, 80 on StackOverflow and 30 on Biomedical in our experiments.", + "bidirectional RNN (bi-RNN) We replace the CNN model in our framework as in Figure FIGREF5 with some bi-RNN models. Particularly, LSTM and GRU units are used in the experiments. In order to generate the fixed-length document representation from the variable-length vector sequences, for both bi-LSTM and bi-GRU based clustering methods, we further utilize three pooling methods: last pooling (using the last hidden state), mean pooling and element-wise max pooling. These pooling methods are respectively used in the previous works BIBREF45 , BIBREF27 , BIBREF46 and BIBREF9 . For regularization, the training gradients of all parameters with an INLINEFORM0 2 norm larger than 40 are clipped to 40, as the previous work BIBREF47 ." + ], + [ + "The clustering performance is evaluated by comparing the clustering results of texts with the tags/labels provided by the text corpus. Two metrics, the accuracy (ACC) and the normalized mutual information metric (NMI), are used to measure the clustering performance BIBREF38 , BIBREF48 . Given a text INLINEFORM0 , let INLINEFORM1 and INLINEFORM2 be the obtained cluster label and the label provided by the corpus, respectively. Accuracy is defined as: DISPLAYFORM0 ", + "where, INLINEFORM0 is the total number of texts, INLINEFORM1 is the indicator function that equals one if INLINEFORM2 and equals zero otherwise, and INLINEFORM3 is the permutation mapping function that maps each cluster label INLINEFORM4 to the equivalent label from the text data by Hungarian algorithm BIBREF49 .", + "Normalized mutual information BIBREF50 between tag/label set INLINEFORM0 and cluster set INLINEFORM1 is a popular metric used for evaluating clustering tasks. It is defined as follows: DISPLAYFORM0 ", + "where, INLINEFORM0 is the mutual information between INLINEFORM1 and INLINEFORM2 , INLINEFORM3 is entropy and the denominator INLINEFORM4 is used for normalizing the mutual information to be in the range of [0, 1]." + ], + [ + "The most of parameters are set uniformly for these datasets. Following previous study BIBREF38 , the number of nearest neighbors in Eqn. ( EQREF15 ) is fixed to 15 when constructing the graph structures for LE and LPI. For CNN model, the networks has two convolutional layers. The widths of the convolutional filters are both 3. The value of INLINEFORM0 for the top INLINEFORM1 -max pooling in Eqn. ( EQREF10 ) is 5. The number of feature maps at the first convolutional layer is 12, and 8 feature maps at the second convolutional layer. Both those two convolutional layers are followed by a folding layer. We further set the dimension of word embeddings INLINEFORM2 as 48. Finally, the dimension of the deep feature representation INLINEFORM3 is fixed to 480. Moreover, we set the learning rate INLINEFORM4 as 0.01 and the mini-batch training size as 200. The output size INLINEFORM5 in Eqn. ( EQREF19 ) is set same as the best dimensions of subspace in the baseline method, as described in Section SECREF37 .", + "For initial centroids have significant impact on clustering results when utilizing the K-means algorithms, we repeat K-means for multiple times with random initial centroids (specifically, 100 times for statistical significance) as Huang BIBREF48 . The all subspace vectors are normalized to 1 before applying K-means and the final results reported are the average of 5 trials with all clustering methods on three text datasets." + ], + [ + "In Table TABREF43 and Table TABREF44 , we report the ACC and NMI performance of our proposed approaches and four baseline methods, K-means, SkipVec, RecNN and Para2vec based clustering methods. Intuitively, we get a general observation that (1) BoW based approaches, including K-means (TF) and K-means (TF-IDF), and SkipVec based approaches perform not well; (2) RecNN based approaches, both RecNN (Ave.) and RecNN (Top+Ave.), do better; (3) Para2vec makes a comparable performance with the most baselines; and (4) the evaluation clearly demonstrate the superiority of our proposed methods STC INLINEFORM0 . It is an expected results. For SkipVec based approaches, the off-the-shelf encoders are trained on the BookCorpus datasets BIBREF51 , and then applied to our datasets to extract the sentence representations. The SkipVec encoders can produce generic sentence representations but may not perform well for specific datasets, in our experiments, StackOverflow and Biomedical datasets consist of many computer terms and medical terms, such as \u201cASP.NET\u201d, \u201cXML\u201d, \u201cC#\u201d, \u201cserum\u201d and \u201cglycolytic\u201d. When we take a more careful look, we find that RecNN (Top) does poorly, even worse than K-means (TF-IDF). The reason maybe that although recursive neural models introduce tree structure to capture compositional semantics, the vector of the top node mainly captures a biased semantic while the average of all vectors in the tree nodes, such as RecNN (Ave.), can be better to represent sentence level semantic. And we also get another observation that, although our proposed STC INLINEFORM1 -LE and STC INLINEFORM2 -LPI outperform both BoW based and RecNN based approaches across all three datasets, STC INLINEFORM3 -AE and STC INLINEFORM4 -LSA do just exhibit some similar performances as RecNN (Ave.) and RecNN (Top+Ave.) do in the datasets of StackOverflow and Biomedical.", + "We further replace the CNN model in our framework as in Figure FIGREF5 with some other non-biased models, such as bi-LSTM and bi-GRU, and report the results in Table TABREF46 and Table TABREF47 . As an instance, the binary codes generated from LPI are used to guide the learning of bi-LSTM/bi-GRU models. From the results, we can see that bi-GRU and bi-LSTM based clustering methods do equally well, no clear winner, and both achieve great enhancements compared with LPI (best). Compared with these bi-LSTM/bi-GRU based models, the evaluation results still demonstrate the superiority of our approach methods, CNN based clustering model, in the most cases. As the results reported by Visin et al. BIBREF33 , despite bi-directional or multi-directional RNN models perform a good non-biased feature extraction, they yet do not outperform state-of-the-art CNN on some tasks.", + "In order to make clear what factors make our proposed method work, we report the bar chart results of ACC and MNI of our proposed methods and the corresponding baseline methods in Figure FIGREF49 and Figure FIGREF53 . It is clear that, although AE and LSA does well or even better than LE and LPI, especially in dataset of both StackOverflow and Biomedical, STC INLINEFORM0 -LE and STC INLINEFORM1 -LPI achieve a much larger performance enhancements than STC INLINEFORM2 -AE and STC INLINEFORM3 -LSA do. The possible reason is that the information the pseudo supervision used to guide the learning of CNN model that make difference. Especially, for AE case, the input features fed into CNN model and the pseudo supervision employed to guide the learning of CNN model are all come from word embeddings. There are no different semantic features to be used into our proposed method, thus the performance enhancements are limited in STC INLINEFORM4 -AE. For LSA case, as we known, LSA is to make matrix factorization to find the best subspace approximation of the original feature space to minimize the global reconstruction error. And as BIBREF24 , BIBREF52 recently point out that word embeddings trained with word2vec or some variances, is essentially to do an operation of matrix factorization. Therefore, the information between input and the pseudo supervision in CNN is not departed very largely from each other, and the performance enhancements of STC INLINEFORM5 -AE is also not quite satisfactory. For LE and LPI case, as we known that LE extracts the manifold structure of the original feature space, and LPI extracts both geometric and discriminating structure of the original feature space BIBREF38 . We guess that our approach STC INLINEFORM6 -LE and STC INLINEFORM7 -LPI achieve enhancements compared with both LE and LPI by a large margin, because both of LE and LPI get useful semantic features, and these features are also different from word embeddings used as input of CNN. From this view, we say that our proposed STC has potential to behave more effective when the pseudo supervision is able to get semantic meaningful features, which is different enough from the input of CNN.", + "Furthermore, from the results of K-means and AE in Table TABREF43 - TABREF44 and Figure FIGREF49 - FIGREF53 , we note that TF-IDF weighting gives a more remarkable improvement for K-means, while TF weighting works better than TF-IDF weighting for Average Embedding. Maybe the reason is that pre-trained word embeddings encode some useful information from external corpus and are able to get even better results without TF-IDF weighting. Meanwhile, we find that LE get quite unusual good performance than LPI, LSA and AE in SearchSnippets dataset, which is not found in the other two datasets. To get clear about this, and also to make a much better demonstration about our proposed approaches and other baselines, we further report 2-dimensional text embeddings on SearchSnippets in Figure FIGREF58 , using t-SNE BIBREF53 to get distributed stochastic neighbor embedding of the feature representations used in the clustering methods. We can see that the results of from AE and LSA seem to be fairly good or even better than the ones from LE and LPI, which is not the same as the results from ACC and NMI in Figure FIGREF49 - FIGREF53 . Meanwhile, RecNN (Ave.) performs better than BoW (both TF and TF-IDF) while RecNN (Top) does not, which is the same as the results from ACC and NMI in Table TABREF43 and Table TABREF44 . Then we guess that both \u201dthe same as\u201d and \u201dnot the same as\u201d above, is just a good example to illustrate that visualization tool, such as t-SNE, get some useful information for measuring results, which is different from the ones of ACC and NMI. Moreover, from this complementary view of t-SNE, we can see that our STC INLINEFORM0 -AE, STC INLINEFORM1 -LSA, STC INLINEFORM2 -LE, and STC INLINEFORM3 -LPI show more clear-cut margins among different semantic topics (that is, tags/labels), compared with AE, LSA, LE and LPI, respectively, as well as compared with both baselines, BoW and RecNN based ones.", + "From all these results, with three measures of ACC, NMI and t-SNE under three datasets, we can get a solid conclusion that our proposed approaches is an effective approaches to get useful semantic features for short text clustering." + ], + [ + "With the emergence of social media, short text clustering has become an increasing important task. This paper explores a new perspective to cluster short texts based on deep feature representation learned from the proposed self-taught convolutional neural networks. Our framework can be successfully accomplished without using any external tags/labels and complicated NLP pre-processing, and and our approach is a flexible framework, in which the traditional dimension reduction approaches could be used to get performance enhancement. Our extensive experimental study on three short text datasets shows that our approach can achieve a significantly better performance. In the future, how to select and incorporate more effective semantic features into the proposed framework would call for more research." + ], + [ + "We would like to thank reviewers for their comments, and acknowledge Kaggle and BioASQ for making the datasets available. This work is supported by the National Natural Science Foundation of China (No. 61602479, No. 61303172, No. 61403385) and the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB02070005)." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0354/instruction.md b/qasper-0354/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..359944633718b6c2767f34865e11e1e7330326d7 --- /dev/null +++ b/qasper-0354/instruction.md @@ -0,0 +1,134 @@ +Name of Paper: Self-Taught Convolutional Neural Networks for Short Text Clustering + +Question: What datasets did they use? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work", + "Short Text Clustering", + "Deep Neural Networks", + "Methodology", + "Deep Convolutional Neural Networks", + "Unsupervised Dimensionality Reduction", + "Learning", + "K-means for Clustering", + "Datasets", + "Pre-trained Word Vectors", + "Comparisons", + "Evaluation Metrics", + "Hyperparameter Settings", + "Results and Analysis", + "Conclusions", + "Acknowledgments" + ], + "paragraphs": [ + [ + "Short text clustering is of great importance due to its various applications, such as user profiling BIBREF0 and recommendation BIBREF1 , for nowaday's social media dataset emerged day by day. However, short text clustering has the data sparsity problem and most words only occur once in each short text BIBREF2 . As a result, the Term Frequency-Inverse Document Frequency (TF-IDF) measure cannot work well in short text setting. In order to address this problem, some researchers work on expanding and enriching the context of data from Wikipedia BIBREF3 or an ontology BIBREF4 . However, these methods involve solid Natural Language Processing (NLP) knowledge and still use high-dimensional representation which may result in a waste of both memory and computation time. Another way to overcome these issues is to explore some sophisticated models to cluster short texts. For example, Yin and Wang BIBREF5 proposed a Dirichlet multinomial mixture model-based approach for short text clustering. Yet how to design an effective model is an open question, and most of these methods directly trained based on Bag-of-Words (BoW) are shallow structures which cannot preserve the accurate semantic similarities.", + "Recently, with the help of word embedding, neural networks demonstrate their great performance in terms of constructing text representation, such as Recursive Neural Network (RecNN) BIBREF6 , BIBREF7 and Recurrent Neural Network (RNN) BIBREF8 . However, RecNN exhibits high time complexity to construct the textual tree, and RNN, using the hidden layer computed at the last word to represent the text, is a biased model where later words are more dominant than earlier words BIBREF9 . Whereas for the non-biased models, the learned representation of one text can be extracted from all the words in the text with non-dominant learned weights. More recently, Convolution Neural Network (CNN), as the most popular non-biased model and applying convolutional filters to capture local features, has achieved a better performance in many NLP applications, such as sentence modeling BIBREF10 , relation classification BIBREF11 , and other traditional NLP tasks BIBREF12 . Most of the previous works focus CNN on solving supervised NLP tasks, while in this paper we aim to explore the power of CNN on one unsupervised NLP task, short text clustering.", + "We systematically introduce a simple yet surprisingly powerful Self-Taught Convolutional neural network framework for Short Text Clustering, called STC INLINEFORM0 . An overall architecture of our proposed approach is illustrated in Figure FIGREF5 . We, inspired by BIBREF13 , BIBREF14 , utilize a self-taught learning framework into our task. In particular, the original raw text features are first embedded into compact binary codes INLINEFORM1 with the help of one traditional unsupervised dimensionality reduction function. Then text matrix INLINEFORM2 projected from word embeddings are fed into CNN model to learn the deep feature representation INLINEFORM3 and the output units are used to fit the pre-trained binary codes INLINEFORM4 . After obtaining the learned features, K-means algorithm is employed on them to cluster texts into clusters INLINEFORM5 . Obviously, we call our approach \u201cself-taught\u201d because the CNN model is learnt from the pseudo labels generated from the previous stage, which is quite different from the term \u201cself-taught\u201d in BIBREF15 . Our main contributions can be summarized as follows:", + "This work is an extension of our conference paper BIBREF16 , and they differ in the following aspects. First, we put forward a general a self-taught CNN framework in this paper which can flexibly couple various semantic features, whereas the conference version can be seen as a specific example of this work. Second, in this paper we use a new short text dataset, Biomedical, in the experiment to verify the effectiveness of our approach. Third, we put much effort on studying the influence of various different semantic features integrated in our self-taught CNN framework, which is not involved in the conference paper.", + "For the purpose of reproducibility, we make the datasets and software used in our experiments publicly available at the website.", + "The remainder of this paper is organized as follows: In Section SECREF2 , we first briefly survey several related works. In Section SECREF3 , we describe the proposed approach STC INLINEFORM0 and implementation details. Experimental results and analyses are presented in Section SECREF4 . Finally, conclusions are given in the last Section." + ], + [ + "In this section, we review the related work from the following two perspectives: short text clustering and deep neural networks." + ], + [ + "There have been several studies that attempted to overcome the sparseness of short text representation. One way is to expand and enrich the context of data. For example, Banerjee et al. BIBREF3 proposed a method of improving the accuracy of short text clustering by enriching their representation with additional features from Wikipedia, and Fodeh et al. BIBREF4 incorporate semantic knowledge from an ontology into text clustering. However, these works need solid NLP knowledge and still use high-dimensional representation which may result in a waste of both memory and computation time. Another direction is to map the original features into reduced space, such as Latent Semantic Analysis (LSA) BIBREF17 , Laplacian Eigenmaps (LE) BIBREF18 , and Locality Preserving Indexing (LPI) BIBREF19 . Even some researchers explored some sophisticated models to cluster short texts. For example, Yin and Wang BIBREF5 proposed a Dirichlet multinomial mixture model-based approach for short text clustering. Moreover, some studies even focus the above both two streams. For example, Tang et al. BIBREF20 proposed a novel framework which enrich the text features by employing machine translation and reduce the original features simultaneously through matrix factorization techniques.", + "Despite the above clustering methods can alleviate sparseness of short text representation to some extent, most of them ignore word order in the text and belong to shallow structures which can not fully capture accurate semantic similarities." + ], + [ + "Recently, there is a revival of interest in DNN and many researchers have concentrated on using Deep Learning to learn features. Hinton and Salakhutdinov BIBREF21 use DAE to learn text representation. During the fine-tuning procedure, they use backpropagation to find codes that are good at reconstructing the word-count vector.", + "More recently, researchers propose to use external corpus to learn a distributed representation for each word, called word embedding BIBREF22 , to improve DNN performance on NLP tasks. The Skip-gram and continuous bag-of-words models of Word2vec BIBREF23 propose a simple single-layer architecture based on the inner product between two word vectors, and Pennington et al. BIBREF24 introduce a new model for word representation, called GloVe, which captures the global corpus statistics.", + "In order to learn the compact representation vectors of sentences, Le and Mikolov BIBREF25 directly extend the previous Word2vec BIBREF23 by predicting words in the sentence, which is named Paragraph Vector (Para2vec). Para2vec is still a shallow window-based method and need a larger corpus to yield better performance. More neural networks utilize word embedding to capture true meaningful syntactic and semantic regularities, such as RecNN BIBREF6 , BIBREF7 and RNN BIBREF8 . However, RecNN exhibits high time complexity to construct the textual tree, and RNN, using the layer computed at the last word to represent the text, is a biased model. Recently, Long Short-Term Memory (LSTM) BIBREF26 and Gated Recurrent Unit (GRU) BIBREF27 , as sophisticated recurrent hidden units of RNN, has presented its advantages in many sequence generation problem, such as machine translation BIBREF28 , speech recognition BIBREF29 , and text conversation BIBREF30 . While, CNN is better to learn non-biased implicit features which has been successfully exploited for many supervised NLP learning tasks as described in Section SECREF1 , and various CNN based variants are proposed in the recent works, such as Dynamic Convolutional Neural Network (DCNN) BIBREF10 , Gated Recursive Convolutional Neural Network (grConv) BIBREF31 and Self-Adaptive Hierarchical Sentence model (AdaSent) BIBREF32 .", + "In the past few days, Visin et al. BIBREF33 have attempted to replace convolutional layer in CNN to learn non-biased features for object recognition with four RNNs, called ReNet, that sweep over lower-layer features in different directions: (1) bottom to top, (2) top to bottom, (3) left to right and (4) right to left. However, ReNet does not outperform state-of-the-art convolutional neural networks on any of the three benchmark datasets, and it is also a supervised learning model for classification. Inspired by Skip-gram of word2vec BIBREF34 , BIBREF23 , Skip-thought model BIBREF35 describe an approach for unsupervised learning of a generic, distributed sentence encoder. Similar as Skip-gram model, Skip-thought model trains an encoder-decoder model that tries to reconstruct the surrounding sentences of an encoded sentence and released an off-the-shelf encoder to extract sentence representation. Even some researchers introduce continuous Skip-gram and negative sampling to CNN for learning visual representation in an unsupervised manner BIBREF36 . This paper, from a new perspective, puts forward a general self-taught CNN framework which can flexibly couple various semantic features and achieve a good performance on one unsupervised learning task, short text clustering." + ], + [ + "Assume that we are given a dataset of INLINEFORM0 training texts denoted as: INLINEFORM1 , where INLINEFORM2 is the dimensionality of the original BoW representation. Denote its tag set as INLINEFORM3 and the pre-trained word embedding set as INLINEFORM4 , where INLINEFORM5 is the dimensionality of word vectors and INLINEFORM6 is the vocabulary size. In order to learn the INLINEFORM7 -dimensional deep feature representation INLINEFORM8 from CNN in an unsupervised manner, some unsupervised dimensionality reduction methods INLINEFORM9 are employed to guide the learning of CNN model. Our goal is to cluster these texts INLINEFORM10 into clusters INLINEFORM11 based on the learned deep feature representation while preserving the semantic consistency.", + "As depicted in Figure FIGREF5 , the proposed framework consist of three components, deep convolutional neural network (CNN), unsupervised dimensionality reduction function and K-means module. In the rest sections, we first present the first two components respectively, and then give the trainable parameters and the objective function to learn the deep feature representation. Finally, the last section describe how to perform clustering on the learned features." + ], + [ + "In this section, we briefly review one popular deep convolutional neural network, Dynamic Convolutional Neural Network (DCNN) BIBREF10 as an instance of CNN in the following sections, which as the foundation of our proposed method has been successfully proposed for the completely supervised learning task, text classification.", + "Taking a neural network with two convolutional layers in Figure FIGREF9 as an example, the network transforms raw input text to a powerful representation. Particularly, each raw text vector INLINEFORM0 is projected into a matrix representation INLINEFORM1 by looking up a word embedding INLINEFORM2 , where INLINEFORM3 is the length of one text. We also let INLINEFORM4 and INLINEFORM5 denote the weights of the neural networks. The network defines a transformation INLINEFORM6 INLINEFORM7 which transforms an input raw text INLINEFORM8 to a INLINEFORM9 -dimensional deep representation INLINEFORM10 . There are three basic operations described as follows:", + "Wide one-dimensional convolution This operation INLINEFORM0 is applied to an individual row of the sentence matrix INLINEFORM1 , and yields a resulting matrix INLINEFORM2 , where INLINEFORM3 is the width of convolutional filter.", + "Folding In this operation, every two rows in a feature map are simply summed component-wisely. For a map of INLINEFORM0 rows, folding returns a map of INLINEFORM1 rows, thus halving the size of the representation and yielding a matrix feature INLINEFORM2 . Note that folding operation does not introduce any additional parameters.", + "Dynamic INLINEFORM0 -max pooling Assuming the pooling parameter as INLINEFORM1 , INLINEFORM2 -max pooling selects the sub-matrix INLINEFORM3 of the INLINEFORM4 highest values in each row of the matrix INLINEFORM5 . For dynamic INLINEFORM6 -max pooling, the pooling parameter INLINEFORM7 is dynamically selected in order to allow for a smooth extraction of higher-order and longer-range features BIBREF10 . Given a fixed pooling parameter INLINEFORM8 for the topmost convolutional layer, the parameter INLINEFORM9 of INLINEFORM10 -max pooling in the INLINEFORM11 -th convolutional layer can be computed as follows: DISPLAYFORM0 ", + "where INLINEFORM0 is the total number of convolutional layers in the network." + ], + [ + "As described in Figure FIGREF5 , the dimensionality reduction function is defined as follows: DISPLAYFORM0 ", + "where, INLINEFORM0 are the INLINEFORM1 -dimensional reduced latent space representations. Here, we take four popular dimensionality reduction methods as examples in our framework.", + "Average Embedding (AE): This method directly averages the word embeddings which are respectively weighted with TF and TF-IDF. Huang et al. BIBREF37 used this strategy as the global context in their task, and Socher et al. BIBREF7 and Lai et al. BIBREF9 used this method for text classification. The weighted average of all word vectors in one text can be computed as follows: DISPLAYFORM0 ", + "where INLINEFORM0 can be any weighting function that captures the importance of word INLINEFORM1 in the text INLINEFORM2 .", + "Latent Semantic Analysis (LSA): LSA BIBREF17 is the most popular global matrix factorization method, which applies a dimension reducing linear projection, Singular Value Decomposition (SVD), of the corresponding term/document matrix. Suppose the rank of INLINEFORM0 is INLINEFORM1 , LSA decompose INLINEFORM2 into the product of three other matrices: DISPLAYFORM0 ", + "where INLINEFORM0 and INLINEFORM1 are the singular values of INLINEFORM2 , INLINEFORM3 is a set of left singular vectors and INLINEFORM4 is a set of right singular vectors. LSA uses the top INLINEFORM5 vectors in INLINEFORM6 as the transformation matrix to embed the original text features into a INLINEFORM7 -dimensional subspace INLINEFORM8 BIBREF17 .", + "Laplacian Eigenmaps (LE): The top eigenvectors of graph Laplacian, defined on the similarity matrix of texts, are used in the method, which can discover the manifold structure of the text space BIBREF18 . In order to avoid storing the dense similarity matrix, many approximation techniques are proposed to reduce the memory usage and computational complexity for LE. There are two representative approximation methods, sparse similarity matrix and Nystr INLINEFORM0 m approximation. Following previous studies BIBREF38 , BIBREF13 , we select the former technique to construct the INLINEFORM1 local similarity matrix INLINEFORM2 by using heat kernel as follows: DISPLAYFORM0 ", + "where, INLINEFORM0 is a tuning parameter (default is 1) and INLINEFORM1 represents the set of INLINEFORM2 -nearest-neighbors of INLINEFORM3 . By introducing a diagonal INLINEFORM4 matrix INLINEFORM5 whose entries are given by INLINEFORM6 , the graph Laplacian INLINEFORM7 can be computed by ( INLINEFORM8 ). The optimal INLINEFORM9 real-valued matrix INLINEFORM10 can be obtained by solving the following objective function: DISPLAYFORM0 ", + "where INLINEFORM0 is the trace function, INLINEFORM1 requires the different dimensions to be uncorrelated, and INLINEFORM2 requires each dimension to achieve equal probability as positive or negative).", + "Locality Preserving Indexing (LPI): This method extends LE to deal with unseen texts by approximating the linear function INLINEFORM0 BIBREF13 , and the subspace vectors are obtained by finding the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the Riemannian manifold BIBREF19 . Similar as LE, we first construct the local similarity matrix INLINEFORM1 , then the graph Laplacian INLINEFORM2 can be computed by ( INLINEFORM3 ), where INLINEFORM4 measures the local density around INLINEFORM5 and is equal to INLINEFORM6 . Compute the eigenvectors INLINEFORM7 and eigenvalues INLINEFORM8 of the following generalized eigen-problem: DISPLAYFORM0 ", + "The mapping function INLINEFORM0 can be obtained and applied to the unseen data BIBREF38 .", + "All of the above methods claim a better performance in capturing semantic similarity between texts in the reduced latent space representation INLINEFORM0 than in the original representation INLINEFORM1 , while the performance of short text clustering can be further enhanced with the help of our framework, self-taught CNN." + ], + [ + "The last layer of CNN is an output layer as follows: DISPLAYFORM0 ", + "where, INLINEFORM0 is the deep feature representation, INLINEFORM1 is the output vector and INLINEFORM2 is weight matrix.", + "In order to incorporate the latent semantic features INLINEFORM0 , we first binary the real-valued vectors INLINEFORM1 to the binary codes INLINEFORM2 by setting the threshold to be the media vector INLINEFORM3 . Then, the output vector INLINEFORM4 is used to fit the binary codes INLINEFORM5 via INLINEFORM6 logistic operations as follows: DISPLAYFORM0 ", + "All parameters to be trained are defined as INLINEFORM0 . DISPLAYFORM0 ", + "Given the training text collection INLINEFORM0 , and the pre-trained binary codes INLINEFORM1 , the log likelihood of the parameters can be written down as follows: DISPLAYFORM0 ", + "Following the previous work BIBREF10 , we train the network with mini-batches by back-propagation and perform the gradient-based optimization using the Adagrad update rule BIBREF39 . For regularization, we employ dropout with 50% rate to the penultimate layer BIBREF10 , BIBREF40 ." + ], + [ + "With the given short texts, we first utilize the trained deep neural network to obtain the semantic representations INLINEFORM0 , and then employ traditional K-means algorithm to perform clustering." + ], + [ + "We test our proposed approach on three public short text datasets. The summary statistics and semantic topics of these datasets are described in Table TABREF24 and Table TABREF25 .", + "SearchSnippets. This dataset was selected from the results of web search transaction using predefined phrases of 8 different domains by Phan et al. BIBREF41 .", + "StackOverflow. We use the challenge data published in Kaggle.com. The raw dataset consists 3,370,528 samples through July 31st, 2012 to August 14, 2012. In our experiments, we randomly select 20,000 question titles from 20 different tags as in Table TABREF25 .", + "Biomedical. We use the challenge data published in BioASQ's official website. In our experiments, we randomly select 20, 000 paper titles from 20 different MeSH major topics as in Table TABREF25 . As described in Table TABREF24 , the max length of selected paper titles is 53.", + "For these datasets, we randomly select 10% of data as the development set. Since SearchSnippets has been pre-processed by Phan et al. BIBREF41 , we do not further process this dataset. In StackOverflow, texts contain lots of computer terminology, and symbols and capital letters are meaningful, thus we do not do any pre-processed procedures. For Biomedical, we remove the symbols and convert letters into lower case." + ], + [ + "We use the publicly available word2vec tool to train word embeddings, and the most parameters are set as same as Mikolov et al. BIBREF23 to train word vectors on Google News setting, except of vector dimensionality using 48 and minimize count using 5. For SearchSnippets, we train word vectors on Wikipedia dumps. For StackOverflow, we train word vectors on the whole corpus of the StackOverflow dataset described above which includes the question titles and post contents. For Biomedical, we train word vectors on all titles and abstracts of 2014 training articles. The coverage of these learned vectors on three datasets are listed in Table TABREF32 , and the words not present in the set of pre-trained words are initialized randomly." + ], + [ + "In our experiment, some widely used text clustering methods are compared with our approach. Besides K-means, Skip-thought Vectors, Recursive Neural Network and Paragraph Vector based clustering methods, four baseline clustering methods are directly based on the popular unsupervised dimensionality reduction methods as described in Section SECREF11 . We further compare our approach with some other non-biased neural networks, such as bidirectional RNN. More details are listed as follows:", + "K-means K-means BIBREF42 on original keyword features which are respectively weighted with term frequency (TF) and term frequency-inverse document frequency (TF-IDF).", + "Skip-thought Vectors (SkipVec) This baseline BIBREF35 gives an off-the-shelf encoder to produce highly generic sentence representations. The encoder is trained using a large collection of novels and provides three encoder modes, that are unidirectional encoder (SkipVec (Uni)) with 2,400 dimensions, bidirectional encoder (SkipVec (Bi)) with 2,400 dimensions and combined encoder (SkipVec (Combine)) with SkipVec (Uni) and SkipVec (Bi) of 2,400 dimensions each. K-means is employed on the these vector representations respectively.", + "Recursive Neural Network (RecNN) In BIBREF6 , the tree structure is firstly greedy approximated via unsupervised recursive autoencoder. Then, semi-supervised recursive autoencoders are used to capture the semantics of texts based on the predicted structure. In order to make this recursive-based method completely unsupervised, we remove the cross-entropy error in the second phrase to learn vector representation and subsequently employ K-means on the learned vectors of the top tree node and the average of all vectors in the tree.", + "Paragraph Vector (Para2vec) K-means on the fixed size feature vectors generated by Paragraph Vector (Para2vec) BIBREF25 which is an unsupervised method to learn distributed representation of words and paragraphs. In our experiments, we use the open source software released by Mesnil et al. BIBREF43 .", + "Average Embedding (AE) K-means on the weighted average vectors of the word embeddings which are respectively weighted with TF and TF-IDF. The dimension of average vectors is equal to and decided by the dimension of word vectors used in our experiments.", + "Latent Semantic Analysis (LSA) K-means on the reduced subspace vectors generated by Singular Value Decomposition (SVD) method. The dimension of subspace is default set to the number of clusters, we also iterate the dimensions ranging from 10:10:200 to get the best performance, that is 10 on SearchSnippets, 20 on StackOverflow and 20 on Biomedical in our experiments.", + "Laplacian Eigenmaps (LE) This baseline, using Laplacian Eigenmaps and subsequently employing K-means algorithm, is well known as spectral clustering BIBREF44 . The dimension of subspace is default set to the number of clusters BIBREF18 , BIBREF38 , we also iterate the dimensions ranging from 10:10:200 to get the best performance, that is 20 on SearchSnippets, 70 on StackOverflow and 30 on Biomedical in our experiments.", + "Locality Preserving Indexing (LPI) This baseline, projecting the texts into a lower dimensional semantic space, can discover both the geometric and discriminating structures of the original feature space BIBREF38 . The dimension of subspace is default set to the number of clusters BIBREF38 , we also iterate the dimensions ranging from 10:10:200 to get the best performance, that is 20 on SearchSnippets, 80 on StackOverflow and 30 on Biomedical in our experiments.", + "bidirectional RNN (bi-RNN) We replace the CNN model in our framework as in Figure FIGREF5 with some bi-RNN models. Particularly, LSTM and GRU units are used in the experiments. In order to generate the fixed-length document representation from the variable-length vector sequences, for both bi-LSTM and bi-GRU based clustering methods, we further utilize three pooling methods: last pooling (using the last hidden state), mean pooling and element-wise max pooling. These pooling methods are respectively used in the previous works BIBREF45 , BIBREF27 , BIBREF46 and BIBREF9 . For regularization, the training gradients of all parameters with an INLINEFORM0 2 norm larger than 40 are clipped to 40, as the previous work BIBREF47 ." + ], + [ + "The clustering performance is evaluated by comparing the clustering results of texts with the tags/labels provided by the text corpus. Two metrics, the accuracy (ACC) and the normalized mutual information metric (NMI), are used to measure the clustering performance BIBREF38 , BIBREF48 . Given a text INLINEFORM0 , let INLINEFORM1 and INLINEFORM2 be the obtained cluster label and the label provided by the corpus, respectively. Accuracy is defined as: DISPLAYFORM0 ", + "where, INLINEFORM0 is the total number of texts, INLINEFORM1 is the indicator function that equals one if INLINEFORM2 and equals zero otherwise, and INLINEFORM3 is the permutation mapping function that maps each cluster label INLINEFORM4 to the equivalent label from the text data by Hungarian algorithm BIBREF49 .", + "Normalized mutual information BIBREF50 between tag/label set INLINEFORM0 and cluster set INLINEFORM1 is a popular metric used for evaluating clustering tasks. It is defined as follows: DISPLAYFORM0 ", + "where, INLINEFORM0 is the mutual information between INLINEFORM1 and INLINEFORM2 , INLINEFORM3 is entropy and the denominator INLINEFORM4 is used for normalizing the mutual information to be in the range of [0, 1]." + ], + [ + "The most of parameters are set uniformly for these datasets. Following previous study BIBREF38 , the number of nearest neighbors in Eqn. ( EQREF15 ) is fixed to 15 when constructing the graph structures for LE and LPI. For CNN model, the networks has two convolutional layers. The widths of the convolutional filters are both 3. The value of INLINEFORM0 for the top INLINEFORM1 -max pooling in Eqn. ( EQREF10 ) is 5. The number of feature maps at the first convolutional layer is 12, and 8 feature maps at the second convolutional layer. Both those two convolutional layers are followed by a folding layer. We further set the dimension of word embeddings INLINEFORM2 as 48. Finally, the dimension of the deep feature representation INLINEFORM3 is fixed to 480. Moreover, we set the learning rate INLINEFORM4 as 0.01 and the mini-batch training size as 200. The output size INLINEFORM5 in Eqn. ( EQREF19 ) is set same as the best dimensions of subspace in the baseline method, as described in Section SECREF37 .", + "For initial centroids have significant impact on clustering results when utilizing the K-means algorithms, we repeat K-means for multiple times with random initial centroids (specifically, 100 times for statistical significance) as Huang BIBREF48 . The all subspace vectors are normalized to 1 before applying K-means and the final results reported are the average of 5 trials with all clustering methods on three text datasets." + ], + [ + "In Table TABREF43 and Table TABREF44 , we report the ACC and NMI performance of our proposed approaches and four baseline methods, K-means, SkipVec, RecNN and Para2vec based clustering methods. Intuitively, we get a general observation that (1) BoW based approaches, including K-means (TF) and K-means (TF-IDF), and SkipVec based approaches perform not well; (2) RecNN based approaches, both RecNN (Ave.) and RecNN (Top+Ave.), do better; (3) Para2vec makes a comparable performance with the most baselines; and (4) the evaluation clearly demonstrate the superiority of our proposed methods STC INLINEFORM0 . It is an expected results. For SkipVec based approaches, the off-the-shelf encoders are trained on the BookCorpus datasets BIBREF51 , and then applied to our datasets to extract the sentence representations. The SkipVec encoders can produce generic sentence representations but may not perform well for specific datasets, in our experiments, StackOverflow and Biomedical datasets consist of many computer terms and medical terms, such as \u201cASP.NET\u201d, \u201cXML\u201d, \u201cC#\u201d, \u201cserum\u201d and \u201cglycolytic\u201d. When we take a more careful look, we find that RecNN (Top) does poorly, even worse than K-means (TF-IDF). The reason maybe that although recursive neural models introduce tree structure to capture compositional semantics, the vector of the top node mainly captures a biased semantic while the average of all vectors in the tree nodes, such as RecNN (Ave.), can be better to represent sentence level semantic. And we also get another observation that, although our proposed STC INLINEFORM1 -LE and STC INLINEFORM2 -LPI outperform both BoW based and RecNN based approaches across all three datasets, STC INLINEFORM3 -AE and STC INLINEFORM4 -LSA do just exhibit some similar performances as RecNN (Ave.) and RecNN (Top+Ave.) do in the datasets of StackOverflow and Biomedical.", + "We further replace the CNN model in our framework as in Figure FIGREF5 with some other non-biased models, such as bi-LSTM and bi-GRU, and report the results in Table TABREF46 and Table TABREF47 . As an instance, the binary codes generated from LPI are used to guide the learning of bi-LSTM/bi-GRU models. From the results, we can see that bi-GRU and bi-LSTM based clustering methods do equally well, no clear winner, and both achieve great enhancements compared with LPI (best). Compared with these bi-LSTM/bi-GRU based models, the evaluation results still demonstrate the superiority of our approach methods, CNN based clustering model, in the most cases. As the results reported by Visin et al. BIBREF33 , despite bi-directional or multi-directional RNN models perform a good non-biased feature extraction, they yet do not outperform state-of-the-art CNN on some tasks.", + "In order to make clear what factors make our proposed method work, we report the bar chart results of ACC and MNI of our proposed methods and the corresponding baseline methods in Figure FIGREF49 and Figure FIGREF53 . It is clear that, although AE and LSA does well or even better than LE and LPI, especially in dataset of both StackOverflow and Biomedical, STC INLINEFORM0 -LE and STC INLINEFORM1 -LPI achieve a much larger performance enhancements than STC INLINEFORM2 -AE and STC INLINEFORM3 -LSA do. The possible reason is that the information the pseudo supervision used to guide the learning of CNN model that make difference. Especially, for AE case, the input features fed into CNN model and the pseudo supervision employed to guide the learning of CNN model are all come from word embeddings. There are no different semantic features to be used into our proposed method, thus the performance enhancements are limited in STC INLINEFORM4 -AE. For LSA case, as we known, LSA is to make matrix factorization to find the best subspace approximation of the original feature space to minimize the global reconstruction error. And as BIBREF24 , BIBREF52 recently point out that word embeddings trained with word2vec or some variances, is essentially to do an operation of matrix factorization. Therefore, the information between input and the pseudo supervision in CNN is not departed very largely from each other, and the performance enhancements of STC INLINEFORM5 -AE is also not quite satisfactory. For LE and LPI case, as we known that LE extracts the manifold structure of the original feature space, and LPI extracts both geometric and discriminating structure of the original feature space BIBREF38 . We guess that our approach STC INLINEFORM6 -LE and STC INLINEFORM7 -LPI achieve enhancements compared with both LE and LPI by a large margin, because both of LE and LPI get useful semantic features, and these features are also different from word embeddings used as input of CNN. From this view, we say that our proposed STC has potential to behave more effective when the pseudo supervision is able to get semantic meaningful features, which is different enough from the input of CNN.", + "Furthermore, from the results of K-means and AE in Table TABREF43 - TABREF44 and Figure FIGREF49 - FIGREF53 , we note that TF-IDF weighting gives a more remarkable improvement for K-means, while TF weighting works better than TF-IDF weighting for Average Embedding. Maybe the reason is that pre-trained word embeddings encode some useful information from external corpus and are able to get even better results without TF-IDF weighting. Meanwhile, we find that LE get quite unusual good performance than LPI, LSA and AE in SearchSnippets dataset, which is not found in the other two datasets. To get clear about this, and also to make a much better demonstration about our proposed approaches and other baselines, we further report 2-dimensional text embeddings on SearchSnippets in Figure FIGREF58 , using t-SNE BIBREF53 to get distributed stochastic neighbor embedding of the feature representations used in the clustering methods. We can see that the results of from AE and LSA seem to be fairly good or even better than the ones from LE and LPI, which is not the same as the results from ACC and NMI in Figure FIGREF49 - FIGREF53 . Meanwhile, RecNN (Ave.) performs better than BoW (both TF and TF-IDF) while RecNN (Top) does not, which is the same as the results from ACC and NMI in Table TABREF43 and Table TABREF44 . Then we guess that both \u201dthe same as\u201d and \u201dnot the same as\u201d above, is just a good example to illustrate that visualization tool, such as t-SNE, get some useful information for measuring results, which is different from the ones of ACC and NMI. Moreover, from this complementary view of t-SNE, we can see that our STC INLINEFORM0 -AE, STC INLINEFORM1 -LSA, STC INLINEFORM2 -LE, and STC INLINEFORM3 -LPI show more clear-cut margins among different semantic topics (that is, tags/labels), compared with AE, LSA, LE and LPI, respectively, as well as compared with both baselines, BoW and RecNN based ones.", + "From all these results, with three measures of ACC, NMI and t-SNE under three datasets, we can get a solid conclusion that our proposed approaches is an effective approaches to get useful semantic features for short text clustering." + ], + [ + "With the emergence of social media, short text clustering has become an increasing important task. This paper explores a new perspective to cluster short texts based on deep feature representation learned from the proposed self-taught convolutional neural networks. Our framework can be successfully accomplished without using any external tags/labels and complicated NLP pre-processing, and and our approach is a flexible framework, in which the traditional dimension reduction approaches could be used to get performance enhancement. Our extensive experimental study on three short text datasets shows that our approach can achieve a significantly better performance. In the future, how to select and incorporate more effective semantic features into the proposed framework would call for more research." + ], + [ + "We would like to thank reviewers for their comments, and acknowledge Kaggle and BioASQ for making the datasets available. This work is supported by the National Natural Science Foundation of China (No. 61602479, No. 61303172, No. 61403385) and the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB02070005)." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0362/instruction.md b/qasper-0362/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..7f2a73966762f364944354e1ed42337c42a3a15d --- /dev/null +++ b/qasper-0362/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: What Do You Mean I'm Funny? Personalizing the Joke Skill of a Voice-Controlled Virtual Assistant + +Question: What feedback labels are used? \ No newline at end of file diff --git a/qasper-0363/instruction.md b/qasper-0363/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..26c5f44820b5a16f14ace03f20357d2755aaff02 --- /dev/null +++ b/qasper-0363/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: A Measure of Similarity in Textual Data Using Spearman's Rank Correlation Coefficient + +Question: What representations for textual documents do they use? \ No newline at end of file diff --git a/qasper-0365/instruction.md b/qasper-0365/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..7e53f260252a395f7f27450fbb197127a34b5fec --- /dev/null +++ b/qasper-0365/instruction.md @@ -0,0 +1,96 @@ +Name of Paper: A Measure of Similarity in Textual Data Using Spearman's Rank Correlation Coefficient + +Question: How do they evaluate knowledge extraction performance? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Background", + "Background ::: Document Representation", + "Background ::: Measures of Similarity", + "Related Work", + "The Spearman's Rank Correlation Coefficient Similarity Measure", + "The Spearman's Rank Correlation Coefficient Similarity Measure ::: Spearman's Rank Correlation Coefficient", + "The Spearman's Rank Correlation Coefficient Similarity Measure ::: Spearman's Rank Correlation Coefficient ::: An Illustration of the Ranking TF-IDF Vectors", + "Experiments", + "Experiments ::: Comparison Between Similarity Measures", + "Experiments ::: Non-linearity of Documents", + "Conclusion and Future Work" + ], + "paragraphs": [ + [ + "Over the past few years, the term big data has become an important key point for research into data mining and information retrieval. Through the years, the quantity of data managed across enterprises has evolved from a simple and imperceptible task to an extent to which it has become the central performance improvement problem. In other words, it evolved to be the next frontier for innovation, competition and productivity BIBREF0. Extracting knowledge from data is now a very competitive environment. Many companies process vast amounts of customer/user data in order to improve the quality of experience (QoE) of their customers. For instance, a typical use-case scenario would be a book seller that performs an automatic extraction of the content of the books a customer has bought, and subsequently extracts knowledge of what customers prefer to read. The knowledge extracted could then be used to recommend other books. Book recommending systems are typical examples where data mining techniques should be considered as the primary tool for making future decisions BIBREF1.", + "KE from TDs is an essential field of research in data mining and it certainly requires techniques that are reliable and accurate in order to neutralize (or even eliminate) uncertainty in future decisions. Grouping TDs based on their content and mutual key information is referred to as clustering. Clustering is mostly performed with respect to a measure of similarity between TDs, which must be represented as vectors in a vector space beforehand BIBREF2. News aggregation engines can be considered as a typical representative where such techniques are extensively applied as a sub-field of natural language processing (NLP).", + "In this paper we present a new technique for measuring similarity between TDs, represented in a vector space, based on SRCC - \"a statistical measure of association between two things\" BIBREF3, which in this case things refer to TDs. The mathematical properties of SRCC (such as the ability to detect nonlinear correlation) make it compelling to be researched into. Our motivation is to provide a new technique of improving the quality of KE based on the well-known association measure SRCC, as opposed to other well-known TD similarity measures.", + "The paper is organized as follows: Section SECREF2 gives a brief overview of the vector space representation of a TD and the corresponding similarity measures, in Section SECREF3 we address conducted research of the role of SRCC in data mining and trend prediction. Section SECREF4 is a detailed description of the proposed technique, and later, in Section SECREF5 we present clustering and classification experiments conducted on several sets of TDs, while Section SECREF6 summarizes our research and contribution to the broad area of statistical text analysis." + ], + [ + "In this section we provide a brief background of vector space representation of TDs and existing similarity measures that have been widely used in statistical text analysis. To begin with, we consider the representation of documents." + ], + [ + "A document $d$ can be defined as a finite sequence of terms (independent textual entities within a document, for example, words), namely $d=(t_1,t_2,\\dots ,t_n)$. A general idea is to associate weight to each term $t_i$ within $d$, such that", + "which has proven superior in prior extensive research BIBREF4. The most common weight measure is Term Frequency - Inverse Document Frequency (TF-IDF). TF is the frequency of a term within a single document, and IDF represents the importance, or uniqueness of a term within a set of documents $D=\\lbrace d_1, d_2, \\dots ,d_m\\rbrace $. TF-IDF is defined as follows:", + "where", + "such that $f$ is the number of occurrences of $t$ in $d$ and $\\log $ is used to avoid very small values close to zero.", + "Having these measures defined, it becomes obvious that each $w_i$, for $i=1,\\dots ,n$ is assigned the TF-IDF value of the corresponding term. It turns out that each document is represented as a vector of TF-IDF weights within a vector space model (VSM) with its properties BIBREF5." + ], + [ + "Different ways of computing the similarity of two vector exist. There are two main approaches in similarity computation:", + "Deterministic - similarity measures exploiting algebraic properties of vectors and their geometrical interpretation. These include, for instance, cosine similarity (CS), Jaccard coefficients (for binary representations), etc.", + "Stochastic - similarity measures in which uncertainty is taken into account. These include, for instance, statistics such as Pearson's Correlation Coefficient (PCC) BIBREF6.", + "Let $\\mathbf {u}$ and $\\mathbf {v}$ be the vector representations of two documents $d_1$ and $d_2$. Cosine similarity simply measures $cos\\theta $, where $\\theta $ is the angle between $\\mathbf {u}$ and $\\mathbf {v}$", + "(cosine similarity)", + "(PCC)", + "where", + "All of the above measures are widely used and have proven efficient, but an important aspect is the lack of importance of the order of terms in textual data. It is easy for one to conclude that, two documents containing a single sentence each, but in a reverse order of terms, most deterministic methods fail to express that these are actually very similar. On the other hand, PCC detects only linear correlation, which constraints the diversity present in textual data. In the following section, we study relevant research in solving this problem, and then in Sections SECREF4 and SECREF5 we present our solution and results." + ], + [ + "A significant number of similarity measures have been proposed and this topic has been thoroughly elaborated. Its main application is considered to be clustering and classification of textual data organized in TDs. In this section, we provide an overview of relevant research on this topic, to which we can later compare our proposed technique for computing vector similarity.", + "KE (also referred to as knowledge discovery) techniques are used to extract information from unstructured data, which can be subsequently used for applying supervised or unsupervised learning techniques, such as clustering and classification of the content BIBREF7. Text clustering should address several challenges such as vast amounts of data, very high dimensionality of more than 10,000 terms (dimensions), and most importantly - an understandable description of the clusters BIBREF8, which essentially implies the demand for high quality of extracted information.", + "Regarding high quality KE and information accuracy, much effort has been put into improving similarity measurements. An improvement based on linear algebra, known as Singular Value Decomposition (SVD), is oriented towards word similarity, but instead, its main application is document similarity BIBREF9. Alluring is the fact that this measure takes the advantage of synonym recognition and has been used to achieve human-level scores on multiple-choice synonym questions from the Test of English as a Foreign Language (TOEFL) in a technique known as Latent Semantic Analysis (LSA) BIBREF10 BIBREF5.", + "Other semantic term similarity measures have been also proposed, based on information exclusively derived from large corpora of words, such as Pointwise Mutual Information (PMI), which has been reported to have achieved a large degree of correctness in the synonym questions in the TOEFL and SAT tests BIBREF11.", + "Moreover, normalized knowledge-based measures, such as Leacock & Chodrow BIBREF12, Lesk (\"how to tell a pine cone from an ice-cream cone\" BIBREF13, or measures for the depth of two concepts (preferably vebs) in the Word-Net taxonomy BIBREF14 have experimentally proven to be efficient. Their accuracy converges to approximately 69%, Leacock & Chodrow and Lesk have showed the highest precision, and having them combined turns out to be the approximately optimal solution BIBREF11." + ], + [ + "The main idea behind our proposed technique is to introduce uncertainty in the calculations of the similarity between TDs represented in a vector space model, based on the nonlinear properties of SRCC. Unlike PCC, which is only able to detect linear correlation, SRCC's nonlinear ability provides a convenient way of taking different ordering of terms into account." + ], + [ + "The Spreaman's Rank Correlation Coefficient BIBREF3, denoted $\\rho $, has a from which is very similar to PCC. Namely, for $n$ raw scores $U_i, V_i$ for $i=1,\\dots ,n$ denoting TF-IDF values for two document vectors $\\mathbf {U}, \\mathbf {V}$,", + "where $u_i$ and $v_i$ are the corresponding ranks of $U_i$ and $V_i$, for $i=0,\\dots ,n-1$. A metric to assign the ranks of each of the TF-IDF values has to be determined beforehand. Each $U_i$ is assigned a rank value $u_i$, such that $u_i=0,1,\\dots ,n-1$. It is important to note that the metric by which the TF-IDF values are ranked is essentially their sorting criteria. A convenient way of determining this criteria when dealing with TF-IDF values, which emphasize the importance of a term within a TD set, is to sort these values in an ascending order. Thus, the largest (or most important) TF-IDF value within a TD vector is assigned the rank value of $n-1$, and the least important is assigned a value of 0." + ], + [ + "Consider two TDs $d_1$ and $d_2$, each containing a single sentence.", + "Document 1: John had asked Mary to marry him before she left.", + "Document 2: Before she left, Mary was asked by John to be his wife.", + "Now consider these sentences lemmatized:", + "Document 1: John have ask Mary marry before leave.", + "Document 2: Before leave Mary ask John his wife.", + "Let us now represent $d_1$ and $d_2$ as TF-IDF vectors for the vocabulary in our small corpus.", + "The results in Table TABREF7 show that SRCC performs much better in knowledge extraction. The two documents' contents contain the same idea expressed by terms in a different order that John had asked Mary to marry him before she left. It is obvious that cosine similarity cannot recognize this association, but SRCC has successfully recognized it and produced a similarity value of -0.285714.", + "SRCC is essentially conducive to semantic similarity. Rising the importance of a term in a TD will eventually rise its importance in another TD. But if the two TDs are of different size, the terms' importance values will also differ, by which a nonlinear association will emerge. This association will not be recognized by PCC at all (as it only detects linear association), but SRCC will definitely catch this detail and produce the desirable similarity value. The idea is to use SRCC to catch such terms which drive the semantic context of a TD, which will follow a nonlinear and lie on a polynomial curve, and not on the line $x=y$.", + "In our approach, we use a non-standard measure of similarity in textual data with simple and common frequency values, such as TF-IDF, in contrast to the statement that simple frequencies are not enough for high-quality knowledge extraction BIBREF5. In the next section, we will present our experiments and discuss the results we have obtained." + ], + [ + "In order to test our proposed approach, we have conducted a series of experiments. In this section, we briefly discuss the outcome and provide a clear view of whether our approach is suitable for knowledge extraction from textual data in a semantic context.", + "We have used a dataset of 14 TDs to conduct our experiments. There are several subjects on which their content is based: (aliens, stories, law, news) BIBREF15." + ], + [ + "In this part, we have compared the similarity values produced by each of the similarity measures CS, SRCC and PCC. We have picked a few notable results and they are summarized in Table TABREF9 below.", + "In Table TABREF9 that SRCC mostly differs from CS and PCC, which also differ in some cases.For instance, $d_1$ refers to leadership in the nineties, while $d_5$ refers to the family and medical lead act of 1993. We have empirically observed that the general topics discussed in these two textual documents are very different. Namely, discusses different frameworks for leadership empowerment, while $d_5$ discusses medical treatment and self-care of employees. We have observed that the term employee is the only connection between $d_1$ and $d_5$. The similarity value of CS of 0.36 is very unreal in this case, while PCC (0.05), and especially SRCC (0.0018) provide a much more realistic view of the semantic knowledge aggregated in these documents. Another example are $d_8$ and $d_9$. The contents of these documents are very straightforward and very similar, because they discuss aliens seen by Boeing-747 pilots and $d_9$ discusses angels that were considered to be aliens. It is obvious that SRCC is able to detect this association as good as CS and PCC which are very good in such straightforward cases.", + "We have observed that SRCC does not perform worse than any other of these similarity measures. It does not always produce the most suitable similarity value, but it indeed does perform at least equally good as other measures. The values in Table TABREF9 are very small, and suggest that SRCC performs well in extracting tiny associations in such cases. It is mostly a few times larger than CS and PCC when there actually exist associations between the documents.", + "These results are visually summarized in Figure FIGREF10. The two above-described examples can be clearly seen as standing out." + ], + [ + "In this part we will briefly present the nonlinear association between some of the TDs we have used in our experiments. Our purpose is to point out that $(d_6,d_{10})$ and $(d_7,d_{12})$ are the pairs where SRCC is the most appropriate measure for the observed content, and as such, it is able to detect the nonlinear association between them. This can be seen in Figure FIGREF12 below. The straightforward case of $d_8$ and $d_9$ also stands out here (SRCC can also detect it very well).", + "The obtained results showed that our technique shows good performance on similarity computing, although it is not a perfect measure. But, it sure comes close to convenient and widely used similarity measures such as CS and PCC. The next section provides a conclusion of our research and suggestions for further work." + ], + [ + "In this paper we have presented a non-standard technique for computing the similarity between TF-IDF vectors. We have propagated our idea and contributed a portion of new knowledge in this field of text analysis. We have proposed a technique that is widely used in similar fields, and our goal is to provide starting information to other researches in this area. We consider our observations promising and they should be extensively researched.", + "Our experiments have proved that our technique should be a subject for further research. Our future work will concentrate on the implementation of machine learning techniques, such as clustering and subsequent classification of textual data. We expect an information of good quality to be extracted. To summarize, the rapidly emerging area of big data and information retrieval is where our technique should reside and where it should be applied." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0391/instruction.md b/qasper-0391/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..8035439ad49c9725529f983a79ba2c77d2aede2c --- /dev/null +++ b/qasper-0391/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization + +Question: How big is unrelated corpus universal embedding is traned on? \ No newline at end of file diff --git a/qasper-0396/instruction.md b/qasper-0396/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..34b057bd4835b89854f62138bb9bba7c221794f1 --- /dev/null +++ b/qasper-0396/instruction.md @@ -0,0 +1,89 @@ +Name of Paper: A Deep Neural Architecture for Sentence-level Sentiment Classification in Twitter Social Networking + +Question: Are results reported only on English datasets? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Basic idea", + "Data Preparation", + "Preprocessing", + "Semantic Rules (SR)", + "Representation Levels", + "Deep Learning Module", + "Regularization", + " Experimental setups", + "Experimental results", + "Analysis", + "Conclusions" + ], + "paragraphs": [ + [ + "Twitter sentiment classification have intensively researched in recent years BIBREF0 BIBREF1 . Different approaches were developed for Twitter sentiment classification by using machine learning such as Support Vector Machine (SVM) with rule-based features BIBREF2 and the combination of SVMs and Naive Bayes (NB) BIBREF3 . In addition, hybrid approaches combining lexicon-based and machine learning methods also achieved high performance described in BIBREF4 . However, a problem of traditional machine learning is how to define a feature extractor for a specific domain in order to extract important features.", + "Deep learning models are different from traditional machine learning methods in that a deep learning model does not depend on feature extractors because features are extracted during training progress. The use of deep learning methods becomes to achieve remarkable results for sentiment analysis BIBREF5 BIBREF6 BIBREF7 . Some researchers used Convolutional Neural Network (CNN) for sentiment classification. CNN models have been shown to be effective for NLP. For example, BIBREF6 proposed various kinds of CNN to learn sentiment-bearing sentence vectors, BIBREF5 adopted two CNNs in character-level to sentence-level representation for sentiment analysis. BIBREF7 constructs experiments on a character-level CNN for several large-scale datasets. In addition, Long Short-Term Memory (LSTM) is another state-of-the-art semantic composition model for sentiment classification with many variants described in BIBREF8 . The studies reveal that using a CNN is useful in extracting information and finding feature detectors from texts. In addition, a LSTM can be good in maintaining word order and the context of words. However, in some important aspects, the use of CNN or LSTM separately may not capture enough information.", + "Inspired by the models above, the goal of this research is using a Deep Convolutional Neural Network (DeepCNN) to exploit the information of characters of words in order to support word-level embedding. A Bi-LSTM produces a sentence-wide feature representation based on these embeddings. The Bi-LSTM is a version of BIBREF9 with Full Gradient described in BIBREF10 . In addition, the rules-based approach also effects classification accuracy by focusing on important sub-sentences expressing the main sentiment of a tweet while removing unnecessary parts of a tweet. The paper makes the following contributions:", + "The organization of the present paper is as follows: In section 2, we describe the model architecture which introduces the structure of the model. We explain the basic idea of model and the way of constructing the model. Section 3 show results and analysis and section 4 summarize this paper." + ], + [ + "Our proposed model consists of a deep learning classifier and a tweet processor. The deep learning classifier is a combination of DeepCNN and Bi-LSTM. The tweet processor standardizes tweets and then applies semantic rules on datasets. We construct a framework that treats the deep learning classifier and the tweet processor as two distinct components. We believe that standardizing data is an important step to achieve high accuracy. To formulate our problem in increasing the accuracy of the classifier, we illustrate our model in Figure. FIGREF4 as follows:", + "Tweets are firstly considered via a processor based on preprocessing steps BIBREF0 and the semantic rules-based method BIBREF11 in order to standardize tweets and capture only important information containing the main sentiment of a tweet.", + "We use DeepCNN with Wide convolution for character-level embeddings. A wide convolution can learn to recognize specific n-grams at every position in a word that allows features to be extracted independently of these positions in the word. These features maintain the order and relative positions of characters. A DeepCNN is constructed by two wide convolution layers and the need of multiple wide convolution layers is widely accepted that a model constructing by multiple processing layers have the ability to learn representations of data with higher levels of abstraction BIBREF12 . Therefore, we use DeepCNN for character-level embeddings to support morphological and shape information for a word. The DeepCNN produces INLINEFORM0 global fixed-sized feature vectors for INLINEFORM1 words.", + "A combination of the global fixed-size feature vectors and word-level embedding is fed into Bi-LSTM. The Bi-LSTM produces a sentence-level representation by maintaining the order of words.", + "Our work is philosophically similar to BIBREF5 . However, our model is distinguished with their approaches in two aspects:", + "Using DeepCNN with two wide convolution layers to increase representation with multiple levels of abstraction.", + "Integrating global character fixed-sized feature vectors with word-level embedding to extract a sentence-wide feature set via Bi-LSTM. This deals with three main problems: (i) Sentences have any different size; (ii) The semantic and the syntactic of words in a sentence are captured in order to increase information for a word; (iii) Important information of characters that can appear at any position in a word are extracted.", + "In sub-section B, we introduce various kinds of dataset. The modules of our model are constructed in other sub-sections." + ], + [ + "Stanford - Twitter Sentiment Corpus (STS Corpus): STS Corpus contains 1,600K training tweets collected by a crawler from BIBREF0 . BIBREF0 constructed a test set manually with 177 negative and 182 positive tweets. The Stanford test set is small. However, it has been widely used in different evaluation tasks BIBREF0 BIBREF5 BIBREF13 .", + "Sanders - Twitter Sentiment Corpus: This dataset consists of hand-classified tweets collected by using search terms: INLINEFORM0 , #google, #microsoft and #twitter. We construct the dataset as BIBREF14 for binary classification.", + "Health Care Reform (HCR): This dataset was constructed by crawling tweets containing the hashtag #hcr BIBREF15 . Task is to predict positive/negative tweets BIBREF14 ." + ], + [ + "We firstly take unique properties of Twitter in order to reduce the feature space such as Username, Usage of links, None, URLs and Repeated Letters. We then process retweets, stop words, links, URLs, mentions, punctuation and accentuation. For emoticons, BIBREF0 revealed that the training process makes the use of emoticons as noisy labels and they stripped the emoticons out from their training dataset because BIBREF0 believed that if we consider the emoticons, there is a negative impact on the accuracies of classifiers. In addition, removing emoticons makes the classifiers learns from other features (e.g. unigrams and bi-grams) presented in tweets and the classifiers only use these non-emoticon features to predict the sentiment of tweets. However, there is a problem is that if the test set contains emoticons, they do not influence the classifiers because emoticon features do not contain in its training data. This is a limitation of BIBREF0 , because the emoticon features would be useful when classifying test data. Therefore, we keep emoticon features in the datasets because deep learning models can capture more information from emoticon features for increasing classification accuracy." + ], + [ + "In Twitter social networking, people express their opinions containing sub-sentences. These sub-sentences using specific PoS particles (Conjunction and Conjunctive adverbs), like \"but, while, however, despite, however\" have different polarities. However, the overall sentiment of tweets often focus on certain sub-sentences. For example:", + "@lonedog bwahahah...you are amazing! However, it was quite the letdown.", + "@kirstiealley my dentist is great but she's expensive...=(", + "In two tweets above, the overall sentiment is negative. However, the main sentiment is only in the sub-sentences following but and however. This inspires a processing step to remove unessential parts in a tweet. Rule-based approach can assists these problems in handling negation and dealing with specific PoS particles led to effectively affect the final output of classification BIBREF11 BIBREF16 . BIBREF11 summarized a full presentation of their semantic rules approach and devised ten semantic rules in their hybrid approach based on the presentation of BIBREF16 . We use five rules in the semantic rules set because other five rules are only used to compute polarity of words after POS tagging or Parsing steps. We follow the same naming convention for rules utilized by BIBREF11 to represent the rules utilized in our proposed method. The rules utilized in the proposed method are displayed in Table TABREF15 in which is included examples from STS Corpus and output after using the rules. Table TABREF16 illustrates the number of processed sentences on each dataset." + ], + [ + "To construct embedding inputs for our model, we use a fixed-sized word vocabulary INLINEFORM0 and a fixed-sized character vocabulary INLINEFORM1 . Given a word INLINEFORM2 is composed from characters INLINEFORM3 , the character-level embeddings are encoded by column vectors INLINEFORM4 in the embedding matrix INLINEFORM5 , where INLINEFORM6 is the size of the character vocabulary. For word-level embedding INLINEFORM7 , we use a pre-trained word-level embedding with dimension 200 or 300. A pre-trained word-level embedding can capture the syntactic and semantic information of words BIBREF17 . We build every word INLINEFORM8 into an embedding INLINEFORM9 which is constructed by two sub-vectors: the word-level embedding INLINEFORM10 and the character fixed-size feature vector INLINEFORM11 of INLINEFORM12 where INLINEFORM13 is the length of the filter of wide convolutions. We have INLINEFORM14 character fixed-size feature vectors corresponding to word-level embedding in a sentence." + ], + [ + "DeepCNN in the deep learning module is illustrated in Figure. FIGREF22 . The DeepCNN has two wide convolution layers. The first layer extract local features around each character windows of the given word and using a max pooling over character windows to produce a global fixed-sized feature vector for the word. The second layer retrieves important context characters and transforms the representation at previous level into a representation at higher abstract level. We have INLINEFORM0 global character fixed-sized feature vectors for INLINEFORM1 words.", + "In the next step of Figure. FIGREF4 , we construct the vector INLINEFORM0 by concatenating the word-level embedding with the global character fixed-size feature vectors. The input of Bi-LSTM is a sequence of embeddings INLINEFORM1 . The use of the global character fixed-size feature vectors increases the relationship of words in the word-level embedding. The purpose of this Bi-LSTM is to capture the context of words in a sentence and maintain the order of words toward to extract sentence-level representation. The top of the model is a softmax function to predict sentiment label. We describe in detail the kinds of CNN and LSTM that we use in next sub-part 1 and 2.", + "The one-dimensional convolution called time-delay neural net has a filter vector INLINEFORM0 and take the dot product of filter INLINEFORM1 with each m-grams in the sequence of characters INLINEFORM2 of a word in order to obtain a sequence INLINEFORM3 : DISPLAYFORM0 ", + "Based on Equation 1, we have two types of convolutions that depend on the range of the index INLINEFORM0 . The narrow type of convolution requires that INLINEFORM1 and produce a sequence INLINEFORM2 . The wide type of convolution does not require on INLINEFORM3 or INLINEFORM4 and produce a sequence INLINEFORM5 . Out-of-range input values INLINEFORM6 where INLINEFORM7 or INLINEFORM8 are taken to be zero. We use wide convolution for our model.", + "Given a word INLINEFORM0 composed of INLINEFORM1 characters INLINEFORM2 , we take a character embedding INLINEFORM3 for each character INLINEFORM4 and construct a character matrix INLINEFORM5 as following Equation. 2: DISPLAYFORM0 ", + "The values of the embeddings INLINEFORM0 are parameters that are optimized during training. The trained weights in the filter INLINEFORM1 correspond to a feature detector which learns to recognize a specific class of n-grams. The n-grams have size INLINEFORM2 . The use of a wide convolution has some advantages more than a narrow convolution because a wide convolution ensures that all weights of filter reach the whole characters of a word at the margins. The resulting matrix has dimension INLINEFORM3 .", + "Long Short-Term Memory networks usually called LSTMs are a improved version of RNN. The core idea behind LSTMs is the cell state which can maintain its state over time, and non-linear gating units which regulate the information flow into and out of the cell. The LSTM architecture that we used in our proposed model is described in BIBREF9 . A single LSTM memory cell is implemented by the following composite function: DISPLAYFORM0 DISPLAYFORM1 ", + "where INLINEFORM0 is the logistic sigmoid function, INLINEFORM1 and INLINEFORM2 are the input gate, forget gate, output gate, cell and cell input activation vectors respectively. All of them have a same size as the hidden vector INLINEFORM3 . INLINEFORM4 is the hidden-input gate matrix, INLINEFORM5 is the input-output gate matrix. The bias terms which are added to INLINEFORM6 and INLINEFORM7 have been omitted for clarity. In addition, we also use the full gradient for calculating with full backpropagation through time (BPTT) described in BIBREF10 . A LSTM gradients using finite differences could be checked and making practical implementations more reliable." + ], + [ + "For regularization, we use a constraint on INLINEFORM0 of the weight vectors BIBREF18 ." + ], + [ + "For the Stanford Twitter Sentiment Corpus, we use the number of samples as BIBREF5 . The training data is selected 80K tweets for a training data and 16K tweets for the development set randomly from the training data of BIBREF0 . We conduct a binary prediction for STS Corpus.", + "For Sander dataset, we use standard 10-fold cross validation as BIBREF14 . We construct the development set by selecting 10% randomly from 9-fold training data.", + "In Health Care Reform Corpus, we also select 10% randomly for the development set in a training set and construct as BIBREF14 for comparison. We describe the summary of datasets in Table III.", + "for all datasets, the filter window size ( INLINEFORM0 ) is 7 with 6 feature maps each for the first wide convolution layer, the second wide convolution layer has a filter window size of 5 with 14 feature maps each. Dropout rate ( INLINEFORM1 ) is 0.5, INLINEFORM2 constraint, learning rate is 0.1 and momentum of 0.9. Mini-batch size for STS Corpus is 100 and others are 4. In addition, training is done through stochastic gradient descent over shuffled mini-batches with Adadelta update rule BIBREF19 .", + "we use the publicly available Word2Vec trained from 100 billion words from Google and TwitterGlove of Stanford is performed on aggregated global word-word co-occurrence statistics from a corpus. Word2Vec has dimensionality of 300 and Twitter Glove have dimensionality of 200. Words that do not present in the set of pre-train words are initialized randomly." + ], + [ + "Table IV shows the result of our model for sentiment classification against other models. We compare our model performance with the approaches of BIBREF0 BIBREF5 on STS Corpus. BIBREF0 reported the results of Maximum Entropy (MaxEnt), NB, SVM on STS Corpus having good performance in previous time. The model of BIBREF5 is a state-of-the-art so far by using a CharSCNN. As can be seen, 86.63 is the best prediction accuracy of our model so far for the STS Corpus.", + "For Sanders and HCR datasets, we compare results with the model of BIBREF14 that used a ensemble of multiple base classifiers (ENS) such as NB, Random Forest (RF), SVM and Logistic Regression (LR). The ENS model is combined with bag-of-words (BoW), feature hashing (FH) and lexicons. The model of BIBREF14 is a state-of-the-art on Sanders and HCR datasets. Our models outperform the model of BIBREF14 for the Sanders dataset and HCR dataset." + ], + [ + "As can be seen, the models with SR outperforms the model with no SR. Semantic rules is effective in order to increase classification accuracy. We evaluate the efficiency of SR for the model in Table V of our full paper . We also conduct two experiments on two separate models: DeepCNN and Bi-LSTM in order to show the effectiveness of combination of DeepCNN and Bi-LSTM. In addition, the model using TwitterGlove outperform the model using GoogleW2V because TwitterGlove captures more information in Twitter than GoogleW2V. These results show that the character-level information and SR have a great impact on Twitter Data. The pre-train word vectors are good, universal feature extractors. The difference between our model and other approaches is the ability of our model to capture important features by using SR and combine these features at high benefit. The use of DeepCNN can learn a representation of words in higher abstract level. The combination of global character fixed-sized feature vectors and a word embedding helps the model to find important detectors for particles such as 'not' that negate sentiment and potentiate sentiment such as 'too', 'so' standing beside expected features. The model not only learns to recognize single n-grams, but also patterns in n-grams lead to form a structure significance of a sentence." + ], + [ + "In the present work, we have pointed out that the use of character embeddings through a DeepCNN to enhance information for word embeddings built on top of Word2Vec or TwitterGlove improves classification accuracy in Tweet sentiment classification. Our results add to the well-establish evidence that character vectors are an important ingredient for word-level in deep learning for NLP. In addition, semantic rules contribute handling non-essential sub-tweets in order to improve classification accuracy." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0397/instruction.md b/qasper-0397/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..60dd49ebb6ae54b6fe38edcd69618d011568aa2a --- /dev/null +++ b/qasper-0397/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: A Deep Neural Architecture for Sentence-level Sentiment Classification in Twitter Social Networking + +Question: Which three Twitter sentiment classification datasets are used for experiments? \ No newline at end of file diff --git a/qasper-0398/instruction.md b/qasper-0398/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..010486d96a36abdf94f537f97b25b7f53df6f068 --- /dev/null +++ b/qasper-0398/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: A Deep Neural Architecture for Sentence-level Sentiment Classification in Twitter Social Networking + +Question: What semantic rules are proposed? \ No newline at end of file diff --git a/qasper-0412/instruction.md b/qasper-0412/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..60d5af9dceb2d9e6bf7f743297ebc5a5542aa2a5 --- /dev/null +++ b/qasper-0412/instruction.md @@ -0,0 +1,84 @@ +Name of Paper: CN-CELEB: a challenging Chinese speaker recognition dataset + +Question: Do they experiment with cross-genre setups? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "The CN-Celeb dataset ::: Data description", + "The CN-Celeb dataset ::: Challenges with CN-Celeb", + "The CN-Celeb dataset ::: Collection pipeline", + "Experiments on speaker recognition", + "Experiments on speaker recognition ::: Data", + "Experiments on speaker recognition ::: Settings", + "Experiments on speaker recognition ::: Basic results", + "Experiments on speaker recognition ::: Further comparison", + "Conclusions" + ], + "paragraphs": [ + [ + "Speaker recognition including identification and verification, aims to recognize claimed identities of speakers. After decades of research, performance of speaker recognition systems has been vastly improved, and the technique has been deployed to a wide range of practical applications. Nevertheless, the present speaker recognition approaches are still far from reliable in unconstrained conditions where uncertainties within the speech recordings could be arbitrary. These uncertainties might be caused by multiple factors, including free text, multiple channels, environmental noises, speaking styles, and physiological status. These uncertainties make the speaker recognition task highly challenging BIBREF0, BIBREF1.", + "Researchers have devoted much effort to address the difficulties in unconstrained conditions. Early methods are based on probabilistic models that treat these uncertainties as an additive Gaussian noise. JFA BIBREF2, BIBREF3 and PLDA BIBREF4 are the most famous among such models. These models, however, are shallow and linear, and therefore cannot deal with the complexity of real-life applications. Recent advance in deep learning methods offers a new opportunity BIBREF5, BIBREF6, BIBREF7, BIBREF8. Resorting to the power of deep neural networks (DNNs) in representation learning, these methods can remove unwanted uncertainties by propagating speech signals through the DNN layer by layer and retain speaker-relevant features only BIBREF9. Significant improvement in robustness has been achieved by the DNN-based approach BIBREF10, which makes it more suitable for applications in unconstrained conditions.", + "The success of DNN-based methods, however, largely relies on a large amount of data, in particular data that involve the true complexity in unconstrained conditions. Unfortunately, most existing datasets for speaker recognition are collected in constrained conditions, where the acoustic environment, channel and speaking style do not change significantly for each speaker BIBREF11, BIBREF12, BIBREF13. These datasets tend to deliver over optimistic performance and do not meet the request of research on speaker recognition in unconstrained conditions.", + "To address this shortage in datasets, researchers have started to collect data `in the wild'. The most successful `wild' dataset may be VoxCeleb BIBREF14, BIBREF15, which contains millions of utterances from over thousands of speakers. The utterances were collected from open-source media using a fully automated pipeline based on computer vision techniques, in particular face detection, tracking and recognition, plus video-audio synchronization. The automated pipeline is almost costless, and thus greatly improves the efficiency of data collection.", + "In this paper, we re-implement the automated pipeline of VoxCeleb and collect a new large-scale speaker dataset, named CN-Celeb. Compared with VoxCeleb, CN-Celeb has three distinct features:", + "CN-Celeb specially focuses on Chinese celebrities, and contains more than $130,000$ utterances from $1,000$ persons.", + "CN-Celeb covers more genres of speech. We intentionally collected data from 11 genres, including entertainment, interview, singing, play, movie, vlog, live broadcast, speech, drama, recitation and advertisement. The speech of a particular speaker may be in more than 5 genres. As a comparison, most of the utterances in VoxCeleb were extracted from interview videos. The diversity in genres makes our database more representative for the true scenarios in unconstrained conditions, but also more challenging.", + "CN-Celeb is not fully automated, but involves human check. We found that more complex the genre is, more errors the automated pipeline tends to produce. Ironically, the error-pron segments could be highly valuable as they tend to be boundary samples. We therefore choose a two-stage strategy that employs the automated pipeline to perform pre-selection, and then perform human check.", + "The rest of the paper is organized as follows. Section SECREF2 presents a detailed description for CN-Celeb, and Section SECREF3 presents more quantitative comparisons between CN-Celeb and VoxCeleb on the speaker recognition task. Section SECREF4 concludes the entire paper." + ], + [ + "The original purpose of the CN-Celeb dataset is to investigate the true difficulties of speaker recognition techniques in unconstrained conditions, and provide a resource for researchers to build prototype systems and evaluate the performance. Ideally, it can be used as a standalone data source, and can be also used with other datasets together, in particular VoxCeleb which is free and large. For this reason, CN-Celeb tries to be distinguished from but also complementary to VoxCeleb from the beginning of the design. This leads to three features that we have discussed in the previous section: Chinese focused, complex genres, and quality guarantee by human check.", + "In summary, CN-Celeb contains over $130,000$ utterances from $1,000$ Chinese celebrities. It covers 11 genres and the total amount of speech waveforms is 274 hours. Table TABREF5 gives the data distribution over the genres, and Table TABREF6 presents the data distribution over the length of utterances." + ], + [ + "Table TABREF13 summarizes the main difference between CN-Celeb and VoxCeleb. Compared to VoxCeleb, CN-Celeb is a more complex dataset and more challenging for speaker recognition research. More details of these challenges are as follows.", + "Most of the utterances involve real-world noise, including ambient noise, background babbling, music, cheers and laugh.", + "A certain amount of utterances involve strong and overlapped background speakers, especially in the dram and movie genres.", + "Most of speakers have different genres of utterances, which results in significant variation in speaking styles.", + "The utterances of the same speaker may be recorded at different time and with different devices, leading to serious cross-time and cross-channel problems.", + "Most of the utterances are short, which meets the scenarios of most real applications but leads to unreliable decision." + ], + [ + "CN-Celeb was collected following a two-stage strategy: firstly we used an automated pipeline to extract potential segments of the Person of Interest (POI), and then applied a human check to remove incorrect segments. This process is much faster than purely human-based segmentation, and reduces errors caused by a purely automated process.", + "Briefly, the automated pipeline we used is similar to the one used to collect VoxCeleb1 BIBREF14 and VoxCeleb2 BIBREF15, though we made some modification to increase efficiency and precision. Especially, we introduced a new face-speaker double check step that fused the information from both the image and speech signals to increase the recall rate while maintaining the precision.", + "The detailed steps of the collection process are summarized as follows.", + "STEP 1. POI list design. We manually selected $1,000$ Chinese celebrities as our target speakers. These speakers were mostly from the entertainment sector, such as singers, drama actors/actrees, news reporters, interviewers. Region diversity was also taken into account so that variation in accent was covered.", + "STEP 2. Pictures and videos download. Pictures and videos of the $1,000$ POIs were downloaded from the data source (https://www.bilibili.com/) by searching for the names of the persons. In order to specify that we were searching for POI names, the word `human' was added in the search queries. The downloaded videos were manually examined and were categorized into the 11 genres.", + "STEP 3. Face detection and tracking. For each POI, we first obtained the portrait of the person. This was achieved by detecting and clipping the face images from all pictures of that person. The RetinaFace algorithm was used to perform the detection and clipping BIBREF16. Afterwards, video segments that contain the target person were extracted. This was achieved by three steps: (1) For each frame, detect all the faces appearing in the frame using RetinaFace; (2) Determine if the target person appears by comparing the POI portrait and the faces detected in the frame. We used the ArcFace face recognition system BIBREF17 to perform the comparison; (3) Apply the MOSSE face tracking system BIBREF18 to produce face streams.", + "STEP 4. Active speaker verification. As in BIBREF14, an active speaker verification system was employed to verify if the speech was really spoken by the target person. This is necessary as it is possible that the target person appears in the video but the speech is from other persons. We used the SyncNet model BIBREF19 as in BIBREF14 to perform the task. This model was trained to detect if a stream of mouth movement and a stream of speech are synchronized. In our implementation, the stream of mouth movement was derived from the face stream produced by the MOSSE system.", + "STEP 5. Double check by speaker recognition.", + "Although SyncNet worked well for videos in simple genres, it failed for videos of complex genres such as movie and vlog. A possible reason is that the video content of these genres may change dramatically in time, which leads to unreliable estimation for the stream of the mouth movement, hence unreliable synchronization detection. In order to improve the robustness of the active speaker verification in complex genres, we introduced a double check procedure based on speaker recognition. The idea is simple: whenever the speaker recognition system states a very low confidence for the target speaker, the segment will be discarded even if the confidence from SyncNet is high; vice versa, if the speaker recognition system states a very high confidence, the segment will be retained. We used an off-the-shelf speaker recognition system BIBREF20 to perform this double check. In our study, this double check improved the recall rate by 30% absolutely.", + "STEP 6. Human check.", + "The segments produced by the above automated pipeline were finally checked by human. According to our experience, this human check is rather efficient: one could check 1 hour of speech in 1 hour. As a comparison, if we do not apply the automated pre-selection, checking 1 hour of speech requires 4 hours." + ], + [ + "In this section, we present a series of experiments on speaker recognition using VoxCeleb and CN-Celeb, to compare the complexity of the two datasets." + ], + [ + "VoxCeleb: The entire dataset involves two parts: VoxCeleb1 and VoxCeleb2. We used SITW BIBREF21, a subset of VoxCeleb1 as the evaluation set. The rest of VoxCeleb1 was merged with VoxCeleb2 to form the training set (simply denoted by VoxCeleb). The training set involves $1,236,567$ utterances from $7,185$ speakers, and the evaluation set involves $6,445$ utterances from 299 speakers (precisely, this is the Eval. Core set within SITW).", + "CN-Celeb: The entire dataset was split into two parts: the first part CN-Celeb(T) involves $111,260$ utterances from 800 speakers and was used as the training set; the second part CN-Celeb(E) involves $18,849$ utterances from 200 speakers and was used as the evaluation set." + ], + [ + "Two state-of-the-art baseline systems were built following the Kaldi SITW recipe BIBREF22: an i-vector system BIBREF3 and an x-vector system BIBREF10.", + "For the i-vector system, the acoustic feature involved 24-dimensional MFCCs plus the log energy, augmented by the first- and second-order derivatives. We also applied the cepstral mean normalization (CMN) and the energy-based voice active detection (VAD). The universal background model (UBM) consisted of $2,048$ Gaussian components, and the dimensionality of the i-vector space was 400. LDA was applied to reduce the dimensionality of the i-vectors to 150. The PLDA model was used for scoring BIBREF4.", + "For the x-vector system, the feature-learning component was a 5-layer time-delay neural network (TDNN). The slicing parameters for the five time-delay layers were: {$t$-2, $t$-1, $t$, $t$+1, $t$+2}, {$t$-2, $t$, $t$+2}, {$t$-3, $t$, $t$+3}, {$t$}, {$t$}. The statistic pooling layer computed the mean and standard deviation of the frame-level features from a speech segment. The size of the output layer was consistent with the number of speakers in the training set. Once trained, the activations of the penultimate hidden layer were read out as x-vectors. In our experiments, the dimension of the x-vectors trained on VoxCeleb was set to 512, while for CN-Celeb, it was set to 256, considering the less number of speakers in the training set. Afterwards, the x-vectors were projected to 150-dimensional vectors by LDA, and finally the PLDA model was employed to score the trials. Refer to BIBREF10 for more details." + ], + [ + "We first present the basic results evaluated on SITW and CN-Celeb(E). Both the front-end (i-vector or x-vector models) and back-end (LDA-PLDA) models were trained with the VoxCeleb training set. Note that for SITW, the averaged length of the utterances is more than 80 seconds, while this number is about 8 seconds for CN-Celeb(E). For a better comparison, we resegmented the data of SITW and created a new dataset denoted by SITW(S), where the averaged lengths of the enrollment and test utterances are 28 and 8 seconds, respectively. These numbers are similar to the statistics of CN-Celeb(E).", + "The results in terms of the equal error rate (EER) are reported in Table TABREF24. It can be observed that for both the i-vector system and the x-vector system, the performance on CN-Celeb(E) is much worse than the performance on SITW and SITW(S). This indicates that there is big difference between these two datasets. From another perspective, it demonstrates that the model trained with VoxCeleb does not generalize well, although it has achieved reasonable performance on data from a similar source (SITW)." + ], + [ + "To further compare CN-Celeb and VoxCeleb in a quantitative way, we built systems based on CN-Celeb and VoxCeleb, respectively. For a fair comparison, we randomly sampled 800 speakers from VoxCeleb and built a new dataset VoxCeleb(L) whose size is comparable to CN-Celeb(T). This data set was used for back-end (LDA-PLDA) training.", + "The experimental results are shown in Table TABREF26. Note that the performance of all the comparative experiments show the same trend with the i-vector system and the x-vector system, we therefore only analyze the i-vector results.", + "Firstly, it can be seen that the system trained purely on VoxCeleb obtained good performance on SITW(S) (1st row). This is understandable as VoxCeleb and SITW(S) were collected from the same source. For the pure CN-Celeb system (2nd row), although CN-Celeb(T) and CN-Celeb(E) are from the same source, the performance is still poor (14.24%). More importantly, with re-training the back-end model with VoxCeleb(L) (4th row), the performance on SITW becomes better than the same-source result on CN-Celeb(E) (11.34% vs 14.24%). All these results reconfirmed the significant difference between the two datasets, and indicates that CN-Celeb is more challenging than VoxCeleb." + ], + [ + "We introduced a free dataset CN-Celeb for speaker recognition research. The dataset contains more than $130k$ utterances from $1,000$ Chinese celebrities, and covers 11 different genres in real world. We compared CN-Celeb and VoxCeleb, a widely used dataset in speaker recognition, by setting up a series of experiments based on two state-of-the-art speaker recognition models. Experimental results demonstrated that CN-Celeb is significantly different from VoxCeleb, and it is more challenging for speaker recognition research. The EER performance we obtained in this paper suggests that in unconstrained conditions, the performance of the current speaker recognition techniques might be much worse than it was thought." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0415/instruction.md b/qasper-0415/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..73cb295fffab6ad76675423b72ac1192afab51e7 --- /dev/null +++ b/qasper-0415/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Conditional BERT Contextual Augmentation + +Question: On what datasets is the new model evaluated on? \ No newline at end of file diff --git a/qasper-0423/instruction.md b/qasper-0423/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..24a2108417a339245a0cc5a92b7275e8b89fa896 --- /dev/null +++ b/qasper-0423/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Recent Advances in Neural Question Generation + +Question: Do they survey multilingual aspects? \ No newline at end of file diff --git a/qasper-0424/instruction.md b/qasper-0424/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..6f57d27c11a57e44a6cc5e85b973d3873fb8c470 --- /dev/null +++ b/qasper-0424/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Recent Advances in Neural Question Generation + +Question: What learning paradigms do they cover in this survey? \ No newline at end of file diff --git a/qasper-0441/instruction.md b/qasper-0441/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..248ad7d3ef78149133abc239703604ca0ca5d6b6 --- /dev/null +++ b/qasper-0441/instruction.md @@ -0,0 +1,145 @@ +Name of Paper: Low-Level Linguistic Controls for Style Transfer and Content Preservation + +Question: How they know what are content words? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work ::: Style Transfer with Parallel Data", + "Related Work ::: Style Transfer without Parallel Data", + "Related Work ::: Controlling Linguistic Features", + "Related Work ::: Stylometry and the Digital Humanities", + "Models ::: Preliminary Classification Experiments", + "Models ::: Formal Model of Style", + "Models ::: Formal Model of Style ::: Reconstruction Task", + "Models ::: Neural Architecture", + "Models ::: Neural Architecture ::: Baseline Genre Model", + "Models ::: Neural Architecture ::: Training", + "Models ::: Neural Architecture ::: Selecting Controls for Style Transfer", + "Automatic Evaluations ::: BLEU Scores & Perplexity", + "Automatic Evaluations ::: Feature Control", + "Automatic Evaluations ::: Automatic Classification", + "Human Evaluation", + "Human Evaluation ::: Fluency Evaluation", + "Human Evaluation ::: Human Classification", + "Human Evaluation ::: The `Vampires in Space' Problem", + "Conclusion and Future Work", + "Acknowledgements" + ], + "paragraphs": [ + [ + "All text has style, whether it be formal or informal, polite or aggressive, colloquial, persuasive, or even robotic. Despite the success of style transfer in image processing BIBREF0, BIBREF1, there has been limited progress in the text domain, where disentangling style from content is particularly difficult.", + "To date, most work in style transfer relies on the availability of meta-data, such as sentiment, authorship, or formality. While meta-data can provide insight into the style of a text, it often conflates style with content, limiting the ability to perform style transfer while preserving content. Generalizing style transfer requires separating style from the meaning of the text itself. The study of literary style can guide us. For example, in the digital humanities and its subfield of stylometry, content doesn't figure prominently in practical methods of discriminating authorship and genres, which can be thought of as style at the level of the individual and population, respectively. Rather, syntactic and functional constructions are the most salient features.", + "In this work, we turn to literary style as a test-bed for style transfer, and build on work from literature scholars using computational techniques for analysis. In particular we draw on stylometry: the use of surface level features, often counts of function words, to discriminate between literary styles. Stylometry first saw success in attributing authorship to the disputed Federalist Papers BIBREF2, but is recently used by scholars to study things such as the birth of genres BIBREF3 and the change of author styles over time BIBREF4. The use of function words is likely not the way writers intend to express style, but they appear to be downstream realizations of higher-level stylistic decisions.", + "We hypothesize that surface-level linguistic features, such as counts of personal pronouns, prepositions, and punctuation, are an excellent definition of literary style, as borne out by their use in the digital humanities, and our own style classification experiments. We propose a controllable neural encoder-decoder model in which these features are modelled explicitly as decoder feature embeddings. In training, the model learns to reconstruct a text using only the content words and the linguistic feature embeddings. We can then transfer arbitrary content words to a new style without parallel data by setting the low-level style feature embeddings to be indicative of the target style.", + "This paper makes the following contributions:", + "A formal model of style as a suite of controllable, low-level linguistic features that are independent of content.", + "An automatic evaluation showing that our model fools a style classifier 84% of the time.", + "A human evaluation with English literature experts, including recommendations for dealing with the entanglement of content with style." + ], + [ + "Following in the footsteps of machine translation, style transfer in text has seen success by using parallel data. BIBREF5 use modern translations of Shakespeare plays to build a modern-to-Shakespearan model. BIBREF6 compile parallel data for formal and informal sentences, allowing them to successfully use various machine translation techniques. While parallel data may work for very specific styles, the difficulty of finding parallel texts dramatically limits this approach." + ], + [ + "There has been a decent amount of work on this approach in the past few years BIBREF7, BIBREF8, mostly focusing on variations of an encoder-decoder framework in which style is modeled as a monolithic style embedding. The main obstacle is often to disentangle style and content. However, it remains a challenging problem.", + "Perhaps the most successful is BIBREF9, who use a de-noising auto encoder and back translation to learn style without parallel data. BIBREF10 outline the benefits of automatically extracting style, and suggest there is a formal weakness of using linguistic heuristics. In contrast, we believe that monolithic style embeddings don't capture the existing knowledge we have about style, and will struggle to disentangle content." + ], + [ + "Several papers have worked on controlling style when generating sentences from restaurant meaning representations BIBREF11, BIBREF12. In each of these cases, the diversity in outputs is quite small given the constraints of the meaning representation, style is often constrained to interjections (like \u201cyeah\u201d), and there is no original style from which to transfer.", + "BIBREF13 investigate using stylistic parameters and content parameters to control text generation using a movie review dataset. Their stylistic parameters are created using word-level heuristics and they are successful in controlling these parameters in the outputs. Their success bodes well for our related approach in a style transfer setting, in which the content (not merely content parameters) is held fixed." + ], + [ + "Style, in literary research, is anything but a stable concept, but it nonetheless has a long tradition of study in the digital humanities. In a remarkably early quantitative study of literature, BIBREF14 charts sentence-level stylistic attributes specific to a number of novelists. Half a century later, BIBREF15 builds on earlier work in information theory by BIBREF16, and defines a literary text as consisting of two \u201cmaterials\": \u201cthe vocabulary, and some structural properties, the style, of its author.\"", + "Beginning with BIBREF2, statistical approaches to style, or stylometry, join the already-heated debates over the authorship of literary works. A noteable example of this is the \u201cDelta\" measure, which uses z-scores of function word frequencies BIBREF17. BIBREF18 find that Shakespeare added some material to a later edition of Thomas Kyd's The Spanish Tragedy, and that Christopher Marlowe collaborated with Shakespeare on Henry VI." + ], + [ + "The stylometric research cited above suggests that the most frequently used words, e.g. function words, are most discriminating of authorship and literary style. We investigate these claims using three corpora that have distinctive styles in the literary community: gothic novels, philosophy books, and pulp science fiction, hereafter sci-fi.", + "We retrieve gothic novels and philosophy books from Project Gutenberg and pulp sci-fi from Internet Archive's Pulp Magazine Archive. We partition this corpus into train, validation, and test sets the sizes of which can be found in Table TABREF12.", + "In order to validate the above claims, we train five different classifiers to predict the literary style of sentences from our corpus. Each classifier has gradually more content words replaced with part-of-speech (POS) tag placeholder tokens. The All model is trained on sentences with all proper nouns replaced by `PROPN'. The models Ablated N, Ablated NV, and Ablated NVA replace nouns, nouns & verbs, and nouns, verbs, & adjectives with the corresponding POS tag respectively. Finally, Content-only is trained on sentences with all words that are not tagged as NOUN, VERB, ADJ removed; the remaining words are not ablated.", + "We train the classifiers on the training set, balancing the class distribution to make sure there are the same number of sentences from each style. Classifiers are trained using fastText BIBREF19, using tri-gram features with all other settings as default. table:classifiers shows the accuracies of the classifiers.", + "The styles are highly distinctive: the All classifier has an accuracy of 86%. Additionally, even the Ablated NVA is quite successful, with 75% accuracy, even without access to any content words. The Content only classifier is also quite successful, at 80% accuracy. This indicates that these stylistic genres are distinctive at both the content level and at the syntactic level." + ], + [ + "Given that non-content words are distinctive enough for a classifier to determine style, we propose a suite of low-level linguistic feature counts (henceforth, controls) as our formal, content-blind definition of style. The style of a sentence is represented as a vector of counts of closed word classes (like personal pronouns) as well as counts of syntactic features like the number of SBAR non-terminals in its constituency parse, since clause structure has been shown to be indicative of style BIBREF20. Controls are extracted heuristically, and almost all rely on counts of pre-defined word lists. For constituency parses we use the Stanford Parser BIBREF21. table:controlexamples lists all the controls along with examples." + ], + [ + "Models are trained with a reconstruction task, in which a distorted version of a reference sentence is input and the goal is to output the original reference.", + "fig:sentenceinput illustrates the process. Controls are calculated heuristically. All words found in the control word lists are then removed from the reference sentence. The remaining words, which represent the content, are used as input into the model, along with their POS tags and lemmas.", + "In this way we encourage models to construct a sentence using content and style independently. This will allow us to vary the stylistic controls while keeping the content constant, and successfully perform style transfer. When generating a new sentence, the controls correspond to the counts of the corresponding syntactic features that we expect to be realized in the output." + ], + [ + "We implement our feature controlled language model using a neural encoder-decoder with attention BIBREF22, using 2-layer uni-directional gated recurrent units (GRUs) for the encoder and decoder BIBREF23.", + "The input to the encoder is a sequence of $M$ content words, along with their lemmas, and fine and coarse grained part-of-speech (POS) tags, i.e. $X_{.,j} = (x_{1,j},\\ldots ,x_{M,j})$ for $j \\in \\mathcal {T} = \\lbrace \\textrm {word, lemma, fine-pos, coarse-pos}\\rbrace $. We embed each token (and its lemma and POS) before concatenating, and feeding into the encoder GRU to obtain encoder hidden states, $ c_i = \\operatorname{gru}(c_{i-1}, \\left[E_j(X_{i,j}), \\; j\\in \\mathcal {T} \\right]; \\omega _{enc}) $ for $i \\in {1,\\ldots ,M},$ where initial state $c_0$, encoder GRU parameters $\\omega _{enc}$ and embedding matrices $E_j$ are learned parameters.", + "The decoder sequentially generates the outputs, i.e. a sequence of $N$ tokens $y =(y_1,\\ldots ,y_N)$, where all tokens $y_i$ are drawn from a finite output vocabulary $\\mathcal {V}$. To generate the each token we first embed the previously generated token $y_{i-1}$ and a vector of $K$ control features $z = ( z_1,\\ldots , z_K)$ (using embedding matrices $E_{dec}$ and $E_{\\textrm {ctrl-1}}, \\ldots , E_{\\textrm {ctrl-K}}$ respectively), before concatenating them into a vector $\\rho _i,$ and feeding them into the decoder side GRU along with the previous decoder state $h_{i-1}$:", + "where $\\omega _{dec}$ are the decoder side GRU parameters.", + "Using the decoder hidden state $h_i$ we then attend to the encoder context vectors $c_j$, computing attention scores $\\alpha _{i,j}$, where", + "before passing $h_i$ and the attention weighted context $\\bar{c}_i=\\sum _{j=1}^M \\alpha _{i,j} c_j$ into a single hidden-layer perceptron with softmax output to compute the next token prediction probability,", + "where $W,U,V$ and $u,v, \\nu $ are parameter matrices and vectors respectively.", + "Crucially, the controls $z$ remain fixed for all input decoder steps. Each $z_k$ represents the frequency of one of the low-level features described in sec:formalstyle. During training on the reconstruction task, we can observe the full output sequence $y,$ and so we can obtain counts for each control feature directly. Controls receive a different embedding depending on their frequency, where counts of 0-20 each get a unique embedding, and counts greater than 20 are assigned to the same embedding. At test time, we set the values of the controls according to procedure described in Section SECREF25.", + "We use embedding sizes of 128, 128, 64, and 32 for token, lemma, fine, and coarse grained POS embedding matrices respectively. Output token embeddings $E_{dec}$ have size 512, and 50 for the control feature embeddings. We set 512 for all GRU and perceptron output sizes. We refer to this model as the StyleEQ model. See fig:model for a visual depiction of the model." + ], + [ + "We compare the above model to a similar model, where rather than explicitly represent $K$ features as input, we have $K$ features in the form of a genre embedding, i.e. we learn a genre specific embedding for each of the gothic, scifi, and philosophy genres, as studied in BIBREF8 and BIBREF7. To generate in a specific style, we simply set the appropriate embedding. We use genre embeddings of size 850 which is equivalent to the total size of the $K$ feature embeddings in the StyleEQ model." + ], + [ + "We train both models with minibatch stochastic gradient descent with a learning rate of 0.25, weight decay penalty of 0.0001, and batch size of 64. We also apply dropout with a drop rate of 0.25 to all embedding layers, the GRUs, and preceptron hidden layer. We train for a maximum of 200 epochs, using validation set BLEU score BIBREF26 to select the final model iteration for evaluation." + ], + [ + "In the Baseline model, style transfer is straightforward: given an input sentence in one style, fix the encoder content features while selecting a different genre embedding. In contrast, the StyleEQ model requires selecting the counts for each control. Although there are a variety of ways to do this, we use a method that encourages a diversity of outputs.", + "In order to ensure the controls match the reference sentence in magnitude, we first find all sentences in the target style with the same number of words as the reference sentence. Then, we add the following constraints: the same number of proper nouns, the same number of nouns, the same number of verbs, and the same number of adjectives. We randomly sample $n$ of the remaining sentences, and for each of these `sibling' sentences, we compute the controls. For each of the new controls, we generate a sentence using the original input sentence content features. The generated sentences are then reranked using the length normalized log-likelihood under the model. We can then select the highest scoring sentence as our style-transferred output, or take the top-$k$ when we need a diverse set of outputs.", + "The reason for this process is that although there are group-level distinctive controls for each style, e.g. the high use of punctuation in philosophy books or of first person pronouns in gothic novels, at the sentence level it can understandably be quite varied. This method matches sentences between styles, capturing the natural distribution of the corpora." + ], + [ + "In tab:blueperpl we report BLEU scores for the reconstruction of test set sentences from their content and feature representations, as well as the model perplexities of the reconstruction. For both models, we use beam decoding with a beam size of eight. Beam candidates are ranked according to their length normalized log-likelihood. On these automatic measures we see that StyleEQ is better able to reconstruct the original sentences. In some sense this evaluation is mostly a sanity check, as the feature controls contain more locally specific information than the genre embeddings, which say very little about how many specific function words one should expect to see in the output." + ], + [ + "Designing controllable language models is often difficult because of the various dependencies between tokens; when changing one control value it may effect other aspects of the surface realization. For example, increasing the number of conjunctions may effect how the generator places prepositions to compensate for structural changes in the sentence. Since our features are deterministically recoverable, we can perturb an individual control value and check to see that the desired change was realized in the output. Moreover, we can check the amount of change in the other non-perturbed features to measure the independence of the controls.", + "We sample 50 sentences from each genre from the test set. For each sample, we create a perturbed control setting for each control by adding $\\delta $ to the original control value. This is done for $\\delta \\in \\lbrace -3, -2, -1, 0, 1, 2, 3\\rbrace $, skipping any settings where the new control value would be negative.", + "table:autoeval:ctrl shows the results of this experiment. The Exact column displays the percentage of generated texts that realize the exact number of control features specified by the perturbed control. High percentages in the Exact column indicate greater one-to-one correspondence between the control and surface realization. For example, if the input was \u201cDracula and Frankenstein and the mummy,\u201d and we change the conjunction feature by $\\delta =-1$, an output of \u201cDracula, Frankenstein and the mummy,\u201d would count towards the Exact category, while \u201cDracula, Frankenstein, the mummy,\u201d would not.", + "The Direction column specifies the percentage of cases where the generated text produces a changed number of the control features that, while not exactly matching the specified value of the perturbed control, does change from the original in the correct direction. For example, if the input again was \u201cDracula and Frankenstein and the mummy,\u201d and we change the conjunction feature by $\\delta =-1$, both outputs of \u201cDracula, Frankenstein and the mummy,\u201d and \u201cDracula, Frankenstein, the mummy,\u201d would count towards Direction. High percentages in Direction mean that we could roughly ensure desired surface realizations by modifying the control by a larger $\\delta $.", + "Finally, the Atomic column specifies the percentage of cases where the generated text with the perturbed control only realizes changes to that specific control, while other features remain constant. For example, if the input was \u201cDracula and Frankenstein in the castle,\u201d and we set the conjunction feature to $\\delta =-1$, an output of \u201cDracula near Frankenstein in the castle,\u201d would not count as Atomic because, while the number of conjunctions did decrease by one, the number of simple preposition changed. An output of \u201cDracula, Frankenstein in the castle,\u201d would count as Atomic. High percentages in the Atomic column indicate this feature is only loosely coupled to the other features and can be changed without modifying other aspects of the sentence.", + "Controls such as conjunction, determiner, and punctuation are highly controllable, with Exact rates above 80%. But with the exception of the constituency parse features, all controls have high Direction rates, many in the 90s. These results indicate our model successfully controls these features. The fact that the Atomic rates are relatively low is to be expected, as controls are highly coupled \u2013 e.g. to increase 1stPer, it is likely another pronoun control will have to decrease." + ], + [ + "For each model we look at the classifier prediction accuracy of reconstructed and transferred sentences. In particular we use the Ablated NVA classifier, as this is the most content-blind one.", + "We produce 16 outputs from both the Baseline and StyleEq models. For the Baseline, we use a beam search of size 16. For the StyleEQ model, we use the method described in Section SECREF25 to select 16 `sibling' sentences in the target style, and generated a transferred sentence for each. We look at three different methods for selection: all, which uses all output sentences; top, which selects the top ranked sentence based on the score from the model; and oracle, which selects the sentence with the highest classifier likelihood for the intended style.", + "The reason for the third method, which indeed acts as an oracle, is that using the score from the model didn't always surface a transferred sentence that best reflected the desired style. Partially this was because the model score was mostly a function of how well a transferred sentence reflected the distribution of the training data. But additionally, some control settings are more indicative of a target style than others. The use of the classifier allows us to identify the most suitable control setting for a target style that was roughly compatible with the number of content words.", + "In table:fasttext-results we see the results. Note that for both models, the all and top classification accuracy tends to be quite similar, though for the Baseline they are often almost exactly the same when the Baseline has little to no diversity in the outputs.", + "However, the oracle introduces a huge jump in accuracy for the StyleEQ model, especially compared to the Baseline, partially because the diversity of outputs from StyleEQ is much higher; often the Baseline model produces no diversity \u2013 the 16 output sentences may be nearly identical, save a single word or two. It's important to note that neither model uses the classifier in any way except to select the sentence from 16 candidate outputs.", + "What this implies is that lurking within the StyleEQ model outputs are great sentences, even if they are hard to find. In many cases, the StyleEQ model has a classification accuracy above the base rate from the test data, which is 75% (see table:classifiers)." + ], + [ + "table:cherrypicking shows example outputs for the StyleEQ and Baseline models. Through inspection we see that the StyleEQ model successfully changes syntactic constructions in stylistically distinctive ways, such as increasing syntactic complexity when transferring to philosophy, or changing relevant pronouns when transferring to sci-fi. In contrast, the Baseline model doesn't create outputs that move far from the reference sentence, making only minor modifications such changing the type of a single pronoun.", + "To determine how readers would classify our transferred sentences, we recruited three English Literature PhD candidates, all of whom had passed qualifying exams that included determining both genre and era of various literary texts." + ], + [ + "To evaluate the fluency of our outputs, we had the annotators score reference sentences, reconstructed sentences, and transferred sentences on a 0-5 scale, where 0 was incoherent and 5 was a well-written human sentence.", + "table:fluency shows the average fluency of various conditions from all three annotators. Both models have fluency scores around 3. Upon inspection of the outputs, it is clear that many have fluency errors, resulting in ungrammatical sentences.", + "Notably the Baseline often has slightly higher fluency scores than the StyleEQ model. This is likely because the Baseline model is far less constrained in how to construct the output sentence, and upon inspection often reconstructs the reference sentence even when performing style transfer. In contrast, the StyleEQ is encouraged to follow the controls, but can struggle to incorporate these controls into a fluent sentence.", + "The fluency of all outputs is lower than desired. We expect that incorporating pre-trained language models would increase the fluency of all outputs without requiring larger datasets." + ], + [ + "Each annotator annotated 90 reference sentences (i.e. from the training corpus) with which style they thought the sentence was from. The accuracy on this baseline task for annotators A1, A2, and A3 was 80%, 88%, and 80% respectively, giving us an upper expected bound on the human evaluation.", + "In discussing this task with the annotators, they noted that content is a heavy predictor of genre, and that would certainly confound their annotations. To attempt to mitigate this, we gave them two annotation tasks: which-of-3 where they simply marked which style they thought a sentence was from, and which-of-2 where they were given the original style and marked which style they thought the sentence was transferred into.", + "For each task, each annotator marked 180 sentences: 90 from each model, with an even split across the three genres. Annotators were presented the sentences in a random order, without information about the models. In total, each marked 270 sentences. (Note there were no reconstructions in this annotation task.)", + "table:humanclassifiers shows the results. In both tasks, accuracy of annotators classifying the sentence as its intended style was low. In which-of-3, scores were around 20%, below the chance rate of 33%. In which-of-2, scores were in the 50s, slightly above the chance rate of 50%. This was the case for both models. There was a slight increase in accuracy for the StyleEQ model over the Baseline for which-of-3, but the opposite trend for which-of-2, suggesting these differences are not significant.", + "It's clear that it's hard to fool the annotators. Introspecting on their approach, the annotators expressed having immediate responses based on key words \u2013 for instance any references of `space' implied `sci-fi'. We call this the `vampires in space' problem, because no matter how well a gothic sentence is rewritten as a sci-fi one, it's impossible to ignore the fact that there is a vampire in space. The transferred sentences, in the eyes of the Ablated NVA classifier (with no access to content words), did quite well transferring into their intended style. But people are not blind to content." + ], + [ + "Working with the annotators, we regularly came up against the 'vampires in space' problem: while syntactic constructions account for much of the distinction of literary styles, these constructions often co-occur with distinctive content.", + "Stylometrics finds syntactic constructions are great at fingerprinting, but suggests that these constructions are surface realizations of higher-level stylistic decisions. The number and type of personal pronouns is a reflection of how characters feature in a text. A large number of positional prepositions may be the result of a writer focusing on physical descriptions of scenes. In our attempt to decouple these, we create Frankenstein sentences, which piece together features of different styles \u2013 we are putting vampires in space.", + "Another way to validate our approach would be to select data that is stylistically distinctive but with similar content: perhaps genres in which content is static but language use changes over time, stylistically distinct authors within a single genre, or parodies of a distinctive genre." + ], + [ + "We present a formal, extendable model of style that can add control to any neural text generation system. We model style as a suite of low-level linguistic controls, and train a neural encoder-decoder model to reconstruct reference sentences given only content words and the setting of the controls. In automatic evaluations, we show that our model can fool a style classifier 84% of the time and outperforms a baseline genre-embedding model. In human evaluations, we encounter the `vampires in space' problem in which content and style are equally discriminative but people focus more on the content.", + "In future work we would like to model higher-level syntactic controls. BIBREF20 show that differences in clausal constructions, for instance having a dependent clause before an independent clause or vice versa, is a marker of style appreciated by the reader. Such features would likely interact with our lower-level controls in an interesting way, and provide further insight into style transfer in text." + ], + [ + "Katy Gero is supported by an NSF GRF (DGE - 1644869). We would also like to thank Elsbeth Turcan for her helpful comments." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0446/instruction.md b/qasper-0446/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..e2b5a2ef8041f63f558a35022b9524df47c4c9f2 --- /dev/null +++ b/qasper-0446/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Fusing Visual, Textual and Connectivity Clues for Studying Mental Health + +Question: How do this framework facilitate demographic inference from social media? \ No newline at end of file diff --git a/qasper-0448/instruction.md b/qasper-0448/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..7f82ecf1f50e38c88aed937df171a3b8512517a3 --- /dev/null +++ b/qasper-0448/instruction.md @@ -0,0 +1,105 @@ +Name of Paper: Fusing Visual, Textual and Connectivity Clues for Studying Mental Health + +Question: How is the data annotated? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + null, + "Introduction", + "Related Work", + "Dataset", + "Data Modality Analysis", + "Demographic Prediction", + "Multi-modal Prediction Framework" + ], + "paragraphs": [ + [ + "0pt*0*0", + "0pt*0*0", + "0pt*0*0 0.95", + "1]Amir Hossein Yazdavar 1]Mohammad Saeid Mahdavinejad 2]Goonmeet Bajaj", + " 3]William Romine 1]Amirhassan Monadjemi 1]Krishnaprasad Thirunarayan", + " 1]Amit Sheth 4]Jyotishman Pathak [1]Department of Computer Science & Engineering, Wright State University, OH, USA [2]Ohio State University, Columbus, OH, USA [3]Department of Biological Science, Wright State University, OH, USA [4] Division of Health Informatics, Weill Cornell University, New York, NY, USA", + "[1] yazdavar.2@wright.edu", + "With ubiquity of social media platforms, millions of people are sharing their online persona by expressing their thoughts, moods, emotions, feelings, and even their daily struggles with mental health issues voluntarily and publicly on social media. Unlike the most existing efforts which study depression by analyzing textual content, we examine and exploit multimodal big data to discern depressive behavior using a wide variety of features including individual-level demographics. By developing a multimodal framework and employing statistical techniques for fusing heterogeneous sets of features obtained by processing visual, textual and user interaction data, we significantly enhance the current state-of-the-art approaches for identifying depressed individuals on Twitter (improving the average F1-Score by 5 percent) as well as facilitate demographic inference from social media for broader applications. Besides providing insights into the relationship between demographics and mental health, our research assists in the design of a new breed of demographic-aware health interventions." + ], + [ + "Depression is a highly prevalent public health challenge and a major cause of disability worldwide. Depression affects 6.7% (i.e., about 16 million) Americans each year . According to the World Mental Health Survey conducted in 17 countries, on average, about 5% of people reported having an episode of depression in 2011 BIBREF0 . Untreated or under-treated clinical depression can lead to suicide and other chronic risky behaviors such as drug or alcohol addiction.", + "Global efforts to curb clinical depression involve identifying depression through survey-based methods employing phone or online questionnaires. These approaches suffer from under-representation as well as sampling bias (with very small group of respondents.) In contrast, the widespread adoption of social media where people voluntarily and publicly express their thoughts, moods, emotions, and feelings, and even share their daily struggles with mental health problems has not been adequately tapped into studying mental illnesses, such as depression. The visual and textual content shared on different social media platforms like Twitter offer new opportunities for a deeper understanding of self-expressed depression both at an individual as well as community-level. Previous research efforts have suggested that language style, sentiment, users' activities, and engagement expressed in social media posts can predict the likelihood of depression BIBREF1 , BIBREF2 . However, except for a few attempts BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , these investigations have seldom studied extraction of emotional state from visual content of images in posted/profile images. Visual content can express users' emotions more vividly, and psychologists noted that imagery is an effective medium for communicating difficult emotions.", + "According to eMarketer, photos accounted for 75% of content posted on Facebook worldwide and they are the most engaging type of content on Facebook (87%). Indeed, \"a picture is worth a thousand words\" and now \"photos are worth a million likes.\" Similarly, on Twitter, the tweets with image links get twice as much attention as those without , and video-linked tweets drive up engagement . The ease and naturalness of expression through visual imagery can serve to glean depression-indicators in vulnerable individuals who often seek social support through social media BIBREF7 . Further, as psychologist Carl Rogers highlights, we often pursue and promote our Ideal-Self . In this regard, the choice of profile image can be a proxy for the online persona BIBREF8 , providing a window into an individual's mental health status. For instance, choosing emaciated legs of girls covered with several cuts as profile image portrays negative self-view BIBREF9 .", + "Inferring demographic information like gender and age can be crucial for stratifying our understanding of population-level epidemiology of mental health disorders. Relying on electronic health records data, previous studies explored gender differences in depressive behavior from different angles including prevalence, age at onset, comorbidities, as well as biological and psychosocial factors. For instance, women have been diagnosed with depression twice as often as men BIBREF10 and national psychiatric morbidity survey in Britain has shown higher risk of depression in women BIBREF11 . On the other hand, suicide rates for men are three to five times higher compared to that of the women BIBREF12 .", + "Although depression can affect anyone at any age, signs and triggers of depression vary for different age groups . Depression triggers for children include parental depression, domestic violence, and loss of a pet, friend or family member. For teenagers (ages 12-18), depression may arise from hormonal imbalance, sexuality concerns and rejection by peers. Young adults (ages 19-29) may develop depression due to life transitions, poverty, trauma, and work issues. Adult (ages 30-60) depression triggers include caring simultaneously for children and aging parents, financial burden, work and relationship issues. Senior adults develop depression from common late-life issues, social isolation, major life loses such as the death of a spouse, financial stress and other chronic health problems (e.g., cardiac disease, dementia). Therefore, inferring demographic information while studying depressive behavior from passively sensed social data, can shed better light on the population-level epidemiology of depression.", + "The recent advancements in deep neural networks, specifically for image analysis task, can lead to determining demographic features such as age and gender BIBREF13 . We show that by determining and integrating heterogeneous set of features from different modalities \u2013 aesthetic features from posted images (colorfulness, hue variance, sharpness, brightness, blurriness, naturalness), choice of profile picture (for gender, age, and facial expression), the screen name, the language features from both textual content and profile's description (n-gram, emotion, sentiment), and finally sociability from ego-network, and user engagement \u2013 we can reliably detect likely depressed individuals in a data set of 8,770 human-annotated Twitter users.", + "We address and derive answers to the following research questions: 1) How well do the content of posted images (colors, aesthetic and facial presentation) reflect depressive behavior? 2) Does the choice of profile picture show any psychological traits of depressed online persona? Are they reliable enough to represent the demographic information such as age and gender? 3) Are there any underlying common themes among depressed individuals generated using multimodal content that can be used to detect depression reliably?" + ], + [ + "Mental Health Analysis using Social Media:", + "Several efforts have attempted to automatically detect depression from social media content utilizing machine/deep learning and natural language processing approaches. Conducting a retrospective study over tweets, BIBREF14 characterizes depression based on factors such as language, emotion, style, ego-network, and user engagement. They built a classifier to predict the likelihood of depression in a post BIBREF14 , BIBREF15 or in an individual BIBREF1 , BIBREF16 , BIBREF17 , BIBREF18 . Moreover, there have been significant advances due to the shared task BIBREF19 focusing on methods for identifying depressed users on Twitter at the Computational Linguistics and Clinical Psychology Workshop (CLP 2015). A corpus of nearly 1,800 Twitter users was built for evaluation, and the best models employed topic modeling BIBREF20 , Linguistic Inquiry and Word Count (LIWC) features, and other metadata BIBREF21 . More recently, a neural network architecture introduced by BIBREF22 combined posts into a representation of user's activities for detecting depressed users. Another active line of research has focused on capturing suicide and self-harm signals BIBREF23 , BIBREF24 , BIBREF25 , BIBREF26 , BIBREF2 , BIBREF27 . Moreover, the CLP 2016 BIBREF28 defined a shared task on detecting the severity of the mental health from forum posts. All of these studies derive discriminative features to classify depression in user-generated content at message-level, individual-level or community-level. Recent emergence of photo-sharing platforms such as Instagram, has attracted researchers attention to study people's behavior from their visual narratives \u2013 ranging from mining their emotions BIBREF29 , and happiness trend BIBREF30 , to studying medical concerns BIBREF31 . Researchers show that people use Instagram to engage in social exchange and storytelling about their difficult experiences BIBREF4 . The role of visual imagery as a mechanism of self-disclosure by relating visual attributes to mental health disclosures on Instagram was highlighted by BIBREF3 , BIBREF5 where individual Instagram profiles were utilized to build a prediction framework for identifying markers of depression. The importance of data modality to understand user behavior on social media was highlighted by BIBREF32 . More recently, a deep neural network sequence modeling approach that marries audio and text data modalities to analyze question-answer style interviews between an individual and an agent has been developed to study mental health BIBREF32 . Similarly, a multimodal depressive dictionary learning was proposed to detect depressed users on Twitter BIBREF33 . They provide a sparse user representations by defining a feature set consisting of social network features, user profile features, visual features, emotional features BIBREF34 , topic-level features, and domain-specific features. Particularly, our choice of multi-model prediction framework is intended to improve upon the prior works involving use of images in multimodal depression analysis BIBREF33 and prior works on studying Instagram photos BIBREF6 , BIBREF35 .", + "Demographic information inference on Social Media: ", + "There is a growing interest in understanding online user's demographic information due to its numerous applications in healthcare BIBREF36 , BIBREF37 . A supervised model developed by BIBREF38 for determining users' gender by employing features such as screen-name, full-name, profile description and content on external resources (e.g., personal blog). Employing features including emoticons, acronyms, slangs, punctuations, capitalization, sentence length and included links/images, along with online behaviors such as number of friends, post time, and commenting activity, a supervised model was built for predicting user's age group BIBREF39 . Utilizing users life stage information such as secondary school student, college student, and employee, BIBREF40 builds age inference model for Dutch Twitter users. Similarly, relying on profile descriptions while devising a set of rules and patterns, a novel model introduced for extracting age for Twitter users BIBREF41 . They also parse description for occupation by consulting the SOC2010 list of occupations and validating it through social surveys. A novel age inference model was developed while relying on homophily interaction information and content for predicting age of Twitter users BIBREF42 . The limitations of textual content for predicting age and gender was highlighted by BIBREF43 . They distinguish language use based on social gender, age identity, biological sex and chronological age by collecting crowdsourced signals using a game in which players (crowd) guess the biological sex and age of a user based only on their tweets. Their findings indicate how linguistic markers can misguide (e.g., a heart represented as <3 can be misinterpreted as feminine when the writer is male.) Estimating age and gender from facial images by training a convolutional neural networks (CNN) for face recognition is an active line of research BIBREF44 , BIBREF13 , BIBREF45 ." + ], + [ + "Self-disclosure clues have been extensively utilized for creating ground-truth data for numerous social media analytic studies e.g., for predicting demographics BIBREF36 , BIBREF41 , and user's depressive behavior BIBREF46 , BIBREF47 , BIBREF48 . For instance, vulnerable individuals may employ depressive-indicative terms in their Twitter profile descriptions. Others may share their age and gender, e.g., \"16 years old suicidal girl\"(see Figure FIGREF15 ). We employ a huge dataset of 45,000 self-reported depressed users introduced in BIBREF46 where a lexicon of depression symptoms consisting of 1500 depression-indicative terms was created with the help of psychologist clinician and employed for collecting self-declared depressed individual's profiles. A subset of 8,770 users (24 million time-stamped tweets) containing 3981 depressed and 4789 control users (that do not show any depressive behavior) were verified by two human judges BIBREF46 . This dataset INLINEFORM0 contains the metadata values of each user such as profile descriptions, followers_count, created_at, and profile_image_url.", + "Age Enabled Ground-truth Dataset: We extract user's age by applying regular expression patterns to profile descriptions (such as \"17 years old, self-harm, anxiety, depression\") BIBREF41 . We compile \"age prefixes\" and \"age suffixes\", and use three age-extraction rules: 1. I am X years old 2. Born in X 3. X years old, where X is a \"date\" or age (e.g., 1994). We selected a subset of 1061 users among INLINEFORM0 as gold standard dataset INLINEFORM1 who disclose their age. From these 1061 users, 822 belong to depressed class and 239 belong to control class. From 3981 depressed users, 20.6% disclose their age in contrast with only 4% (239/4789) among control group. So self-disclosure of age is more prevalent among vulnerable users. Figure FIGREF18 depicts the age distribution in INLINEFORM2 . The general trend, consistent with the results in BIBREF42 , BIBREF49 , is biased toward young people. Indeed, according to Pew, 47% of Twitter users are younger than 30 years old BIBREF50 . Similar data collection procedure with comparable distribution have been used in many prior efforts BIBREF51 , BIBREF49 , BIBREF42 . We discuss our approach to mitigate the impact of the bias in Section 4.1. The median age is 17 for depressed class versus 19 for control class suggesting either likely depressed-user population is younger, or depressed youngsters are more likely to disclose their age for connecting to their peers (social homophily.) BIBREF51 ", + "Gender Enabled Ground-truth Dataset: We selected a subset of 1464 users INLINEFORM0 from INLINEFORM1 who disclose their gender in their profile description. From 1464 users 64% belonged to the depressed group, and the rest (36%) to the control group. 23% of the likely depressed users disclose their gender which is considerably higher (12%) than that for the control class. Once again, gender disclosure varies among the two gender groups. For statistical significance, we performed chi-square test (null hypothesis: gender and depression are two independent variables). Figure FIGREF19 illustrates gender association with each of the two classes. Blue circles (positive residuals, see Figure FIGREF19 -A,D) show positive association among corresponding row and column variables while red circles (negative residuals, see Figure FIGREF19 -B,C) imply a repulsion. Our findings are consistent with the medical literature BIBREF10 as according to BIBREF52 more women than men were given a diagnosis of depression. In particular, the female-to-male ratio is 2.1 and 1.9 for Major Depressive Disorder and Dysthymic Disorder respectively. Our findings from Twitter data indicate there is a strong association (Chi-square: 32.75, p-value:1.04e-08) between being female and showing depressive behavior on Twitter." + ], + [ + "We now provide an in-depth analysis of visual and textual content of vulnerable users.", + "Visual Content Analysis: We show that the visual content in images from posts as well as profiles provide valuable psychological cues for understanding a user's depression status. Profile/posted images can surface self-stigmatization BIBREF53 . Additionally, as opposed to typical computer vision framework for object recognition that often relies on thousands of predetermined low-level features, what matters more for assessing user's online behavior is the emotions reflected in facial expressions BIBREF54 , attributes contributing to the computational aesthetics BIBREF55 , and sentimental quotes they may subscribe to (Figure FIGREF15 ) BIBREF8 .", + "Facial Presence: ", + "For capturing facial presence, we rely on BIBREF56 's approach that uses multilevel convolutional coarse-to-fine network cascade to tackle facial landmark localization. We identify facial presentation, emotion from facial expression, and demographic features from profile/posted images . Table TABREF21 illustrates facial presentation differences in both profile and posted images (media) for depressed and control users in INLINEFORM0 . With control class showing significantly higher in both profile and media (8%, 9% respectively) compared to that for the depressed class. In contrast with age and gender disclosure, vulnerable users are less likely to disclose their facial identity, possibly due to lack of confidence or fear of stigma.", + "Facial Expression:", + "Following BIBREF8 's approach, we adopt Ekman's model of six emotions: anger, disgust, fear, joy, sadness and surprise, and use the Face++ API to automatically capture them from the shared images. Positive emotions are joy and surprise, and negative emotions are anger, disgust, fear, and sadness. In general, for each user u in INLINEFORM0 , we process profile/shared images for both the depressed and the control groups with at least one face from the shared images (Table TABREF23 ). For the photos that contain multiple faces, we measure the average emotion.", + "Figure FIGREF27 illustrates the inter-correlation of these features. Additionally, we observe that emotions gleaned from facial expressions correlated with emotional signals captured from textual content utilizing LIWC. This indicates visual imagery can be harnessed as a complementary channel for measuring online emotional signals.", + "General Image Features:", + "The importance of interpretable computational aesthetic features for studying users' online behavior has been highlighted by several efforts BIBREF55 , BIBREF8 , BIBREF57 . Color, as a pillar of the human vision system, has a strong association with conceptual ideas like emotion BIBREF58 , BIBREF59 . We measured the normalized red, green, blue and the mean of original colors, and brightness and contrast relative to variations of luminance. We represent images in Hue-Saturation-Value color space that seems intuitive for humans, and measure mean and variance for saturation and hue. Saturation is defined as the difference in the intensities of the different light wavelengths that compose the color. Although hue is not interpretable, high saturation indicates vividness and chromatic purity which are more appealing to the human eye BIBREF8 . Colorfulness is measured as a difference against gray background BIBREF60 . Naturalness is a measure of the degree of correspondence between images and the human perception of reality BIBREF60 . In color reproduction, naturalness is measured from the mental recollection of the colors of familiar objects. Additionally, there is a tendency among vulnerable users to share sentimental quotes bearing negative emotions. We performed optical character recognition (OCR) with python-tesseract to extract text and their sentiment score. As illustrated in Table TABREF26 , vulnerable users tend to use less colorful (higher grayscale) profile as well as shared images to convey their negative feelings, and share images that are less natural (Figure FIGREF15 ). With respect to the aesthetic quality of images (saturation, brightness, and hue), depressed users use images that are less appealing to the human eye. We employ independent t-test, while adopting Bonferroni Correction as a conservative approach to adjust the confidence intervals. Overall, we have 223 features, and choose Bonferroni-corrected INLINEFORM0 level of INLINEFORM1 (*** INLINEFORM2 , ** INLINEFORM3 ).", + "** alpha= 0.05, *** alpha = 0.05/223", + "Demographics Inference & Language Cues: LIWC has been used extensively for examining the latent dimensions of self-expression for analyzing personality BIBREF61 , depressive behavior, demographic differences BIBREF43 , BIBREF40 , etc. Several studies highlight that females employ more first-person singular pronouns BIBREF62 , and deictic language BIBREF63 , while males tend to use more articles BIBREF64 which characterizes concrete thinking, and formal, informational and affirmation words BIBREF65 . For age analysis, the salient findings include older individuals using more future tense verbs BIBREF62 triggering a shift in focus while aging. They also show positive emotions BIBREF66 and employ fewer self-references (i.e. 'I', 'me') with greater first person plural BIBREF62 . Depressed users employ first person pronouns more frequently BIBREF67 , repeatedly use negative emotions and anger words. We analyzed psycholinguistic cues and language style to study the association between depressive behavior as well as demographics. Particularly, we adopt Levinson's adult development grouping that partitions users in INLINEFORM0 into 5 age groups: (14,19],(19,23], (23,34],(34,46], and (46,60]. Then, we apply LIWC for characterizing linguistic styles for each age group for users in INLINEFORM1 .", + "Qualitative Language Analysis: The recent LIWC version summarizes textual content in terms of language variables such as analytical thinking, clout, authenticity, and emotional tone. It also measures other linguistic dimensions such as descriptors categories (e.g., percent of target words gleaned by dictionary, or longer than six letters - Sixltr) and informal language markers (e.g., swear words, netspeak), and other linguistic aspects (e.g., 1st person singular pronouns.)", + "Thinking Style:", + "Measuring people's natural ways of trying to analyze, and organize complex events have strong association with analytical thinking. LIWC relates higher analytic thinking to more formal and logical reasoning whereas a lower value indicates focus on narratives. Also, cognitive processing measures problem solving in mind. Words such as \"think,\" \"realize,\" and \"know\" indicates the degree of \"certainty\" in communications. Critical thinking ability relates to education BIBREF68 , and is impacted by different stages of cognitive development at different ages . It has been shown that older people communicate with greater cognitive complexity while comprehending nuances and subtle differences BIBREF62 . We observe a similar pattern in our data (Table TABREF40 .) A recent study highlights how depression affects brain and thinking at molecular level using a rat model BIBREF69 . Depression can promote cognitive dysfunction including difficulty in concentrating and making decisions. We observed a notable differences in the ability to think analytically in depressed and control users in different age groups (see Figure FIGREF39 - A, F and Table TABREF40 ). Overall, vulnerable younger users are not logical thinkers based on their relative analytical score and cognitive processing ability.", + "Authenticity:", + "Authenticity measures the degree of honesty. Authenticity is often assessed by measuring present tense verbs, 1st person singular pronouns (I, me, my), and by examining the linguistic manifestations of false stories BIBREF70 . Liars use fewer self-references and fewer complex words. Psychologists often see a child's first successfull lie as a mental growth. There is a decreasing trend of the Authenticity with aging (see Figure FIGREF39 -B.) Authenticity for depressed youngsters is strikingly higher than their control peers. It decreases with age (Figure FIGREF39 -B.)", + "Clout:", + "People with high clout speak more confidently and with certainty, employing more social words with fewer negations (e.g., no, not) and swear words. In general, midlife is relatively stable w.r.t. relationships and work. A recent study shows that age 60 to be best for self-esteem BIBREF71 as people take on managerial roles at work and maintain a satisfying relationship with their spouse. We see the same pattern in our data (see Figure FIGREF39 -C and Table TABREF40 ). Unsurprisingly, lack of confidence (the 6th PHQ-9 symptom) is a distinguishable characteristic of vulnerable users, leading to their lower clout scores, especially among depressed users before middle age (34 years old).", + "Self-references:", + "First person singular words are often seen as indicating interpersonal involvement and their high usage is associated with negative affective states implying nervousness and depression BIBREF66 . Consistent with prior studies, frequency of first person singular for depressed people is significantly higher compared to that of control class. Similarly to BIBREF66 , youngsters tend to use more first-person (e.g. I) and second person singular (e.g. you) pronouns (Figure FIGREF39 -G).", + "Informal Language Markers; Swear, Netspeak:", + "Several studies highlighted the use of profanity by young adults has significantly increased over the last decade BIBREF72 . We observed the same pattern in both the depressed and the control classes (Table TABREF40 ), although it's rate is higher for depressed users BIBREF1 . Psychologists have also shown that swearing can indicate that an individual is not a fragmented member of a society. Depressed youngsters, showing higher rate of interpersonal involvement and relationships, have a higher rate of cursing (Figure FIGREF39 -E). Also, Netspeak lexicon measures the frequency of terms such as lol and thx.", + "Sexual, Body: ", + "Sexual lexicon contains terms like \"horny\", \"love\" and \"incest\", and body terms like \"ache\", \"heart\", and \"cough\". Both start with a higher rate for depressed users while decreasing gradually while growing up, possibly due to changes in sexual desire as we age (Figure FIGREF39 -H,I and Table TABREF40 .)", + "Quantitative Language Analysis:", + "We employ one-way ANOVA to compare the impact of various factors and validate our findings above. Table TABREF40 illustrates our findings, with a degree of freedom (df) of 1055. The null hypothesis is that the sample means' for each age group are similar for each of the LIWC features.", + "*** alpha = 0.001, ** alpha = 0.01, * alpha = 0.05" + ], + [ + "We leverage both the visual and textual content for predicting age and gender.", + "Prediction with Textual Content:", + "We employ BIBREF73 's weighted lexicon of terms that uses the dataset of 75,394 Facebook users who shared their status, age and gender. The predictive power of this lexica was evaluated on Twitter, blog, and Facebook, showing promising results BIBREF73 . Utilizing these two weighted lexicon of terms, we are predicting the demographic information (age or gender) of INLINEFORM0 (denoted by INLINEFORM1 ) using following equation: INLINEFORM2 ", + "where INLINEFORM0 is the lexicon weight of the term, and INLINEFORM1 represents the frequency of the term in the user generated INLINEFORM2 , and INLINEFORM3 measures total word count in INLINEFORM4 . As our data is biased toward young people, we report age prediction performance for each age group separately (Table TABREF42 ). Moreover, to measure the average accuracy of this model, we build a balanced dataset (keeping all the users above 23 -416 users), and then randomly sampling the same number of users from the age ranges (11,19] and (19,23]. The average accuracy of this model is 0.63 for depressed users and 0.64 for control class. Table TABREF44 illustrates the performance of gender prediction for each class. The average accuracy is 0.82 on INLINEFORM5 ground-truth dataset.", + "Prediction with Visual Imagery:", + "Inspired by BIBREF56 's approach for facial landmark localization, we use their pretrained CNN consisting of convolutional layers, including unshared and fully-connected layers, to predict gender and age from both the profile and shared images. We evaluate the performance for gender and age prediction task on INLINEFORM0 and INLINEFORM1 respectively as shown in Table TABREF42 and Table TABREF44 .", + "Demographic Prediction Analysis:", + "We delve deeper into the benefits and drawbacks of each data modality for demographic information prediction. This is crucial as the differences between language cues between age groups above age 35 tend to become smaller (see Figure FIGREF39 -A,B,C) and making the prediction harder for older people BIBREF74 . In this case, the other data modality (e.g., visual content) can play integral role as a complementary source for age inference. For gender prediction (see Table TABREF44 ), on average, the profile image-based predictor provides a more accurate prediction for both the depressed and control class (0.92 and 0.90) compared to content-based predictor (0.82). For age prediction (see Table TABREF42 ), textual content-based predictor (on average 0.60) outperforms both of the visual-based predictors (on average profile:0.51, Media:0.53).", + "However, not every user provides facial identity on his account (see Table TABREF21 ). We studied facial presentation for each age-group to examine any association between age-group, facial presentation and depressive behavior (see Table TABREF43 ). We can see youngsters in both depressed and control class are not likely to present their face on profile image. Less than 3% of vulnerable users between 11-19 years reveal their facial identity. Although content-based gender predictor was not as accurate as image-based one, it is adequate for population-level analysis." + ], + [ + "We use the above findings for predicting depressive behavior. Our model exploits early fusion BIBREF32 technique in feature space and requires modeling each user INLINEFORM0 in INLINEFORM1 as vector concatenation of individual modality features. As opposed to computationally expensive late fusion scheme where each modality requires a separate supervised modeling, this model reduces the learning effort and shows promising results BIBREF75 . To develop a generalizable model that avoids overfitting, we perform feature selection using statistical tests and all relevant ensemble learning models. It adds randomness to the data by creating shuffled copies of all features (shadow feature), and then trains Random Forest classifier on the extended data. Iteratively, it checks whether the actual feature has a higher Z-score than its shadow feature (See Algorithm SECREF6 and Figure FIGREF45 ) BIBREF76 .", + "Main each Feature INLINEFORM0 INLINEFORM1 ", + "RndForrest( INLINEFORM0 ) Calculate Imp INLINEFORM1 INLINEFORM2 Generate next hypothesis , INLINEFORM3 Once all hypothesis generated Perform Statistical Test INLINEFORM4 //Binomial Distribution INLINEFORM5 Feature is important Feature is important", + " Ensemble Feature Selection", + "Next, we adopt an ensemble learning method that integrates the predictive power of multiple learners with two main advantages; its interpretability with respect to the contributions of each feature and its high predictive power. For prediction we have INLINEFORM0 where INLINEFORM1 is a weak learner and INLINEFORM2 denotes the final prediction.", + "In particular, we optimize the loss function: INLINEFORM0 where INLINEFORM1 incorporates INLINEFORM2 and INLINEFORM3 regularization. In each iteration, the new INLINEFORM4 is obtained by fitting weak learner to the negative gradient of loss function. Particularly, by estimating the loss function with Taylor expansion : INLINEFORM5 where its first expression is constant, the second and the third expressions are first ( INLINEFORM6 ) and second order derivatives ( INLINEFORM7 ) of the loss. INLINEFORM8 ", + "For exploring the weak learners, assume INLINEFORM0 has k leaf nodes, INLINEFORM1 be subset of users from INLINEFORM2 belongs to the node INLINEFORM3 , and INLINEFORM4 denotes the prediction for node INLINEFORM5 . Then, for each user INLINEFORM6 belonging to INLINEFORM7 , INLINEFORM8 and INLINEFORM9 INLINEFORM10 ", + "Next, for each leaf node INLINEFORM0 , deriving w.r.t INLINEFORM1 : INLINEFORM2 ", + "and by substituting weights: INLINEFORM0 ", + "which represents the loss for fixed weak learners with INLINEFORM0 nodes. The trees are built sequentially such that each subsequent tree aims to reduce the errors of its predecessor tree. Although, the weak learners have high bias, the ensemble model produces a strong learner that effectively integrate the weak learners by reducing bias and variance (the ultimate goal of supervised models) BIBREF77 . Table TABREF48 illustrates our multimodal framework outperform the baselines for identifying depressed users in terms of average specificity, sensitivity, F-Measure, and accuracy in 10-fold cross-validation setting on INLINEFORM1 dataset. Figure FIGREF47 shows how the likelihood of being classified into the depressed class varies with each feature addition to the model for a sample user in the dataset. The prediction bar (the black bar) shows that the log-odds of prediction is 0.31, that is, the likelihood of this person being a depressed user is 57% (1 / (1 + exp(-0.3))). The figure also sheds light on the impact of each contributing feature. The waterfall charts represent how the probability of being depressed changes with the addition of each feature variable. For instance, the \"Analytic thinking\" of this user is considered high 48.43 (Median:36.95, Mean: 40.18) and this decreases the chance of this person being classified into the depressed group by the log-odds of -1.41. Depressed users have significantly lower \"Analytic thinking\" score compared to control class. Moreover, the 40.46 \"Clout\" score is a low value (Median: 62.22, Mean: 57.17) and it decreases the chance of being classified as depressed. With respect to the visual features, for instance, the mean and the median of 'shared_colorfulness' is 112.03 and 113 respectively. The value of 136.71 would be high; thus, it decreases the chance of being depressed for this specific user by log-odds of -0.54. Moreover, the 'profile_naturalness' of 0.46 is considered high compared to 0.36 as the mean for the depressed class which justifies pull down of the log-odds by INLINEFORM2 . For network features, for instance, 'two_hop_neighborhood' for depressed users (Mean : 84) are less than that of control users (Mean: 154), and is reflected in pulling down the log-odds by -0.27.", + "Baselines:", + "To test the efficacy of our multi-modal framework for detecting depressed users, we compare it against existing content, content-network, and image-based models (based on the aforementioned general image feature, facial presence, and facial expressions.)" + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0470/instruction.md b/qasper-0470/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..6b321537b427a468f5ef4e26e69bdbc5e53c271b --- /dev/null +++ b/qasper-0470/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Text-based inference of moral sentiment change + +Question: Which fine-grained moral dimension examples do they showcase? \ No newline at end of file diff --git a/qasper-0477/instruction.md b/qasper-0477/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..c83967f1f7b1ba3b32b0267081361123025b7ac8 --- /dev/null +++ b/qasper-0477/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever + +Question: What were the evaluation metrics? \ No newline at end of file diff --git a/qasper-0479/instruction.md b/qasper-0479/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..089aa116f05c8118ca4675ab43abd6fb164f68d0 --- /dev/null +++ b/qasper-0479/instruction.md @@ -0,0 +1,165 @@ +Name of Paper: Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever + +Question: Which dialog datasets did they experiment with? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Definition", + "Definition ::: Dialogue History", + "Definition ::: Knowledge Base", + "Definition ::: Seq2Seq Dialogue Generation", + "Our Framework", + "Our Framework ::: Encoder", + "Our Framework ::: Vanilla Attention-based Decoder", + "Our Framework ::: Entity-Consistency Augmented Decoder", + "Our Framework ::: Entity-Consistency Augmented Decoder ::: KB Row Selection", + "Our Framework ::: Entity-Consistency Augmented Decoder ::: KB Row Selection ::: Dialogue History Representation:", + "Our Framework ::: Entity-Consistency Augmented Decoder ::: KB Row Selection ::: KB Row Representation:", + "Our Framework ::: Entity-Consistency Augmented Decoder ::: KB Row Selection ::: Memory Network-Based Retriever:", + "Our Framework ::: Entity-Consistency Augmented Decoder ::: KB Column Selection", + "Our Framework ::: Entity-Consistency Augmented Decoder ::: Decoder with Retrieved Entity", + "Training the KB-Retriever", + "Training the KB-Retriever ::: Training with Distant Supervision", + "Training the KB-Retriever ::: Training with Gumbel-Softmax", + "Training the KB-Retriever ::: Experimental Settings", + "Training the KB-Retriever ::: Baseline Models", + "Results", + "Results ::: The proportion of responses that can be supported by a single KB row", + "Results ::: Generation Consistency", + "Results ::: Correlation between the number of KB rows and generation consistency", + "Results ::: Visualization", + "Results ::: Human Evaluation", + "Related Work", + "Conclusion", + "Acknowledgments" + ], + "paragraphs": [ + [ + "Task-oriented dialogue system, which helps users to achieve specific goals with natural language, is attracting more and more research attention. With the success of the sequence-to-sequence (Seq2Seq) models in text generation BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, several works tried to model the task-oriented dialogue as the Seq2Seq generation of response from the dialogue history BIBREF5, BIBREF6, BIBREF7. This kind of modeling scheme frees the task-oriented dialogue system from the manually designed pipeline modules and heavy annotation labor for these modules.", + "Different from typical text generation, the successful conversations for task-oriented dialogue system heavily depend on accurate knowledge base (KB) queries. Taking the dialogue in Figure FIGREF1 as an example, to answer the driver's query on the gas station, the dialogue system is required to retrieve the entities like \u201c200 Alester Ave\u201d and \u201cValero\u201d. For the task-oriented system based on Seq2Seq generation, there is a trend in recent study towards modeling the KB query as an attention network over the entire KB entity representations, hoping to learn a model to pay more attention to the relevant entities BIBREF6, BIBREF7, BIBREF8, BIBREF9. Though achieving good end-to-end dialogue generation with over-the-entire-KB attention mechanism, these methods do not guarantee the generation consistency regarding KB entities and sometimes yield responses with conflict entities, like \u201cValero is located at 899 Ames Ct\u201d for the gas station query (as shown in Figure FIGREF1). In fact, the correct address for Valero is 200 Alester Ave. A consistent response is relatively easy to achieve for the conventional pipeline systems because they query the KB by issuing API calls BIBREF10, BIBREF11, BIBREF12, and the returned entities, which typically come from a single KB row, are consistently related to the object (like the \u201cgas station\u201d) that serves the user's request. This indicates that a response can usually be supported by a single KB row. It's promising to incorporate such observation into the Seq2Seq dialogue generation model, since it encourages KB relevant generation and avoids the model from producing responses with conflict entities.", + "To achieve entity-consistent generation in the Seq2Seq task-oriented dialogue system, we propose a novel framework which query the KB in two steps. In the first step, we introduce a retrieval module \u2014 KB-retriever to explicitly query the KB. Inspired by the observation that a single KB row usually supports a response, given the dialogue history and a set of KB rows, the KB-retriever uses a memory network BIBREF13 to select the most relevant row. The retrieval result is then fed into a Seq2Seq dialogue generation model to filter the irrelevant KB entities and improve the consistency within the generated entities. In the second step, we further perform attention mechanism to address the most correlated KB column. Finally, we adopt the copy mechanism to incorporate the retrieved KB entity.", + "Since dialogue dataset is not typically annotated with the retrieval results, training the KB-retriever is non-trivial. To make the training feasible, we propose two methods: 1) we use a set of heuristics to derive the training data and train the retriever in a distant supervised fashion; 2) we use Gumbel-Softmax BIBREF14 as an approximation of the non-differentiable selecting process and train the retriever along with the Seq2Seq dialogue generation model. Experiments on two publicly available datasets (Camrest BIBREF11 and InCar Assistant BIBREF6) confirm the effectiveness of the KB-retriever. Both the retrievers trained with distant-supervision and Gumbel-Softmax technique outperform the compared systems in the automatic and human evaluations. Analysis empirically verifies our assumption that more than 80% responses in the dataset can be supported by a single KB row and better retrieval results lead to better task-oriented dialogue generation performance." + ], + [ + "In this section, we will describe the input and output of the end-to-end task-oriented dialogue system, and the definition of Seq2Seq task-oriented dialogue generation." + ], + [ + "Given a dialogue between a user ($u$) and a system ($s$), we follow eric:2017:SIGDial and represent the $k$-turned dialogue utterances as $\\lbrace (u_{1}, s_{1} ), (u_{2} , s_{2} ), ... , (u_{k}, s_{k})\\rbrace $. At the $i^{\\text{th}}$ turn of the dialogue, we aggregate dialogue context which consists of the tokens of $(u_{1}, s_{1}, ..., s_{i-1}, u_{i})$ and use $\\mathbf {x} = (x_{1}, x_{2}, ..., x_{m})$ to denote the whole dialogue history word by word, where $m$ is the number of tokens in the dialogue history." + ], + [ + "In this paper, we assume to have the access to a relational-database-like KB $B$, which consists of $|\\mathcal {R}|$ rows and $|\\mathcal {C}|$ columns. The value of entity in the $j^{\\text{th}}$ row and the $i^{\\text{th}}$ column is noted as $v_{j, i}$." + ], + [ + "We define the Seq2Seq task-oriented dialogue generation as finding the most likely response $\\mathbf {y}$ according to the input dialogue history $\\mathbf {x}$ and KB $B$. Formally, the probability of a response is defined as", + "where $y_t$ represents an output token." + ], + [ + "In this section, we describe our framework for end-to-end task-oriented dialogues. The architecture of our framework is demonstrated in Figure FIGREF3, which consists of two major components including an memory network-based retriever and the seq2seq dialogue generation with KB Retriever. Our framework first uses the KB-retriever to select the most relevant KB row and further filter the irrelevant entities in a Seq2Seq response generation model to improve the consistency among the output entities. While in decoding, we further perform the attention mechanism to choose the most probable KB column. We will present the details of our framework in the following sections." + ], + [ + "In our encoder, we adopt the bidirectional LSTM BIBREF15 to encode the dialogue history $\\mathbf {x}$, which captures temporal relationships within the sequence. The encoder first map the tokens in $\\mathbf {x}$ to vectors with embedding function $\\phi ^{\\text{emb}}$, and then the BiLSTM read the vector forwardly and backwardly to produce context-sensitive hidden states $(\\mathbf {h}_{1}, \\mathbf {h}_2, ..., \\mathbf {h}_{m})$ by repeatedly applying the recurrence $\\mathbf {h}_{i}=\\text{BiLSTM}\\left( \\phi ^{\\text{emb}}\\left( x_{i}\\right) , \\mathbf {h}_{i-1}\\right)$." + ], + [ + "Here, we follow eric:2017:SIGDial to adopt the attention-based decoder to generation the response word by word. LSTM is also used to represent the partially generated output sequence $(y_{1}, y_2, ...,y_{t-1})$ as $(\\tilde{\\mathbf {h}}_{1}, \\tilde{\\mathbf {h}}_2, ...,\\tilde{\\mathbf {h}}_t)$. For the generation of next token $y_t$, their model first calculates an attentive representation $\\tilde{\\mathbf {h}}^{^{\\prime }}_t$ of the dialogue history as", + "Then, the concatenation of the hidden representation of the partially outputted sequence $\\tilde{\\mathbf {h}}_t$ and the attentive dialogue history representation $\\tilde{\\mathbf {h}}^{^{\\prime }}_t$ are projected to the vocabulary space $\\mathcal {V}$ by $U$ as", + "to calculate the score (logit) for the next token generation. The probability of next token $y_t$ is finally calculated as" + ], + [ + "As shown in section SECREF7, we can see that the generation of tokens are just based on the dialogue history attention, which makes the model ignorant to the KB entities. In this section, we present how to query the KB explicitly in two steps for improving the entity consistence, which first adopt the KB-retriever to select the most relevant KB row and the generation of KB entities from the entities-augmented decoder is constrained to the entities within the most probable row, thus improve the entity generation consistency. Next, we perform the column attention to select the most probable KB column. Finally, we show how to use the copy mechanism to incorporate the retrieved entity while decoding." + ], + [ + "In our framework, our KB-retriever takes the dialogue history and KB rows as inputs and selects the most relevant row. This selection process resembles the task of selecting one word from the inputs to answer questions BIBREF13, and we use a memory network to model this process. In the following sections, we will first describe how to represent the inputs, then we will talk about our memory network-based retriever" + ], + [ + "We encode the dialogue history by adopting the neural bag-of-words (BoW) followed the original paper BIBREF13. Each token in the dialogue history is mapped into a vector by another embedding function $\\phi ^{\\text{emb}^{\\prime }}(x)$ and the dialogue history representation $\\mathbf {q}$ is computed as the sum of these vectors: $\\mathbf {q} = \\sum ^{m}_{i=1} \\phi ^{\\text{emb}^{\\prime }} (x_{i}) $." + ], + [ + "In this section, we describe how to encode the KB row. Each KB cell is represented as the cell value $v$ embedding as $\\mathbf {c}_{j, k} = \\phi ^{\\text{value}}(v_{j, k})$, and the neural BoW is also used to represent a KB row $\\mathbf {r}_{j}$ as $\\mathbf {r}_{j} = \\sum _{k=1}^{|\\mathcal {C}|} \\mathbf {c}_{j,k}$." + ], + [ + "We model the KB retrieval process as selecting the row that most-likely supports the response generation. Memory network BIBREF13 has shown to be effective to model this kind of selection. For a $n$-hop memory network, the model keeps a set of input matrices $\\lbrace R^{1}, R^{2}, ..., R^{n+1}\\rbrace $, where each $R^{i}$ is a stack of $|\\mathcal {R}|$ inputs $(\\mathbf {r}^{i}_1, \\mathbf {r}^{i}_2, ..., \\mathbf {r}^{i}_{|\\mathcal {R}|})$. The model also keeps query $\\mathbf {q}^{1}$ as the input. A single hop memory network computes the probability $\\mathbf {a}_j$ of selecting the $j^{\\text{th}}$ input as", + "For the multi-hop cases, layers of single hop memory network are stacked and the query of the $(i+1)^{\\text{th}}$ layer network is computed as", + "and the output of the last layer is used as the output of the whole network. For more details about memory network, please refer to the original paper BIBREF13.", + "After getting $\\mathbf {a}$, we represent the retrieval results as a 0-1 matrix $T \\in \\lbrace 0, 1\\rbrace ^{|\\mathcal {R}|\\times \\mathcal {|C|}}$, where each element in $T$ is calculated as", + "In the retrieval result, $T_{j, k}$ indicates whether the entity in the $j^{\\text{th}}$ row and the $k^{\\text{th}}$ column is relevant to the final generation of the response. In this paper, we further flatten T to a 0-1 vector $\\mathbf {t} \\in \\lbrace 0, 1\\rbrace ^{|\\mathcal {E}|}$ (where $|\\mathcal {E}|$ equals $|\\mathcal {R}|\\times \\mathcal {|C|}$) as our retrieval row results." + ], + [ + "After getting the retrieved row result that indicates which KB row is the most relevant to the generation, we further perform column attention in decoding time to select the probable KB column. For our KB column selection, following the eric:2017:SIGDial we use the decoder hidden state $(\\tilde{\\mathbf {h}}_{1}, \\tilde{\\mathbf {h}}_2, ...,\\tilde{\\mathbf {h}}_t)$ to compute an attention score with the embedding of column attribute name. The attention score $\\mathbf {c}\\in R^{|\\mathcal {E}|}$ then become the logits of the column be selected, which can be calculated as", + "where $\\mathbf {c}_j$ is the attention score of the $j^{\\text{th}}$ KB column, $\\mathbf {k}_j$ is represented with the embedding of word embedding of KB column name. $W^{^{\\prime }}_{1}$, $W^{^{\\prime }}_{2}$ and $\\mathbf {t}^{T}$ are trainable parameters of the model." + ], + [ + "After the row selection and column selection, we can define the final retrieved KB entity score as the element-wise dot between the row retriever result and the column selection score, which can be calculated as", + "where the $v^{t}$ indicates the final KB retrieved entity score. Finally, we follow eric:2017:SIGDial to use copy mechanism to incorporate the retrieved entity, which can be defined as", + "where $\\mathbf {o}_t$\u2019s dimensionality is $ |\\mathcal {V}|$ +$|\\mathcal {E}|$. In $\\mathbf {v}^t$ , lower $ |\\mathcal {V}|$ is zero and the rest$|\\mathcal {E}|$ is retrieved entity scores." + ], + [ + "As mentioned in section SECREF9, we adopt the memory network to train our KB-retriever. However, in the Seq2Seq dialogue generation, the training data does not include the annotated KB row retrieval results, which makes supervised training the KB-retriever impossible. To tackle this problem, we propose two training methods for our KB-row-retriever. 1) In the first method, inspired by the recent success of distant supervision in information extraction BIBREF16, BIBREF17, BIBREF18, BIBREF19, we take advantage of the similarity between the surface string of KB entries and the reference response, and design a set of heuristics to extract training data for the KB-retriever. 2) In the second method, instead of training the KB-retriever as an independent component, we train it along with the training of the Seq2Seq dialogue generation. To make the retrieval process in Equation DISPLAY_FORM13 differentiable, we use Gumbel-Softmax BIBREF14 as an approximation of the $\\operatornamewithlimits{argmax}$ during training." + ], + [ + "Although it's difficult to obtain the annotated retrieval data for the KB-retriever, we can \u201cguess\u201d the most relevant KB row from the reference response, and then obtain the weakly labeled data for the retriever. Intuitively, for the current utterance in the same dialogue which usually belongs to one topic and the KB row that contains the largest number of entities mentioned in the whole dialogue should support the utterance. In our training with distant supervision, we further simplify our assumption and assume that one dialogue which is usually belongs to one topic and can be supported by the most relevant KB row, which means for a $k$-turned dialogue, we construct $k$ pairs of training instances for the retriever and all the inputs $(u_{1}, s_{1}, ..., s_{i-1}, u_{i} \\mid i \\le k)$ are associated with the same weakly labeled KB retrieval result $T^*$.", + "In this paper, we compute each row's similarity to the whole dialogue and choose the most similar row as $T^*$. We define the similarity of each row as the number of matched spans with the surface form of the entities in the row. Taking the dialogue in Figure FIGREF1 for an example, the similarity of the 4$^\\text{th}$ row equals to 4 with \u201c200 Alester Ave\u201d, \u201cgas station\u201d, \u201cValero\u201d, and \u201croad block nearby\u201d matching the dialogue context; and the similarity of the 7$^\\text{th}$ row equals to 1 with only \u201croad block nearby\u201d matching.", + "In our model with the distantly supervised retriever, the retrieval results serve as the input for the Seq2Seq generation. During training the Seq2Seq generation, we use the weakly labeled retrieval result $T^{*}$ as the input." + ], + [ + "In addition to treating the row retrieval result as an input to the generation model, and training the kb-row-retriever independently, we can train it along with the training of the Seq2Seq dialogue generation in an end-to-end fashion. The major difficulty of such a training scheme is that the discrete retrieval result is not differentiable and the training signal from the generation model cannot be passed to the parameters of the retriever. Gumbel-softmax technique BIBREF14 has been shown an effective approximation to the discrete variable and proved to work in sentence representation. In this paper, we adopt the Gumbel-Softmax technique to train the KB retriever. We use", + "as the approximation of $T$, where $\\mathbf {g}_{j}$ are i.i.d samples drawn from $\\text{Gumbel}(0,1)$ and $\\tau $ is a constant that controls the smoothness of the distribution. $T^{\\text{approx}}_{j}$ replaces $T^{\\text{}}_{j}$ in equation DISPLAY_FORM13 and goes through the same flattening and expanding process as $\\mathbf {V}$ to get $\\mathbf {v}^{\\mathbf {t}^{\\text{approx}^{\\prime }}}$ and the training signal from Seq2Seq generation is passed via the logit", + "To make training with Gumbel-Softmax more stable, we first initialize the parameters by pre-training the KB-retriever with distant supervision and further fine-tuning our framework." + ], + [ + "We choose the InCar Assistant dataset BIBREF6 including three distinct domains: navigation, weather and calendar domain. For weather domain, we follow wen2018sequence to separate the highest temperature, lowest temperature and weather attribute into three different columns. For calendar domain, there are some dialogues without a KB or incomplete KB. In this case, we padding a special token \u201c-\u201d in these incomplete KBs. Our framework is trained separately in these three domains, using the same train/validation/test split sets as eric:2017:SIGDial. To justify the generalization of the proposed model, we also use another public CamRest dataset BIBREF11 and partition the datasets into training, validation and testing set in the ratio 3:1:1. Especially, we hired some human experts to format the CamRest dataset by equipping the corresponding KB to every dialogues.", + "All hyper-parameters are selected according to validation set. We use a three-hop memory network to model our KB-retriever. The dimensionalities of the embedding is selected from $\\lbrace 100, 200\\rbrace $ and LSTM hidden units is selected from $\\lbrace 50, 100, 150, 200, 350\\rbrace $. The dropout we use in our framework is selected from $\\lbrace 0.25, 0.5, 0.75\\rbrace $ and the batch size we adopt is selected from $\\lbrace 1,2\\rbrace $. L2 regularization is used on our model with a tension of $5\\times 10^{-6}$ for reducing overfitting. For training the retriever with distant supervision, we adopt the weight typing trick BIBREF20. We use Adam BIBREF21 to optimize the parameters in our model and adopt the suggested hyper-parameters for optimization.", + "We adopt both the automatic and human evaluations in our experiments." + ], + [ + "We compare our model with several baselines including:", + "Attn seq2seq BIBREF22: A model with simple attention over the input context at each time step during decoding.", + "Ptr-UNK BIBREF23: Ptr-UNK is the model which augments a sequence-to-sequence architecture with attention-based copy mechanism over the encoder context.", + "KV Net BIBREF6: The model adopted and argumented decoder which decodes over the concatenation of vocabulary and KB entities, which allows the model to generate entities.", + "Mem2Seq BIBREF7: Mem2Seq is the model that takes dialogue history and KB entities as input and uses a pointer gate to control either generating a vocabulary word or selecting an input as the output.", + "DSR BIBREF9: DSR leveraged dialogue state representation to retrieve the KB implicitly and applied copying mechanism to retrieve entities from knowledge base while decoding.", + "In InCar dataset, for the Attn seq2seq, Ptr-UNK and Mem2seq, we adopt the reported results from madotto2018mem2seq. In CamRest dataset, for the Mem2Seq, we adopt their open-sourced code to get the results while for the DSR, we run their code on the same dataset to obtain the results." + ], + [ + "Follow the prior works BIBREF6, BIBREF7, BIBREF9, we adopt the BLEU and the Micro Entity F1 to evaluate our model performance. The experimental results are illustrated in Table TABREF30.", + "In the first block of Table TABREF30, we show the Human, rule-based and KV Net (with*) result which are reported from eric:2017:SIGDial. We argue that their results are not directly comparable because their work uses the entities in thier canonicalized forms, which are not calculated based on real entity value. It's noticing that our framework with two methods still outperform KV Net in InCar dataset on whole BLEU and Entity F metrics, which demonstrates the effectiveness of our framework.", + "In the second block of Table TABREF30, we can see that our framework trained with both the distant supervision and the Gumbel-Softmax beats all existing models on two datasets. Our model outperforms each baseline on both BLEU and F1 metrics. In InCar dataset, Our model with Gumbel-Softmax has the highest BLEU compared with baselines, which which shows that our framework can generate more fluent response. Especially, our framework has achieved 2.5% improvement on navigate domain, 1.8% improvement on weather domain and 3.5% improvement on calendar domain on F1 metric. It indicates that the effectiveness of our KB-retriever module and our framework can retrieve more correct entity from KB. In CamRest dataset, the same trend of improvement has been witnessed, which further show the effectiveness of our framework.", + "Besides, we observe that the model trained with Gumbel-Softmax outperforms with distant supervision method. We attribute this to the fact that the KB-retriever and the Seq2Seq module are fine-tuned in an end-to-end fashion, which can refine the KB-retriever and further promote the dialogue generation." + ], + [ + "In this section, we verify our assumption by examining the proportion of responses that can be supported by a single row.", + "We define a response being supported by the most relevant KB row as all the responded entities are included by that row. We study the proportion of these responses over the test set. The number is 95% for the navigation domain, 90% for the CamRest dataset and 80% for the weather domain. This confirms our assumption that most responses can be supported by the relevant KB row. Correctly retrieving the supporting row should be beneficial.", + "We further study the weather domain to see the rest 20% exceptions. Instead of being supported by multiple rows, most of these exceptions cannot be supported by any KB row. For example, there is one case whose reference response is \u201cIt 's not rainy today\u201d, and the related KB entity is sunny. These cases provide challenges beyond the scope of this paper. If we consider this kind of cases as being supported by a single row, such proportion in the weather domain is 99%." + ], + [ + "In this paper, we expect the consistent generation from our model. To verify this, we compute the consistency recall of the utterances that have multiple entities. An utterance is considered as consistent if it has multiple entities and these entities belong to the same row which we annotated with distant supervision.", + "The consistency result is shown in Table TABREF37. From this table, we can see that incorporating retriever in the dialogue generation improves the consistency." + ], + [ + "To further explore the correlation between the number of KB rows and generation consistency, we conduct experiments with distant manner to study the correlation between the number of KB rows and generation consistency.", + "We choose KBs with different number of rows on a scale from 1 to 5 for the generation. From Figure FIGREF32, as the number of KB rows increase, we can see a decrease in generation consistency. This indicates that irrelevant information would harm the dialogue generation consistency." + ], + [ + "To gain more insights into how the our retriever module influences the whole KB score distribution, we visualized the KB entity probability at the decoding position where we generate the entity 200_Alester_Ave. From the example (Fig FIGREF38), we can see the $4^\\text{th}$ row and the $1^\\text{th}$ column has the highest probabilities for generating 200_Alester_Ave, which verify the effectiveness of firstly selecting the most relevant KB row and further selecting the most relevant KB column." + ], + [ + "We provide human evaluation on our framework and the compared models. These responses are based on distinct dialogue history. We hire several human experts and ask them to judge the quality of the responses according to correctness, fluency, and humanlikeness on a scale from 1 to 5. In each judgment, the expert is presented with the dialogue history, an output of a system with the name anonymized, and the gold response.", + "The evaluation results are illustrated in Table TABREF37. Our framework outperforms other baseline models on all metrics according to Table TABREF37. The most significant improvement is from correctness, indicating that our model can retrieve accurate entity from KB and generate more informative information that the users want to know." + ], + [ + "Sequence-to-sequence (Seq2Seq) models in text generation BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4 has gained more popular and they are applied for the open-domain dialogs BIBREF24, BIBREF25 in the end-to-end training method. Recently, the Seq2Seq can be used for learning task oriented dialogs and how to query the structured KB is the remaining challenges.", + "Properly querying the KB has long been a challenge in the task-oriented dialogue system. In the pipeline system, the KB query is strongly correlated with the design of language understanding, state tracking, and policy management. Typically, after obtaining the dialogue state, the policy management module issues an API call accordingly to query the KB. With the development of neural network in natural language processing, efforts have been made to replacing the discrete and pre-defined dialogue state with the distributed representation BIBREF10, BIBREF11, BIBREF12, BIBREF26. In our framework, our retrieval result can be treated as a numeric representation of the API call return.", + "Instead of interacting with the KB via API calls, more and more recent works tried to incorporate KB query as a part of the model. The most popular way of modeling KB query is treating it as an attention network over the entire KB entities BIBREF6, BIBREF27, BIBREF8, BIBREF28, BIBREF29 and the return can be a fuzzy summation of the entity representations. madotto2018mem2seq's practice of modeling the KB query with memory network can also be considered as learning an attentive preference over these entities. wen2018sequence propose the implicit dialogue state representation to query the KB and achieve the promising performance. Different from their modes, we propose the KB-retriever to explicitly query the KB, and the query result is used to filter the irrelevant entities in the dialogue generation to improve the consistency among the output entities." + ], + [ + "In this paper, we propose a novel framework to improve entities consistency by querying KB in two steps. In the first step, inspired by the observation that a response can usually be supported by a single KB row, we introduce the KB retriever to return the most relevant KB row, which is used to filter the irrelevant KB entities and encourage consistent generation. In the second step, we further perform attention mechanism to select the most relevant KB column. Experimental results show the effectiveness of our method. Extensive analysis further confirms the observation and reveal the correlation between the success of KB query and the success of task-oriented dialogue generation." + ], + [ + "We thank the anonymous reviewers for their helpful comments and suggestions. This work was supported by the National Natural Science Foundation of China (NSFC) via grant 61976072, 61632011 and 61772153." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0483/instruction.md b/qasper-0483/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..0bdb083d1e895921086c5b17e05f4d87fadc60d9 --- /dev/null +++ b/qasper-0483/instruction.md @@ -0,0 +1,83 @@ +Name of Paper: From FiLM to Video: Multi-turn Question Answering with Multi-modal Context + +Question: Do they train a different training method except from scheduled sampling? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work", + "The avsd dataset and challenge", + "Models", + "Utterance-level Encoder", + "Description Encoder", + "Video Encoder with Time-Extended FiLM", + "Audio Encoder", + "Fusing Modalities for Dialogue Context", + "Decoders", + "Loss Function", + "Experiments", + "Conclusions" + ], + "paragraphs": [ + [ + "Deep neural networks have been successfully applied to several computer vision tasks such as image classification BIBREF0 , object detection BIBREF1 , video action classification BIBREF2 , etc. They have also been successfully applied to natural language processing tasks such as machine translation BIBREF3 , machine reading comprehension BIBREF4 , etc. There has also been an explosion of interest in tasks which combine multiple modalities such as audio, vision, and language together. Some popular multi-modal tasks combining these three modalities, and their differences are highlighted in Table TABREF1 .", + "Given an image and a question related to the image, the vqa challenge BIBREF5 tasked users with selecting an answer to the question. BIBREF6 identified several sources of bias in the vqa dataset, which led to deep neural models answering several questions superficially. They found that in several instances, deep architectures exploited the statistics of the dataset to select answers ignoring the provided image. This prompted the release of vqa 2.0 BIBREF7 which attempts to balance the original dataset. In it, each question is paired to two similar images which have different answers. Due to the complexity of vqa, understanding the failures of deep neural architectures for this task has been a challenge. It is not easy to interpret whether the system failed in understanding the question or in understanding the image or in reasoning over it. The CLEVR dataset BIBREF8 was hence proposed as a useful benchmark to evaluate such systems on the task of visual reasoning. Extending question answering over images to videos, BIBREF9 have proposed MovieQA, where the task is to select the correct answer to a provided question given the movie clip on which it is based.", + "Intelligent systems that can interact with human users for a useful purpose are highly valuable. To this end, there has been a recent push towards moving from single-turn qa to multi-turn dialogue, which is a natural and intuitive setting for humans. Among multi-modal dialogue tasks, visdial BIBREF10 provides an image and dialogue where each turn is a qa pair. The task is to train a model to answer these questions within the dialogue. The avsd challenge extends the visdial task from images to the audio-visual domain.", + "We present our modelname model for the avsd task. modelname combines a hred for encoding and generating qa-dialogue with a novel FiLM-based audio-visual feature extractor for videos and an auxiliary multi-task learning-based decoder for decoding a summary of the video. It outperforms the baseline results for the avsd dataset BIBREF11 and was ranked 2nd overall among the dstc7 avsd challenge participants.", + "In Section SECREF2 , we discuss existing literature on end-to-end dialogue systems with a special focus on multi-modal dialogue systems. Section SECREF3 describes the avsd dataset. In Section SECREF4 , we present the architecture of our modelname model. We describe our evaluation and experimental setup in Section SECREF5 and then conclude in Section SECREF6 ." + ], + [ + "With the availability of large conversational corpora from sources like Reddit and Twitter, there has been a lot of recent work on end-to-end modelling of dialogue for open domains. BIBREF12 treated dialogue as a machine translation problem where they translate from the stimulus to the response. They observed this to be more challenging than machine translation tasks due the larger diversity of possible responses. Among approaches that just use the previous utterance to generate the current response, BIBREF13 proposed a response generation model based on the encoder decoder framework. BIBREF14 also proposed an encoder-decoder based neural network architecture that uses the previous two utterances to generate the current response. Among discriminative methods (i.e. methods that produce a score for utterances from a set and then rank them), BIBREF15 proposed a neural architecture to select the best next response from a list of responses by measuring their similarity to the dialogue context. BIBREF16 extended prior work on encoder-decoder-based models to multi-turn conversations. They trained a hierarchical model called hred for generating dialogue utterances where a recurrent neural network encoder encodes each utterance. A higher-level recurrent neural network maintains the dialogue state by further encoding the individual utterance encodings. This dialogue state is then decoded by another recurrent decoder to generate the response at that point in time. In followup work, BIBREF17 used a latent stochastic variable to condition the generation process which aided their model in producing longer coherent outputs that better retain the context.", + "Datasets and tasks BIBREF10 , BIBREF18 , BIBREF19 have also been released recently to study visual-input based conversations. BIBREF10 train several generative and discriminative deep neural models for the visdial task. They observe that on this task, discriminative models outperform generative models and that models making better use of the dialogue history do better than models that do not use dialogue history at all. Unexpectedly, the performance between models that use the image features and models that do no use these features is not significantly different. As we discussed in Section SECREF1 , this is similar to the issues vqa models faced initially due to the imbalanced nature of the dataset, which leads us to believe that language is a strong prior on the visdial dataset too. BIBREF20 train two separate agents to play a cooperative game where one agent has to answer the other agent's questions, which in turn has to predict the fc7 features of the Image obtained from VGGNet. Both agents are based on hred models and they show that agents fine-tuned with rl outperform agents trained solely with supervised learning. BIBREF18 train both generative and discriminative deep neural models on the igc dataset, where the task is to generate questions and answers to carry on a meaningful conversation. BIBREF19 train hred-based models on GuessWhat?! dataset in which agents have to play a guessing game where one player has to find an object in the picture which the other player knows about and can answer questions about them.", + "Moving from image-based dialogue to video-based dialogue adds further complexity and challenges. Limited availability of such data is one of the challenges. Apart from the avsd dataset, there does not exist a video dialogue dataset to the best of our knowledge and the avsd data itself is fairly limited in size. Extracting relevant features from videos also contains the inherent complexity of extracting features from individual frames and additionally requires understanding their temporal interaction. The temporal nature of videos also makes it important to be able to focus on a varying-length subset of video frames as the action which is being asked about might be happening within them. There is also the need to encode the additional modality of audio which would be required for answering questions that rely on the audio track. With limited size of publicly available datasets based on the visual modality, learning useful features from high dimensional visual data has been a challenge even for the visdial dataset, and we anticipate this to be an even more significant challenge on the avsd dataset as it involves videos.", + "On the avsd task, BIBREF11 train an attention-based audio-visual scene-aware dialogue model which we use as the baseline model for this paper. They divide each video into multiple equal-duration segments and, from each of them, extract video features using an I3D BIBREF21 model, and audio features using a VGGish BIBREF22 model. The I3D model was pre-trained on Kinetics BIBREF23 dataset and the VGGish model was pre-trained on Audio Set BIBREF24 . The baseline encodes the current utterance's question with a lstm BIBREF25 and uses the encoding to attend to the audio and video features from all the video segments and to fuse them together. The dialogue history is modelled with a hierarchical recurrent lstm encoder where the input to the lower level encoder is a concatenation of question-answer pairs. The fused feature representation is concatenated with the question encoding and the dialogue history encoding and the resulting vector is used to decode the current answer using an lstm decoder. Similar to the visdial models, the performance difference between the best model that uses text and the best model that uses both text and video features is small. This indicates that the language is a stronger prior here and the baseline model is unable to make good use of the highly relevant video.", + "Automated evaluation of both task-oriented and non-task-oriented dialogue systems has been a challenge BIBREF26 , BIBREF27 too. Most such dialogue systems are evaluated using per-turn evaluation metrics since there is no suitable per-dialogue metric as conversations do not need to happen in a deterministic ordering of turns. These per-turn evaluation metrics are mostly word-overlap-based metrics such as BLEU, METEOR, ROUGE, and CIDEr, borrowed from the machine translation literature. Due to the diverse nature of possible responses, world-overlap metrics are not highly suitable for evaluating these tasks. Human evaluation of generated responses is considered the most reliable metric for such tasks but it is cost prohibitive and hence the dialogue system literature continues to rely widely on word-overlap-based metrics." + ], + [ + "The avsd dataset BIBREF28 consists of dialogues collected via amt. Each dialogue is associated with a video from the Charades BIBREF29 dataset and has conversations between two amt workers related to the video. The Charades dataset has multi-action short videos and it provides text descriptions for these videos, which the avsd challenge also distributes as the caption. The avsd dataset has been collected using similar methodology as the visdial dataset. In avsd, each dialogue turn consists of a question and answer pair. One of the amt workers assumes the role of questioner while the other amt worker assumes the role of answerer. The questioner sees three static frames from the video and has to ask questions. The answerer sees the video and answers the questions asked by the questioner. After 10 such qa turns, the questioner wraps up by writing a summary of the video based on the conversation.", + "Dataset statistics such as the number of dialogues, turns, and words for the avsd dataset are presented in Table TABREF5 . For the initially released prototype dataset, the training set of the avsd dataset corresponds to videos taken from the training set of the Charades dataset while the validation and test sets of the avsd dataset correspond to videos taken from the validation set of the Charades dataset. For the official dataset, training, validation and test sets are drawn from the corresponding Charades sets.", + "The Charades dataset also provides additional annotations for the videos such as action, scene, and object annotations, which are considered to be external data sources by the avsd challenge, for which there is a special sub-task in the challenge. The action annotations also include the start and end time of the action in the video." + ], + [ + "Our modelname model is based on the hred framework for modelling dialogue systems. In our model, an utterance-level recurrent lstm encoder encodes utterances and a dialogue-level recurrent lstm encoder encodes the final hidden states of the utterance-level encoders, thus maintaining the dialogue state and dialogue coherence. We use the final hidden states of the utterance-level encoders in the attention mechanism that is applied to the outputs of the description, video, and audio encoders. The attended features from these encoders are fused with the dialogue-level encoder's hidden states. An utterance-level decoder decodes the response for each such dialogue state following a question. We also add an auxiliary decoding module which is similar to the response decoder except that it tries to generate the caption and/or the summary of the video. We present our model in Figure FIGREF2 and describe the individual components in detail below." + ], + [ + "The utterance-level encoder is a recurrent neural network consisting of a single layer of lstm cells. The input to the lstm are word embeddings for each word in the utterance. The utterance is concatenated with a special symbol marking the end of the sequence. We initialize our word embeddings using 300-dimensional GloVe BIBREF30 and then fine-tune them during training. For words not present in the GloVe vocabulary, we initialize their word embeddings from a random uniform distribution." + ], + [ + "Similar to the utterance-level encoder, the description encoder is also a single-layer lstm recurrent neural network. Its word embeddings are also initialized with GloVe and then fine-tuned during training. For the description, we use the caption and/or the summary for the video provided with the dataset. The description encoder also has access to the last hidden state of the utterance-level encoder, which it uses to generate an attention map over the hidden states of its lstm. The final output of this module is the attention-weighted sum of the lstm hidden states." + ], + [ + "For the video encoder, we use an I3D model pre-trained on the Kinetics dataset BIBREF23 and extract the output of its Mixed_7c layer for INLINEFORM0 (30 for our models) equi-distant segments of the video. Over these features, we add INLINEFORM1 (2 for our models) FiLM BIBREF31 blocks which have been highly successful in visual reasoning problems. Each FiLM block applies a conditional (on the utterance encoding) feature-wise affine transformation on the features input to it, ultimately leading to the extraction of more relevant features. The FiLM blocks are followed by fully connected layers which are further encoded by a single layer recurrent lstm network. The last hidden state of the utterance-level encoder then generates an attention map over the hidden states of its lstm, which is multiplied by the hidden states to provide the output of this module. We also experimented with using convolutional Mixed_5c features to capture spatial information but on the limited avsd dataset they did not yield any improvement. When not using the FiLM blocks, we use the final layer I3D features (provided by the avsd organizers) and encode them with the lstm directly, followed by the attention step. We present the video encoder in Figure FIGREF3 ." + ], + [ + "The audio encoder is structurally similar to the video encoder. We use the VGGish features provided by the avsd challenge organizers. Also similar to the video encoder, when not using the FiLM blocks, we use the VGGish features and encode them with the lstm directly, followed by the attention step. The audio encoder is depicted in Figure FIGREF4 ." + ], + [ + "The outputs of the encoders for past utterances, descriptions, video, and audio together form the dialogue context INLINEFORM0 which is the input of the decoder. We first combine past utterances using a dialogue-level encoder which is a single-layer lstm recurrent neural network. The input to this encoder are the final hidden states of the utterance-level lstm. To combine the hidden states of these diverse modalities, we found concatenation to perform better on the validation set than averaging or the Hadamard product." + ], + [ + "The answer decoder consists of a single-layer recurrent lstm network and generates the answer to the last question utterance. At each time-step, it is provided with the dialogue-level state and produces a softmax over a vector corresponding to vocabulary words and stops when 30 words were produced or an end of sentence token is encountered.", + "The auxiliary decoder is functionally similar to the answer decoder. The decoded sentence is the caption and/or description of the video. We use the Video Encoder state instead of the Dialogue-level Encoder state as input since with this module we want to learn a better video representation capable of decoding the description." + ], + [ + "For a given context embedding INLINEFORM0 at dialogue turn INLINEFORM1 , we minimize the negative log-likelihood of the answer word INLINEFORM2 (vocabulary size), normalized by the number of words INLINEFORM3 in the ground truth response INLINEFORM4 , L(Ct, r) = -1Mm=1MiV( [rt,m=i] INLINEFORM5 ) , where the probabilities INLINEFORM6 are given by the decoder LSTM output, r*t,m-1 ={ll rt,m-1 ; s>0.2, sU(0, 1)", + "v INLINEFORM0 ; else . is given by scheduled sampling BIBREF32 , and INLINEFORM1 is a symbol denoting the start of a sequence. We optimize the model using the AMSGrad algorithm BIBREF33 and use a per-condition random search to determine hyperparameters. We train the model using the BLEU-4 score on the validation set as our stopping citerion." + ], + [ + "The avsd challenge tasks we address here are:", + "We train our modelname model for Task 1.a and Task 2.a of the challenge and we present the results in Table TABREF9 . Our model outperforms the baseline model released by BIBREF11 on all of these tasks. The scores for the winning team have been released to challenge participants and are also included. Their approach, however, is not public as of yet. We observe the following for our models:", + "Since the official test set has not been released publicly, results reported on the official test set have been provided by the challenge organizers. For the prototype test set and for the ablation study presented in Table TABREF24 , we use the same code for evaluation metrics as used by BIBREF11 for fairness and comparability. We attribute the significant performance gain of our model over the baseline to a combination of several factors as described below:", + "Our primary architectural differences over the baseline model are: not concatenating the question, answer pairs before encoding them, the auxiliary decoder module, and using the Time-Extended FiLM module for feature extraction. These, combined with using scheduled sampling and running hyperparameter optimization over the validation set to select hyperparameters, give us the observed performance boost.", + "We observe that our models generate fairly relevant responses to questions in the dialogues, and models with audio-visual inputs respond to audio-visual questions (e.g. \u201cis there any voices or music ?\u201d) correctly more often.", + "We conduct an ablation study on the effectiveness of different components (eg., text, video and audio) and present it in Table TABREF24 . Our experiments show that:" + ], + [ + "We presented modelname, a state-of-the-art dialogue model for conversations about videos. We evaluated the model on the official AVSD test set, where it achieves a relative improvement of more than 16% over the baseline model on BLEU-4 and more than 33% on CIDEr. The challenging aspect of multi-modal dialogue is fusing modalities with varying information density. On AVSD, it is easiest to learn from the input text, while video features remain largely opaque to the decoder. modelname uses a generalization of FiLM to video that conditions video feature extraction on a question. However, similar to related work, absolute improvements of incorporating video features into dialogue are consistent but small. Thus, while our results indicate the suitability of our FiLM generalization, they also highlight that applications at the intersection between language and video are currently constrained by the quality of video features, and emphasizes the need for larger datasets." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0484/instruction.md b/qasper-0484/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..b6bb39ae59297b85f441f370212f7b058f8d69f3 --- /dev/null +++ b/qasper-0484/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Civique: Using Social Media to Detect Urban Emergencies + +Question: Is the web interface publicly accessible? \ No newline at end of file diff --git a/qasper-0502/instruction.md b/qasper-0502/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..ec416319d13f493fec96baba2cab39ad1d8dd40b --- /dev/null +++ b/qasper-0502/instruction.md @@ -0,0 +1,82 @@ +Name of Paper: Synchronising audio and ultrasound by learning cross-modal embeddings + +Question: Does their neural network predict a single offset in a recording? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Background", + "Audiovisual synchronisation for lip videos", + "Lip videos vs. ultrasound tongue imaging (UTI)", + "Model", + "Data", + "Preparing the data", + "Creating samples using a self-supervision strategy", + "Dividing samples for training, validation and testing", + "Experiments", + "Conclusion", + "Acknowledgements" + ], + "paragraphs": [ + [ + "Ultrasound tongue imaging (UTI) is a non-invasive way of observing the vocal tract during speech production BIBREF0 . Instrumental speech therapy relies on capturing ultrasound videos of the patient's tongue simultaneously with their speech audio in order to provide a diagnosis, design treatments, and measure therapy progress BIBREF1 . The two modalities must be correctly synchronised, with a minimum shift of INLINEFORM0 45ms if the audio leads and INLINEFORM1 125ms if the audio lags, based on synchronisation standards for broadcast audiovisual signals BIBREF2 . Errors beyond this range can render the data unusable \u2013 indeed, synchronisation errors do occur, resulting in significant wasted effort if not corrected. No mechanism currently exists to automatically correct these errors, and although manual synchronisation is possible in the presence of certain audiovisual cues such as stop consonants BIBREF3 , it is time consuming and tedious.", + "In this work, we exploit the correlation between the two modalities to synchronise them. We utilise a two-stream neural network architecture for the task BIBREF4 , using as our only source of supervision pairs of ultrasound and audio segments which have been automatically generated and labelled as positive (correctly synchronised) or negative (randomly desynchronised); a process known as self-supervision BIBREF5 . We demonstrate how this approach enables us to correctly synchronise the majority of utterances in our test set, and in particular, those exhibiting natural variation in speech.", + "Section SECREF2 reviews existing approaches for audiovisual synchronisation, and describes the challenges specifically associated with UTI data, compared with lip videos for which automatic synchronisation has been previously attempted. Section SECREF3 describes our approach. Section SECREF4 describes the data we use, including data preprocessing and positive and negative sample creation using a self-supervision strategy. Section SECREF5 describes our experiments, followed by an analysis of the results. We conclude with a summary and future directions in Section SECREF6 ." + ], + [ + "Ultrasound and audio are recorded using separate components, and hardware synchronisation is achieved by translating information from the visual signal into audio at recording time. Specifically, for every ultrasound frame recorded, the ultrasound beam-forming unit releases a pulse signal, which is translated by an external hardware synchroniser into an audio pulse signal and captured by the sound card BIBREF6 , BIBREF7 . Synchronisation is achieved by aligning the ultrasound frames with the audio pulse signal, which is already time-aligned with the speech audio BIBREF8 .", + "Hardware synchronisation can fail for a number of reasons. The synchroniser is an external device which needs to be correctly connected and operated by therapists. Incorrect use can lead to missing the pulse signal, which would cause synchronisation to fail for entire therapy sessions BIBREF9 . Furthermore, low-quality sound cards report an approximate, rather than the exact, sample rate which leads to errors in the offset calculation BIBREF8 . There is currently no recovery mechanism for when synchronisation fails, and to the best of our knowledge, there has been no prior work on automatically correcting the synchronisation error between ultrasound tongue videos and audio. There is, however, some prior work on synchronising lip movement with audio which we describe next." + ], + [ + "Speech audio is generated by articulatory movement and is therefore fundamentally correlated with other manifestations of this movement, such as lip or tongue videos BIBREF10 . An alternative to the hardware approach is to exploit this correlation to find the offset. Previous approaches have investigated the effects of using different representations and feature extraction techniques on finding dimensions of high correlation BIBREF11 , BIBREF12 , BIBREF13 . More recently, neural networks, which learn features directly from input, have been employed for the task. SyncNet BIBREF4 uses a two-stream neural network and self-supervision to learn cross-modal embeddings, which are then used to synchronise audio with lip videos. It achieves near perfect accuracy ( INLINEFORM0 99 INLINEFORM1 ) using manual evaluation where lip-sync error is not detectable to a human. It has since been extended to use different sample creation methods for self-supervision BIBREF5 , BIBREF14 and different training objectives BIBREF14 . We adopt the original approach BIBREF4 , as it is both simpler and significantly less expensive to train than the more recent variants." + ], + [ + "Videos of lip movement can be obtained from various sources including TV, films, and YouTube, and are often cropped to include only the lips BIBREF4 . UTI data, on the other hand, is recorded in clinics by trained therapists BIBREF15 . An ultrasound probe placed under the chin of the patient captures the midsaggital view of their oral cavity as they speak. UTI data consists of sequences of 2D matrices of raw ultrasound reflection data, which can be interpreted as greyscale images BIBREF15 . There are several challenges specifically associated with UTI data compared with lip videos, which can potentially lower the performance of models relative to results reported on lip video data. These include:", + "Poor image quality: Ultrasound data is noisy, containing arbitrary high-contrast edges, speckle noise, artefacts, and interruptions to the tongue's surface BIBREF0 , BIBREF16 , BIBREF17 . The oral cavity is not entirely visible, missing the lips, the palate, and the pharyngeal wall, and visually interpreting the data requires specialised training. In contrast, videos of lip movement are of much higher quality and suffer from none of these issues.", + "Probe placement variation: Surfaces that are orthogonal to the ultrasound beam image better than those at an angle. Small shifts in probe placement during recording lead to high variation between otherwise similar tongue shapes BIBREF0 , BIBREF18 , BIBREF17 . In contrast, while the scaling and rotations of lip videos lead to variation, they do not lead to a degradation in image quality.", + "Inter-speaker variation: Age and physiology affect the quality of ultrasound data, and subjects with smaller vocal tracts and less tissue fat image better BIBREF0 , BIBREF17 . Dryness in the mouth, as a result of nervousness during speech therapy, leads to poor imaging. While inter-speaker variation is expected in lip videos, again, the variation does not lead to quality degradation.", + "Limited amount of data: Existing UTI datasets are considerably smaller than lip movement datasets. Consider for example VoxCeleb and VoxCeleb2 used to train SyncNet BIBREF4 , BIBREF14 , which together contain 1 million utterances from 7,363 identities BIBREF19 , BIBREF20 . In contrast, the UltraSuite repository (used in this work) contains 13,815 spoken utterances from 86 identities.", + "Uncorrelated segments: Speech therapy data contains interactions between the therapist and patient. The audio therefore contains speech from both speakers, while the ultrasound captures only the patient's tongue BIBREF15 . As a result, parts of the recordings will consist of completely uncorrelated audio and ultrasound. This issue is similar to that of dubbed voices in lip videos BIBREF4 , but is more prevalent in speech therapy data." + ], + [ + "We adopt the approach in BIBREF4 , modifying it to synchronise audio with UTI data. Our model, UltraSync, consists of two streams: the first takes as input a short segment of ultrasound and the second takes as input the corresponding audio. Both inputs are high-dimensional and are of different sizes. The objective is to learn a mapping from the inputs to a pair of low-dimensional vectors of the same length, such that the Euclidean distance between the two vectors is small when they correlate and large otherwise BIBREF21 , BIBREF22 . This model can be viewed as an extension of a siamese neural network BIBREF23 but with two asymmetrical streams and no shared parameters. Figure FIGREF1 illustrates the main architecture. The visual data INLINEFORM0 (ultrasound) and audio data INLINEFORM1 (MFCC), which have different shapes, are mapped to low dimensional embeddings INLINEFORM2 (visual) and INLINEFORM3 (audio) of the same size: DISPLAYFORM0 ", + "The model is trained using a contrastive loss function BIBREF21 , BIBREF22 , INLINEFORM0 , which minimises the Euclidean distance INLINEFORM1 between INLINEFORM2 and INLINEFORM3 for positive pairs ( INLINEFORM4 ), and maximises it for negative pairs ( INLINEFORM5 ), for a number of training samples INLINEFORM6 : DISPLAYFORM0 ", + "Given a pair of ultrasound and audio segments we can calculate the distance between them using our model. To predict the synchronisation offset for an utterance, we consider a discretised set of candidate offsets, calculate the average distance for each across utterance segments, and select the one with the minimum average distance. The candidate set is independent of the model, and is chosen based on task knowledge (Section SECREF5 )." + ], + [ + "For our experiments, we select a dataset whose utterances have been correctly synchronised at recording time. This allows us to control how the model is trained and verify its performance using ground truth synchronisation offsets. We use UltraSuite: a repository of ultrasound and acoustic data gathered from child speech therapy sessions BIBREF15 . We used all three datasets from the repository: UXTD (recorded with typically developing children), and UXSSD and UPX (recorded with children with speech sound disorders). In total, the dataset contains 13,815 spoken utterances from 86 speakers, corresponding to 35.9 hours of recordings. The utterances have been categorised by the type of task the child was given, and are labelled as: Words (A), Non-words (B), Sentence (C), Articulatory (D), Non-speech (E), or Conversations (F). See BIBREF15 for details.", + "Each utterance consists of 3 files: audio, ultrasound, and parameter. The audio file is a RIFF wave file, sampled at 22.05 KHz, containing the speech of the child and therapist. The ultrasound file consists of a sequence of ultrasound frames capturing the midsagittal view of the child's tongue. A single ultrasound frame is recorded as a 2D matrix where each column represents the ultrasound reflection intensities along a single scan line. Each ultrasound frame consists of 63 scan lines of 412 data points each, and is sampled at a rate of INLINEFORM0 121.5 fps. Raw ultrasound frames can be visualised as greyscale images and can thus be interpreted as videos. The parameter file contains the synchronisation offset value (in milliseconds), determined using hardware synchronisation at recording time and confirmed by the therapists to be correct for this dataset." + ], + [ + "First, we exclude utterances of type \u201cNon-speech\" (E) from our training data (and statistics). These are coughs recorded to obtain additional tongue shapes, or swallowing motions recorded to capture a trace of the hard palate. Both of these rarely contain audible content and are therefore not relevant to our task. Next, we apply the offset, which should be positive if the audio leads and negative if the audio lags. In this dataset, the offset is always positive. We apply it by cropping the leading audio and trimming the end of the longer signal to match the duration.", + "To process the ultrasound more efficiently, we first reduce the frame rate from INLINEFORM0 121.5 fps to INLINEFORM1 24.3 fps by retaining 1 out of every 5 frames. We then downsample by a factor of (1, 3), shrinking the frame size from 63x412 to 63x138 using max pixel value. This retains the number of ultrasound vectors (63), but reduces the number of pixels per vector (from 412 to 138).", + "The final pre-preprocessing step is to remove empty regions. UltraSuite was previously anonymised by zero-ing segments of audio which contained personally identifiable information. As a preprocessing step, we remove the zero regions from audio and corresponding ultrasound. We additionally experimented with removing regions of silence using voice activity detection, but obtained a higher performance by retaining them." + ], + [ + "To train our model we need positive and negative training pairs. The model ingests short clips from each modality of INLINEFORM0 200ms long, calculated as INLINEFORM1 , where INLINEFORM2 is the time window, INLINEFORM3 is the number of ultrasound frames per window (5 in our case), and INLINEFORM4 is the ultrasound frame rate of the utterance ( INLINEFORM5 24.3 fps). For each recording, we split the ultrasound into non-overlapping windows of 5 frames each. We extract MFCC features (13 cepstral coefficients) from the audio using a window length of INLINEFORM6 20ms, calculated as INLINEFORM7 , and a step size of INLINEFORM8 10ms, calculated as INLINEFORM9 . This give us the input sizes shown in Figure FIGREF1 .", + "Positive samples are pairs of ultrasound windows and the corresponding MFCC frames. To create negative samples, we randomise pairings of ultrasound windows to MFCC frames within the same utterance, generating as many negative as positive samples to achieve a balanced dataset. We obtain 243,764 samples for UXTD (13.5hrs), 333,526 for UXSSD (18.5hrs), and 572,078 for UPX (31.8 hrs), or a total 1,149,368 samples (63.9hrs) which we divide into training, validation and test sets." + ], + [ + "We aim to test whether our model generalises to data from new speakers, and to data from new sessions recorded with known speakers. To simulate this, we select a group of speakers from each dataset, and hold out all of their data either for validation or for testing. Additionally, we hold out one entire session from each of the remaining speakers, and use the rest of their data for training. We aim to reserve approximately 80% of the created samples for training, 10% for validation, and 10% for testing, and select speakers and sessions on this basis.", + "Each speaker in UXTD recorded 1 session, but sessions are of different durations. We reserve 45 speakers for training, 5 for validation, and 8 for testing. UXSSD and UPX contain fewer speakers, but each recorded multiple sessions. We hold out 1 speaker for validation and 1 for testing from each of the two datasets. We also hold out a session from the first half of the remaining speakers for validation, and a session from the second half of the remaining speakers for testing. This selection process results in 909,858 (pooled) samples for training (50.5hrs), 128,414 for validation (7.1hrs) and 111,096 for testing (6.2hrs). From the training set, we create shuffled batches which are balanced in the number of positive and negative samples." + ], + [ + "We select the hyper-parameters of our model empirically by tuning on the validation set (Table ). Hyper-parameter exploration is guided by BIBREF24 . We train our model using the Adam optimiser BIBREF25 with a learning rate of 0.001, a batch size of 64 samples, and for 20 epochs. We implement learning rate scheduling which reduces the learning rate by a factor of 0.1 when the validation loss plateaus for 2 epochs.", + "Upon convergence, the model achieves 0.193 training loss, 0.215 validation loss, and 0.213 test loss. By placing a threshold of 0.5 on predicted distances, the model achieves 69.9% binary classification accuracy on training samples, 64.7% on validation samples, and 65.3% on test samples.", + "Synchronisation offset prediction: Section SECREF3 described briefly how to use our model to predict the synchronisation offset for test utterances. To obtain a discretised set of offset candidates, we retrieve the true offsets of the training utterances, and find that they fall in the range [0, 179] ms. We discretise this range taking 45ms steps and rendering 40 candidate values (45ms is the smaller of the absolute values of the detectability boundaries, INLINEFORM0 125 and INLINEFORM1 45 ms). We bin the true offsets in the candidate set and discard empty bins, reducing the set from 40 to 24 values. We consider all 24 candidates for each test utterance. We do this by aligning the two signals according to the given candidate, then producing the non-overlapping windows of ultrasound and MFCC pairs, as we did when preparing the data. We then use our model to predict the Euclidean distance for each pair, and average the distances. Finally, we select the offset with the smallest average distance as our prediction.", + "Evaluation: Because the true offsets are known, we evaluate the performance of our model by computing the discrepancy between the predicted and the true offset for each utterance. If the discrepancy falls within the minimum detectability range ( INLINEFORM0 125 INLINEFORM1 INLINEFORM2 INLINEFORM3 INLINEFORM4 45) then the prediction is correct. Random prediction (averaged over 1000 runs) yields 14.6% accuracy with a mean and standard deviation discrepancy of 328 INLINEFORM5 518ms. We achieve 82.9% accuracy with a mean and standard deviation discrepancy of 32 INLINEFORM6 223ms. SyncNet reports INLINEFORM7 99% accuracy on lip video synchronisation using a manual evaluation where the lip error is not detectable to a human observer BIBREF4 . However, we argue that our data is more challenging (Section SECREF4 ).", + "Analysis: We analyse the performance of our model across different conditions. Table shows the model accuracy broken down by utterance type. The model achieves 91.2% accuracy on utterances containing words, sentences, and conversations, all of which exhibit natural variation in speech. The model is less successful with Articulatory utterances, which contain isolated phones occurring once or repeated (e.g., \u201csh sh sh\"). Such utterances contain subtle tongue movement, making it more challenging to correlate the visual signal with the audio. And indeed, the model finds the correct offset for only 55.9% of Articulatory utterances. A further analysis shows that 84.4% (N INLINEFORM0 90) of stop consonants (e.g., \u201ct\u201d), which are relied upon by therapists as the most salient audiovisual synchronisation cues BIBREF3 , are correctly synchronised by our model, compared to 48.6% (N INLINEFORM1 140) of vowels, which contain less distinct movement and are also more challenging for therapists to synchronise.", + "Table shows accuracy broken down by test set. The model performs better on test sets containing entirely new speakers compared with test sets containing new sessions from previously seen speakers. This is contrary to expectation but could be due to the UTI challenges (described in Section SECREF4 ) affecting different subsets to different degrees. Table shows that the model performs considerably worse on UXTD compared to other test sets (64.8% accuracy). However, a further breakdown of the results in Table by test set and utterance type explains this poor performance; the majority of UXTD utterances (71%) are Articulatory utterances which the model struggles to correctly synchronise. In fact, for other utterance types (where there is a large enough sample, such as Words) performance on UXTD is on par with other test sets." + ], + [ + "We have shown how a two-stream neural network originally designed to synchronise lip videos with audio can be used to synchronise UTI data with audio. Our model exploits the correlation between the modalities to learn cross-model embeddings which are used to find the synchronisation offset. It generalises well to held-out data, allowing us to correctly synchronise the majority of test utterances. The model is best-suited to utterances which contain natural variation in speech and least suited to those containing isolated phones, with the exception of stop consonants. Future directions include integrating the model and synchronisation offset prediction process into speech therapy software BIBREF6 , BIBREF7 , and using the learned embeddings for other tasks such as active speaker detection BIBREF4 ." + ], + [ + "Supported by EPSRC Healthcare Partnerships Programme grant number EP/P02338X/1 (Ultrax2020)." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0505/instruction.md b/qasper-0505/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..f54b48a514a6aeb43d9a686d514117731290a89f --- /dev/null +++ b/qasper-0505/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Generalisation in Named Entity Recognition: A Quantitative Analysis + +Question: What web and user-generated NER datasets are used for the analysis? \ No newline at end of file diff --git a/qasper-0513/instruction.md b/qasper-0513/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..4d6a92e8202532307a4672804bdf773f62d77835 --- /dev/null +++ b/qasper-0513/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping + +Question: What languages do they experiment on? \ No newline at end of file diff --git a/qasper-0533/instruction.md b/qasper-0533/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..83d8cfeae9d81827ac8a98b4a4686036e53f1003 --- /dev/null +++ b/qasper-0533/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Deja-vu: Double Feature Presentation and Iterated Loss in Deep Transformer Networks + +Question: Do they normalize the calculated intermediate output hypotheses to compensate for the incompleteness? \ No newline at end of file diff --git a/qasper-0534/instruction.md b/qasper-0534/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..908636cee66ae689bb839e64b0a4ce7111dd6648 --- /dev/null +++ b/qasper-0534/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Deja-vu: Double Feature Presentation and Iterated Loss in Deep Transformer Networks + +Question: How many layers do they use in their best performing network? \ No newline at end of file diff --git a/qasper-0535/instruction.md b/qasper-0535/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..387b696bb151cde13904c5648dc53bfdaa805b51 --- /dev/null +++ b/qasper-0535/instruction.md @@ -0,0 +1,85 @@ +Name of Paper: Deja-vu: Double Feature Presentation and Iterated Loss in Deep Transformer Networks + +Question: Do they just sum up all the loses the calculate to end up with one single loss? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Transformer Modules", + "Iterated Feature Presentation", + "Iterated Feature Presentation ::: Feature Re-Presentation", + "Iterated Feature Presentation ::: Iterated Loss", + "Experimental results ::: Dataset", + "Experimental results ::: Target Units", + "Experimental results ::: Deep Transformer Acoustic Model", + "Experimental results ::: Results", + "Related Work", + "Conclusion" + ], + "paragraphs": [ + [ + "In this paper, we propose the processing of features not only in the input layer of a deep network, but in the intermediate layers as well. We are motivated by a desire to enable a neural network acoustic model to adaptively process the features depending on partial hypotheses and noise conditions. Many previous methods for adaptation have operated by linearly transforming either input features or intermediate layers in a two pass process where the transform is learned to maximize the likelihood of some adaptation data BIBREF0, BIBREF1, BIBREF2. Other methods have involved characterizing the input via factor analysis or i-vectors BIBREF3, BIBREF4. Here, we suggest an alternative approach in which adaptation can be achieved by re-presenting the feature stream at an intermediate layer of the network that is constructed to be correlated with the ultimate graphemic or phonetic output of the system.", + "We present this work in the context of Transformer networks BIBREF5. Transformers have become a popular deep learning architecture for modeling sequential datasets, showing improvements in many tasks such as machine translation BIBREF5, language modeling BIBREF6 and autoregressive image generation BIBREF7. In the speech recognition field, Transformers have been proposed to replace recurrent neural network (RNN) architectures such as LSTMs and GRUs BIBREF8. A recent survey of Transformers in many speech related applications may be found in BIBREF9. Compared to RNNs, Transformers have several advantages, specifically an ability to aggregate information across all the time-steps by using a self-attention mechanism. Unlike RNNs, the hidden representations do not need to be computed sequentially across time, thus enabling significant efficiency improvements via parallelization.", + "In the context of Transformer module, secondary feature analysis is enabled through an additional mid-network transformer module that has access both to previous-layer activations and the raw features. To implement this model, we apply the objective function several times at the intermediate layers, to encourage the development of phonetically relevant hypotheses. Interestingly, we find that the iterated use of an auxiliary loss in the intermediate layers significantly improves performance by itself, as well as enabling the secondary feature analysis.", + "This paper makes two main contributions:", + "We present improvements in the basic training process of deep transformer networks, specifically the iterated use of CTC or CE in intermediate layers, and", + "We show that an intermediate-layer attention model with access to both previous-layer activations and raw feature inputs can significantly improve performance.", + "We evaluate our proposed model on Librispeech and a large-scale video dataset. From our experimental results, we observe 10-20% relative improvement on Librispeech and 3.2-11% on the video dataset." + ], + [ + "A transformer network BIBREF5 is a powerful approach to learning and modeling sequential data. A transformer network is itself constructed with a series of transformer modules that each perform some processing. Each module has a self-attention mechanism and several feed-forward layers, enabling easy parallelization over time-steps compared to recurrent models such as RNNs or LSTMs BIBREF10. We use the architecture defined in BIBREF5, and provide only a brief summary below.", + "Assume we have an input sequence that is of length $S$: $X = [x_1,...,x_S]$. Each $x_i$ is itself a vector of activations. A transformer layer encodes $X$ into a corresponding output representation $Z = [z_1,...,z_S]$ as described below.", + "Transformers are built around the notion of a self-attention mechanism that is used to extract the relevant information for each time-step $s$ from all time-steps $[1..S]$ in the preceding layer. Self attention is defined in terms of a Query, Key, Value triplet $\\lbrace {Q}, {K}, {V}\\rbrace \\in \\mathbb {R}^{S \\times d_k}$. In self-attention, the queries, keys and values are the columns of the input itself, $[x_1,...,x_S]$. The output activations are computed as:", + "Transformer modules deploy a multi-headed version of self-attention. As described in BIBREF5, this is done by linearly projecting the queries, keys and values $P$ times with different, learned linear projections. Self-attention is then applied to each of these projected versions of Queries, Keys and Values. These are concatenated and once again projected, resulting in the final values. We refer to the input projection matrices as $W_p^{Q}, W_p^{K}, W_p^{V}$, and to the output projection as $W_O$. Multihead attention is implemented as", + "Here, $ W_p^Q, W_p^K, W_p^V \\in \\mathbb {R}^{d_{k} \\times d_m}$, $d_m = d_{k} / P$, and $W_O \\in \\mathbb {R}^{Pd_m \\times d_k}$.", + "After self-attention, a transformer module applies a series of linear layer, RELU, layer-norm and dropout operations, as well as the application of residual connections. The full sequence of processing is illustrated in Figure FIGREF3." + ], + [ + "In this section, we present our proposal for allowing the network to (re)-consider the input features in the light of intermediate processing. We do this by again deploying a self-attention mechanism to combine the information present in the original features with the information available in the activations of an intermediate layer. As described earlier, we calculate the output posteriors and auxiliary loss at the intermediate layer as well. The overall architecture is illustrated in Figure FIGREF6. Here, we have used a 24 layer network, with feature re-presentation after the 12th layer.", + "In the following subsections, we provide detail on the feature re-presentation mechanism, and iterated loss calculation." + ], + [ + "We process the features in the intermediate later by concatenating a projection of the original features with a projection of previous hidden layer activations, and then applying self-attention.", + "First, we project both the the input and intermediate layer features $(Z_0 \\in \\mathbb {R}^{S \\times d_0}, Z_{k} \\in \\mathbb {R}^{S \\times d_{k}} )$, apply layer normalization and concatenate with position encoding:", + "where $d_0$ is the input feature dimension, $d_k$ is the Transformer output dimension, $W_1 \\in \\mathbb {R}^{d_0 \\times d_c}, W_2 \\in \\mathbb {R}^{d_{k} \\times d_c}$ and $E \\in \\mathbb {R}^{S \\times d_{e}}$ is a sinusoidal position encoding BIBREF5.", + "After we project both information sources to the same dimensionality, we merge the information by using time-axis concatenation:", + "Then, we extract relevant features with extra Transformer layer and followed by linear projection and ReLU:", + "where $W_3 \\in \\mathbb {R}^{d_{k+1}^{^{\\prime }} \\times d_{k+1}}$ is a linear projection. All biases in the formula above are omitted for simplicity.", + "Note that in doing time-axis concatenation, our Key and Value sequences are twice as long as the original input. In the standard self-attention where the Query is the same as the Key and Value, the output preserves the sequence length. Therefore, in order to maintain the necessary sequence length $S$, we select either the first half (split A) or the second half (split B) to represent the combined information. The difference between these two is that the use of split A uses the projected input features as the Query set, while split B uses the projected higher level activations as the Query. In initial experiments, we found that the use of high-level features (split B) as queries is preferable. We illustrates this operation on Figure FIGREF11.", + "Another way of combining information from the features with an intermediate layer is to concatenate the two along with the feature rather than the time axis. However, in initial experiments, we found that time axis concatenation produces better results, and focus on that in the experimental results." + ], + [ + "We have found it beneficial to apply the loss function at several intermediate layers of the network. Suppose there are $M$ total layers, and define a subset of these layers at which to apply the loss function: $K = \\lbrace k_1, k_2, ..., k_L\\rbrace \\subseteq \\lbrace 1,..,M-1\\rbrace $. The total objective function is then defined as", + "where $Z_{k_l}$ is the $k_l$-th Transformer layer activations, $Y$ is the ground-truth transcription for CTC and context dependent states for hybrid ASR, and $Loss(P, Y)$ can be defined as CTC objective BIBREF11 or cross entropy for hybrid ASR. The coefficient $\\lambda $ scales the auxiliary loss and we set $\\lambda = 0.3$ based on our preliminary experiments. We illustrate the auxiliary prediction and loss in Figure FIGREF6." + ], + [ + "We evaluate our proposed module on both the Librispeech BIBREF12 dataset and a large-scale English video dataset. In the Librispeech training set, there are three splits, containing 100 and 360 hours sets of clean speech and 500 hours of other speech. We combined everything, resulting in 960 hours of training data. For the development set, there are also two splits: dev-clean and dev-other. For the test set, there is an analogous split.", + "The video dataset is a collection of public and anonymized English videos. It consists of a 1000 hour training set, a 9 hour dev set, and a $46.1$ hour test set. The test set comprises an $8.5$ hour curated set of carefully selected very clean videos, a 19 hour clean set and a $18.6$ hour noisy set BIBREF13. For the hybrid ASR experiments on video dataset, alignments were generated with a production system trained with 14k hours.", + "All speech features are extracted by using log Mel-filterbanks with 80 dimensions, a 25 ms window size and a 10 ms time step between two windows. Then we apply mean and variance normalization." + ], + [ + "For CTC training, we use word-pieces as our target. During training, the reference is tokenized to 5000 sub-word units using sentencepiece with a uni-gram language model BIBREF14. Neural networks are thus used to produce a posterior distribution for 5001 symbols (5000 sub-word units plus blank symbol) every frame. For decoding, each sub-word is modeled by a HMM with two states where the last states share the same blank symbol probability; the best sub-word segmentation of each word is used to form a lexicon; these HMMs, lexicon are then combined with the standard $n$-gram via FST BIBREF15 to form a static decoding graph. Kaldi decoderBIBREF16 is used to produce the best hypothesis.", + "We further present results with hybrid ASR systems. In this, we use the same HMM topology, GMM bootstrapping and decision tree building procedure as BIBREF13. Specifically, we use context-dependent (CD) graphemes as modeling units. On top of alignments from a GMM model, we build a decision tree to cluster CD graphemes. This results in 7248 context dependent units for Librispeech, and 6560 units for the video dataset. Training then proceeds with the CE loss function. We also apply SpecAugment BIBREF17 online during training, using the LD policy without time warping. For decoding, a standard Kaldi's WFST decoder BIBREF16 is used." + ], + [ + "All neural networks are implemented with the in-house extension of the fairseq BIBREF18 toolkit. Our speech features are produced by processing the log Mel-spectrogram with two VGG BIBREF19 layers that have the following configurations: (1) two 2-D convolutions with 32 output filters, kernel=3, stride=1, ReLU activation, and max-pooling kernel=2, (2) two 2-D convolutions with 64 output filters, kernel=3, stride=1 and max-pooling kernel=2 for CTC or max-pooling kernel=1 for hybrid. After the VGG layers, the total number of frames are subsampled by (i) 4x for CTC, or (ii) 2x for hybrid, thus enabling us to reduce the run-time and memory usage significantly. After VGG processing, we use 24 Transformer layers with $d_k=512$ head dimensions (8 heads, each head has 64 dimensions), 2048 feedforward hidden dimensions (total parameters $\\pm $ 80 millions), and dropout $0.15$. For the proposed models, we utilized an auxiliary MLP with two linear layers with 256 hidden units, LeakyReLU activation and softmax (see Sec. SECREF3). We set our position encoding dimensions $d_e=256$ and pre-concatenation projection $d_c=768$ for the feature re-presentation layer. The loss function is either CTC loss or hybrid CE loss." + ], + [ + "Table TABREF19 presents CTC based results for the Librispeech dataset, without data augmentation. Our baseline is a 24 layer Transformer network trained with CTC. For the proposed method, we varied the number and placement of iterated loss and the feature re-presentation. The next three results show the effect of using CTC multiple times. We see 12 and 8% relative improvements for test-clean and test-other. Adding feature re-presentation gives a further boost, with net 20 and 18% relative improvements over the baseline.", + "Table TABREF20 shows results for Librispeech with SpecAugment. We test both CTC and CE/hybrid systems. There are consistent gains first from iterated loss, and then from multiple feature presentation. We also run additional CTC experiments with 36 layers Transformer (total parameters $\\pm $120 millions). The baseline with 36 layers has the same performance with 24 layers, but by adding the proposed methods, the 36 layer performance improved to give the best results. This shows that our proposed methods can improve even very deep models.", + "As shown in Table TABREF21, the proposed methods also provide large performance improvements on the curated video set, up to 13% with CTC, and up to 9% with the hybrid model. We also observe moderate gains of between 3.2 and 8% relative on the clean and noisy video sets." + ], + [ + "In recent years, Transformer models have become an active research topic in speech processing. The key features of Transformer networks is self-attention, which produces comparable or better performance to LSTMs when used for encoder-decoder based ASR BIBREF23, as well as when trained with CTC BIBREF9. Speech-Transformers BIBREF24 also produce comparable performance to the LSTM-based attention model, but with higher training speed in a single GPU. Abdelrahman et al.BIBREF8 integrates a convolution layer to capture audio context and reduces WER in Librispeech.", + "The use of an objective function in intermediate layers has been found useful in several previous works such as image classification BIBREF25 and language modeling BIBREF26. In BIBREF27, the authors did pre-training with an RNN-T based model by using a hierarchical CTC criterion with different target units. In this paper, we don't need additional types of target unit, instead we just use same tokenization and targets for both intermediate and final losses.", + "The application of the objective function to intermediate layers is also similar in spirit to the use of KL-divergence in BIBREF28, which estimates output posteriors at an intermediate layer and regularizes them towards the distributions at the final layer. In contrast to this approach, the direct application of the objective function does not require the network to have a good output distribution before the new gradient contribution is meaningful." + ], + [ + "In this paper, we have proposed a method for re-processing the input features in light of the information available at an intermediate network layer. We do this in the context of deep transformer networks, via a self-attention mechanism on both features and hidden states representation. To encourage meaningful partial results, we calculate the objective function at intermediate layers of the network as well as the output layer. This improves performance in and of itself, and when combined with feature re-presentation we observe consistent relative improvements of 10 - 20% for Librispeech and 3.2 - 13% for videos." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0550/instruction.md b/qasper-0550/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..90e1345ce7de4c9026f84e6fb9f86fe52812d30c --- /dev/null +++ b/qasper-0550/instruction.md @@ -0,0 +1,150 @@ +Name of Paper: Frozen Binomials on the Web: Word Ordering and Language Conventions in Online Text + +Question: How is order of binomials tracked across time? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Introduction ::: Related Work", + "Data", + "Dimensions of Binomials", + "Dimensions of Binomials ::: Definitions", + "Dimensions of Binomials ::: Dimensions", + "Models And Predictions", + "Models And Predictions ::: Stability of Asymmetry", + "Models And Predictions ::: Prediction Results", + "Proper Nouns and the Proximity Principle", + "Proper Nouns and the Proximity Principle ::: NBA Names", + "Proper Nouns and the Proximity Principle ::: Subreddit and team names", + "Proper Nouns and the Proximity Principle ::: Political Names", + "Formal Text", + "Formal Text ::: Wine", + "Formal Text ::: News", + "Global Structure", + "Multinomials", + "Discussion", + "Acknowledgements" + ], + "paragraphs": [ + [ + "Lists are extremely common in text and speech, and the ordering of items in a list can often reveal information. For instance, orderings can denote relative importance, such as on a to-do list, or signal status, as is the case for author lists of scholarly publications. In other cases, orderings might come from cultural or historical conventions. For example, `red, white, and blue' is a specific ordering of colors that is recognizable to those familiar with American culture.", + "The orderings of lists in text and speech is a subject that has been repeatedly touched upon for more than a century. By far the most frequently studied aspect of list ordering is the binomial, a list of two words usually separated by a conjunction such as `and' or `or', which is the focus of our paper. The academic treatment of binomial orderings dates back more than a century to Jespersen BIBREF0, who proposed in 1905 that the ordering of many common English binomials could be predicted by the rhythm of the words. In the case of a binomial consisting of a monosyllable and a disyllable, the prediction was that the monosyllable would appear first followed by the conjunction `and'. The idea was that this would give a much more standard and familiar syllable stress to the overall phrase, e.g., the binomial `bread and butter' would have the preferable rhythm compared to `butter and bread.'", + "This type of analysis is meaningful when the two words in the binomial nearly always appear in the same ordering. Binomials like this that appear in strictly one order (perhaps within the confines of some text corpus), are commonly termed frozen binomials BIBREF1, BIBREF2. Examples of frozen binomials include `salt and pepper' and `pros and cons', and explanations for their ordering in English and other languages have become increasingly complex. Early work focused almost exclusively on common frozen binomials, often drawn from everyday speech. More recent work has expanded this view to include nearly frozen binomials, binomials from large data sets such as books, and binomials of particular types such as food, names, and descriptors BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8. Additionally, explanations have increasingly focused on meaning rather than just sound, implying value systems inherent to the speaker or the culture of the language's speakers (one such example is that men are usually listed before women in English BIBREF9). The fact that purely phonetic explanations have been insufficient suggests that list orderings rely at least partially on semantics, and it has previously been suggested that these semantics could be revealing about the culture in which the speech takes place BIBREF3. Thus, it is possible that understanding these orderings could reveal biases or values held by the speaker.", + "Overall, this prior research has largely been confined to pristine examples, often relying on small samples of lists to form conclusions. Many early studies simply drew a small sample of what the author(s) considered some of the more representative or prominent binomials in whatever language they were studying BIBREF10, BIBREF1, BIBREF11, BIBREF0, BIBREF12, BIBREF13, BIBREF14, BIBREF15, BIBREF3. Other researchers have used books or news articles BIBREF2, BIBREF4, or small samples from the Web (web search results and Google books) BIBREF5. Many of these have lacked a large-scale text corpus and have relied on a focused set of statistics about word orderings.", + "Thus, despite the long history of this line of inquiry, there is an opportunity to extend it significantly by examining a broad range of questions about binomials coming from a large corpus of online text data produced organically by many people. Such an analysis could produce at least two types of benefits. First, such a study could help us learn about cultural phenomena embedded in word orderings and how they vary across communities and over time. Second, such an analysis could become a case study for the extension of theories developed at small scales in this domain to a much larger context.", + "The present work: Binomials in large-scale online text. In this work, we use data from large-scale Internet text corpora to study binomials at a massive scale, drawing on text created by millions of users. Our approach is more wholesale than prior work - we focus on all binomials of sufficient frequency, without first restricting to small samples of binomials that might be frozen. We draw our data from news publications, wine reviews, and Reddit, which in addition to large volume, also let us characterize binomials in new ways, and analyze differences in binomial orderings across communities and over time. Furthermore, the subject matter on Reddit leads to many lists about people and organizations that lets us study orderings of proper names \u2014 a key setting for word ordering which has been difficult to study by other means.", + "We begin our analysis by introducing several new key measures for the study of binomials, including a quantity we call asymmetry that measures how frequently a given binomial appears in some ordering. By looking at the distribution of asymmetries across a wide range of binomials, we find that most binomials are not frozen, barring a few strong exceptions. At the same time, there may still be an ordering preference. For example, `10 and 20' is not a frozen binomial; instead, the binomial ordering `10 and 20' appears 60% of the time and `20 and 10' appears 40% of time.", + "We also address temporal and community structure in collections of binomials. While it has been recognized that the orderings of binomials may change over time or between communities BIBREF5, BIBREF10, BIBREF1, BIBREF13, BIBREF14, BIBREF15, there has been little analysis of this change. We develop new metrics for the agreement of binomial orderings across communities and the movement of binomial orderings over time. Using subreddits as communities, these metrics reveal variations in orderings, some of which suggest cultural change influencing language. For example, in one community, we find that over a period of 10 years, the binomial `son and daughter' went from nearly frozen to appearing in that order only 64% of the time.", + "While these changes do happen, they are generally quite rare. Most binomials \u2014 frozen or not \u2014 are ordered in one way about the same percentage of the time, regardless of community or the year. We develop a null model to determine how much variation in binomial orderings we might expect across communities and across time, if binomial orderings were randomly ordered according to global asymmetry values. We find that there is less variation across time and communities in the data compared to this model, implying that binomial orderings are indeed remarkably stable.", + "Given this stability, one might expect that the dominant ordinality of a given binomial is still predictable, even if the binomial is not frozen. For example, one might expect that the global frequency of a single word or the number of syllables in a word would predict ordering in many cases. However, we find that these simple predictors are quite poor at determining binomial ordering.", + "On the other hand, we find that a notion of `proximity' is robust at predicting ordering in some cases. Here, the idea is that the person producing the text will list the word that is conceptually \u201ccloser\u201d to them first \u2014 a phenomenon related to a \u201cMe First\u201d principle of binomial orderings suggested by Cooper and Ross BIBREF3. One way in which we study this notion of proximity is through sports team subreddits. For example, we find that when two NBA team names form a binomial on a specific team's subreddit, the team that is the subject of the subreddit tends to appear first.", + "The other source of improved predictions comes from using word embeddings BIBREF16: we find that a model based on the positions of words in a standard pre-trained word embedding can be a remarkably reliable predictor of binomial orderings. While not applicable to all words, such as names, this type of model is strongly predictive in most cases.", + "Since binomial orderings are in general difficult to predict individually, we explore a new way of representing the global binomial ordering structure, we form a directed graph where an edge from $i$ to $j$ means that $i$ tends to come before $j$ in binomials. These graphs show tendencies across the English language and also reveal peculiarities in the language of particular communities. For instance, in a graph formed from the binomials in a sports community, the names of sports teams and cities are closely clustered, showing that they are often used together in binomials. Similarly, we identify clusters of names, numbers, and years. The presence of cycles in these graphs are also informative. For example, cycles are rare in graphs formed from proper names in politics, suggesting a possible hierarchy of names, and at the same time very common for other binomials. This suggests that no such hierarchy exists for most of the English language, further complicating attempts to predict binomial order.", + "Finally, we expand our work to include multinomials, which are lists of more than two words. There already appears to be more structure in trinomials (lists of three) compared to binomials. Trinomials are likely to appear in exactly one order, and when they appear in more than one order the last word is almost always the same across all instances. For instance, in one section of our Reddit data, `Fraud, Waste, and Abuse' appears 34 times, and `Waste, Fraud, and Abuse' appears 20 times. This could point to, for example, recency principles being more important in lists of three than in lists of two. While multinomials were in principle part of the scope of past research in this area, they were difficult to study in smaller corpora, suggesting another benefit of working at our current scale." + ], + [ + "Interest in list orderings spans the last century BIBREF10, BIBREF1, with a focus almost exclusively on binomials. This research has primarily investigated frozen binomials, also called irreversible binomials, fixed coordinates, and fixed conjuncts BIBREF11, although some work has also looked at non-coordinate freezes where the individual words are nonsensical by themselves (e.g., `dribs and drabs') BIBREF11. One study has directly addressed mostly frozen binomials BIBREF5, and we expand the scope of this paper by exploring the general question of how frequently binomials appear in a particular order. Early research investigated languages other than English BIBREF1, BIBREF10, but most recent research has worked almost exclusively with English. Overall, this prior research can be separated into three basic categories \u2014 phonological rules, semantic rules, and metadata rules.", + "Phonology. The earliest research on binomial orderings proposed mostly phonological explanations, particularly rhythm BIBREF0, BIBREF12. Another highly supported proposal is Panini's Law, which claims that words with fewer syllables come first BIBREF17; we find only very mild preference for this type of ordering. Cooper and Ross's work expands these to a large list of rules, many overlapping, and suggests that they can compound BIBREF3; a number of subsequent papers have expanded on their work BIBREF11, BIBREF15, BIBREF9, BIBREF17.", + "Semantics. There have also been a number of semantic explanations, mostly in the form of categorical tendencies (such as `desirable before undesirable') that may have cultural differences BIBREF10, BIBREF1. The most influential of these may be the `Me First' principle codified by Cooper and Ross. This suggests that the first word of a binomial tends to follow a hierarchy that favors `here', `now', present generation, adult, male, and positive. Additional hierarchies also include a hierarchy of food, plants vs. animals, etc. BIBREF3.", + "Frequency. More recently, it has been proposed that the more cognitively accessible word might come first, which often means the word the author sees or uses most frequently BIBREF18. There has also been debate on whether frequency may encompass most phonological and semantic rules that have been previously proposed BIBREF13, BIBREF4. We find that frequency is in general a poor predictor of word ordering.", + "Combinations. Given the number of theories, there have also been attempts to give a hierarchy of rules and study their interactions BIBREF4, BIBREF5. This research has complemented the proposals of Cooper and Ross BIBREF3. These types of hierarchies are also presented as explanations for the likelihood of a binomial becoming frozen BIBREF5.", + "Names. Work on the orderings of names has been dominated by a single phenomenon: men's names usually come before women's names. Explanations range from a power differential, to men being more `agentic' within `Me First', to men's names being more common or even exhibiting more of the phonological features of words that usually come first BIBREF8, BIBREF5, BIBREF18, BIBREF3, BIBREF13, BIBREF9, BIBREF19, BIBREF6. However, it has also been demonstrated that this preference may be affected by the author's own gender and relationship with the people named BIBREF6, BIBREF19, as well as context more generally BIBREF20.", + "Orderings on the Web. List orderings have also been explored in other Web data, specifically on the ordering of tags applied to images BIBREF21. There is evidence that these tags are ordered intentionally by users, and that a bias to order tag A before tag B may be influenced by historical precedent in that environment but also by the relative importance of A and B BIBREF21. Further work also demonstrates that exploiting the order of tags on images can improve models that rank those images BIBREF22." + ], + [ + "We take our data mostly from Reddit, a large social media website divided into subcommunities called `subreddits' or `subs'. Each subreddit has a theme (usually clearly expressed in its name), and we have focused our study on subreddits primarily in sports and politics, in part because of the richness of proper names in these domains: r/nba, r/nfl, r/politics, r/Conservative, r/Libertarian, r/The_Donald, r/food, along with a variety of NBA team subreddits (e.g., r/rockets for the Houston Rockets). Apart from the team-specific and food subreddits, these are among the largest and most heavily used subreddits BIBREF23. We gather text data from comments made by users in discussion threads. In all cases, we have data from when the subreddit started until mid-2018. (Data was contributed by Cristian Danescu-Niculescu-Mizil.) Reddit in general, and the subreddits we examined in particular, are rapidly growing, both in terms of number of users and number of comments.", + "Some of the subreddits we looked at (particularly sports subreddits) exhibited very distinctive `seasons', where commenting spikes (Fig. FIGREF2). These align with, e.g., the season of the given sport. When studying data across time, our convention is to bin the data by year, but we adjust the starting point of a year based on these seasons. Specifically, a year starts in May for r/nfl, August for r/nba, and February for all politics subreddits.", + "We use two methods to identify lists from user comments: `All Words' and `Names Only', with the latter focusing on proper names. In both cases, we collect a number of lists and discard lists for any pair of words that appear fewer than 30 times within the time frame that we examined (see Table TABREF3 for summary statistics).", + "The All Words method simply searches for two words $A$ and $B$ separated by `and' or `or', where a word is merely a series of characters separated by a space or punctuation. This process only captures lists of length two, or binomials. We then filter out lists containing words from a collection of stop-words that, by their grammatical role or formatting structure, are almost exclusively involved in false positive lists. No metadata is captured for these lists beyond the month and year of posting.", + "The Names Only method uses a curated list of full names relevant to the subreddit, focusing on sports and politics. For sports, we collected names of all NBA and NFL player active during 1980\u20132019 from basketball-reference.com and pro-football-reference.com. For politics, we collected the names of congresspeople from the @unitedstates project BIBREF24. To form lists, we search for any combination of any part of these names such that at least two partial names are separated by `and', `or', `v.s.', `vs', or `/' and the rest are separated by `,'. While we included a variety of separators, about 83% of lists include only `and', about 17% include `or' and the rest of the separators are negligible. Most lists that we retrieve in this way are of length 2, but we also found lists up to length 40 (Fig. FIGREF5). Finally, we also captured full metadata for these lists, including a timestamp, the user, any flairs attributed to the user (short custom text that appears next to the username), and other information.", + "We additionally used wine reviews and a variety of news paper articles for additional analysis. The wine data gives reviews of wine from WineEnthusiast and is hosted on Kaggle BIBREF25. While not specifically dated, the reviews were scraped between June and November of 2017. There are 20 different reviewers included, but the amount of reviews each has ranges from tens to thousands. The news data consists of news articles pulled from a variety of sources, including (in random order) the New York Times, Breitbart, CNN, the Atlantic, Buzzfeed News, National Review, New York Post, NPR, Reuters, and the Washington Post. The articles are primarily from 2016 and early 2017 with a few from 2015. The articles are scraped from home-page headline and RSS feeds BIBREF26. Metadata was limited for both of these data sets." + ], + [ + "In this paper we introduce a new framework to interpret binomials, based on three properties: asymmetry (how frozen a binomial is), movement (how binomial orderings change over time), and agreement (how consistent binomial orderings are between communities), which we will visualize as a cube with three dimensions. Again, prior work has focused essentially entirely on asymmetry, and we argue that this can only really be understood in the context of the other two dimensions.", + "For this paper we will use the convention {A,B} to refer to an unordered pair of words, and [A,B] to refer to an ordered pair where A comes before B. We say that [A,B] and [B,A] are the two possible orientations of {A,B}." + ], + [ + "Previous work has one main measure of binomials \u2014 their `frozen-ness'. A binomial is `frozen' if it always appears with a particular order. For example, if the pair {`arrow', `bow'} always occurs as [`bow', `arrow'] and never as [`arrow', `bow'], then it is frozen. This leaves open the question of how describe the large number of binomials that are not frozen. To address this point, we instead consider the ordinality of a list, or how often the list is `in order' according to some arbitrary underlying reference order. Unless otherwise specified, the underlying order is assumed to be alphabetical. If the list [`cat', `dog'] appears 40 times and the list [`dog', `cat'] 10 times, then the list {`cat', `dog'} would have an ordinality of 0.8.", + "Let $n_{x,y}$ be the number of times the ordered list $[x,y]$ appears, and let $f_{x,y} = n_{x,y} / (n_{x,y} + n_{y,x})$ be the fraction of times that the unordered version of the list appears in that order. We formalize ordinality as follows. [Ordinality] Given an ordering $<$ on words (by default, we assume alphabetical ordering), the ordinality $o_{x,y}$ of the pair $\\lbrace x,y\\rbrace $ is equal to $f_{x,y}$ if $x < y$ and $f_{y,x}$ otherwise.", + "Similarly, we introduce the concept of asymmetry in the context of binomials, which is how often the word appears in its dominant order. In our framework, a `frozen' list is one with ordinality 0 or 1 and would be considered a high asymmetry list, with asymmetry of 1. A list that appears as [`A', `B'] half of the time and [`B', `A'] half of the time (or with ordinality 0.5) would be considered a low asymmetry list, with asymmetry of 0.", + "[Asymmetry] The asymmetry of an unordered list $\\lbrace x,y\\rbrace $ is $A_{x,y} = 2 \\cdot \\vert o_{x,y} - 0.5 \\vert $.", + "The Reddit data described above gives us access to new dimensions of binomials not previously addressed. We define movement as how the ordinality of a list changes over time [Movement] Let $o_{x,y,t}$ be the ordinality of an unordered list $\\lbrace x,y\\rbrace $ for data in year $t \\in T$. The movement of $\\lbrace x,y\\rbrace $ is $M_{x,y} = \\max _{t \\in T} o_{x,y,t} - \\min _{t \\in T} o_{x,y,t}$. And agreement describes how the ordinality of a list differs between different communities. [Agreement] Let $o_{x,y,c}$ be the ordinality of an unordered list ${x,y}$ for data in community (subreddit) $c \\in C$. The agreement of $\\lbrace x,y\\rbrace $ is $A_{x,y} = 1 - (\\max _{c \\in C} o_{x,y,c} - \\min _{c \\in C} o_{x,y,c})$." + ], + [ + "Let the point $(A,M,G)_{x,y}$ be a vector of the asymmetry, movement, and agreement for some unordered list $\\lbrace x,y\\rbrace $. These vectors then define a 3-dimensional space in which each list occupies a point. Since our measures for asymmetry, agreement, and movement are all defined from 0 to 1, their domains form a unit cube (Fig. FIGREF8). The corners of this cube correspond to points with coordinates are entirely made up of 0s or 1s. By examining points near the corners of this cube, we can get a better understanding of the range of binomials. Some corners are natural \u2014 it is easy to imagine a high asymmetry, low movement, high agreement binomial \u2014 such as {`arrow', `bow'} from earlier. On the other hand, we have found no good examples of a high asymmetry, low movement, low agreement binomial. There are a few unusual examples, such as {10, 20}, which has 0.4 asymmetry, 0.2 movement, and 0.1 agreement and is clearly visible as an isolated point in Fig. FIGREF8.", + "Asymmetry. While a majority of binomials have low asymmetry, almost all previous work has focused exclusively on high-asymmetry binomials. In fact, asymmetry is roughly normally distributed across binomials with an additional increase of highly asymmetric binomials (Fig. FIGREF9). This implies that previous work has overlooked the vast majority of binomials, and an investigation into whether rules proposed for highly asymmetric binomials also functions for other binomials is a core piece of our analysis.", + "Movement. The vast majority of binomials have low movement. However, the exceptions to this can be very informative. Within r/nba a few of these pairs show clear change in linguistics and/or culture. The binomial [`rpm', `vorp'] (a pair of basketball statistics) started at 0.74 ordinality and within three years dropped to 0.32 ordinality, showing a potential change in users' representation of how these statistics relate to each other. In r/politics, [`daughter', `son'] moved from 0.07 ordinality to 0.36 ordinality over ten years. This may represent a cultural shift in how users refer to children, or a shift in topics discussed relating to children. And in r/politics, ['dems', 'obama'] went from 0.75 ordinality to 0.43 ordinality from 2009\u20132018, potentially reflecting changes in Obama's role as a defining feature of the Democratic Party. Meanwhile the ratio of unigram frequency of `dems' to `obama' actually increased from 10% to 20% from 2010 to 2017. Similarly, [`fdr', `lincoln'] moved from 0.49 ordinality to 0.17 ordinality from 2015\u20132018. This is particularly interesting, since in 2016 `fdr' had a unigram frequency 20% higher than `lincoln', but in 2017 they are almost the same. This suggests that movement could be unrelated to unigram frequency changes. Note also that the covariance for movement across subreddits is quite low TABREF10, and movement in one subreddit is not necessarily reflected by movement in another.", + "Agreement. Most binomials have high agreement (Table TABREF11) but again the counterexamples are informative. For instance, [`score', `kick'] has ordinality of 0.921 in r/nba and 0.204 in r/nfl. This likely points to the fact that American football includes field goals. A less obvious example is the list [`ceiling', `floor']. In r/nba and r/nfl, it has ordinality 0.44, and in r/politics, it has ordinality 0.27.", + "There are also differences among proper nouns. One example is [`france', `israel'], which has ordinality 0.6 in r/politics, 0.16 in r/Libertarian, and 0.51 in r/The_Donald (and the list does not appear in r/Conservative). And the list [`romney', `trump'] has ordinality 0.48 in r/poltics, 0.55 in r/The_Donald, and 0.73 in r/Conservative." + ], + [ + "In this section, we establish a null model under which different communities or time slices have the same probability of ordering a binomial in a particular way. With this, we would expect to see variation in binomial asymmetry. We find that our data shows smaller variation than this null model predicts, suggesting that binomial orderings are extremely stable across communities and time. From this, we might also expect that orderings are predictable; but we find that standard predictors in fact have limited success." + ], + [ + "Recall that the asymmetry of binomials with respect to alphabetic order (excluding frozen binomials) is roughly normal centered around $0.5$ (Fig. FIGREF9). One way of seeing this type of distribution would be if binomials are ordered randomly, with $p=0.5$ for each order. In this case, if each instance $l$ of a binomial $\\lbrace x,y\\rbrace $ takes value 0 (non-alphabetical ordering) or 1 (alphabetical ordering), then $l \\sim \\text{Bernoulli}(0.5)$. If $\\lbrace x,y\\rbrace $ appears $n$ times, then the number of instances of value 1 is distributed by $W \\sim \\text{Bin}(n, 0.5)$, and $W / n$ is approximately normally distributed with mean 0.5.", + "One way to test this behavior is to first estimate $p$ for each list within each community. If the differences in these estimates are not normal, then the above model is incorrect. We first omit frozen binomials before any analysis. Let $L$ be a set of unordered lists and $C$ be a set of communities. We estimate $p$ for list $l \\in L$ in community $c \\in C$ by $\\hat{p}_{l,c} = o_{l,c}$, the ordinality of $l$ in $C$. Next, for all $l \\in L$ let $p^*_{l} = \\max _{c \\in C}(\\hat{p}_{l, c}) - \\min _{ c \\in C}(\\hat{p}_{l, c})$. The distribution of $p^*_{l}$ over $l \\in L$ has median 0, mean 0.0145, and standard deviation 0.0344. We can perform a similar analysis over time. Define $Y$ as our set of years, and $\\hat{p}_{l, y} = o_{l,y}$ for $y \\in Y$ our estimates. The distribution of $p^{\\prime }_{l} = \\max _{y \\in Y}(\\hat{p}_{l, y}) - \\min _{y \\in Y}(\\hat{p}_{l, y})$ over $l \\in L$ has median 0.0216, mean 0.0685, and standard deviation 0.0856. The fact that $p$ varies very little across both time and communities suggests that there is some $p_l$ for each $l \\in L$ that is consistent across time and communities, which is not the case in the null model, where these values would be normally distributed.", + "We also used a bootstrapping technique to understand the mean variance in ordinality for lists over communities and years. Specifically, let $o_{l, c, y}$ be the ordinality of list $l$ in community $c$ and year $y$, $O_l$ be the set of $o_{l,c,y}$ for a given list $l$, and $s_l$ be the standard deviation of $O_l$. Finally, let $\\bar{s}$ be the average of the $s_l$. We re-sample data by randomizing the order of each binomial instance, sampling its orderings by a binomial random variable with success probability equal to its ordinality across all seasons and communities ($p_l$). We repeated this process to get samples estimates $\\lbrace \\bar{s}_1, \\ldots , \\bar{s}_{k}\\rbrace $, where $k$ is the size of the set of seasons and communities. These averages range from 0.0277 to 0.0278 and are approximately normally distributed (each is a mean over an approximately normal scaled Binomial random variable). However, $\\bar{s} = 0.0253$ for our non-randomized data. This is significantly smaller than the randomized data and implies that the true variation in $p_l$ across time and communities is even smaller than a binomial distribution would predict. One possible explanation for this is that each instance of $l$ is not actually independent, but is in fact anti-correlated, violating one of the conditions of the binomial distribution. An explanation for that could be that users attempt to draw attention by intentionally going against the typical ordering BIBREF1, but it is an open question what the true model is and why the variation is so low. Regardless, it is clear that the orientation of binomials varies very little across years and communities (Fig. FIGREF13)." + ], + [ + "Given the stability of binomials within our data, we now try to predict their ordering. We consider deterministic or rule-based methods that predict the order for a given binomial. We use two classes of evaluation measures for success on this task: (i) by token \u2014 judging each instance of a binomial separately; and (ii) by type \u2014 judging all instances of a particular binomial together. We further characterize these into weighted and unweighted.", + "To formalize these notions, first consider any unordered list $\\lbrace x,y\\rbrace $ that appears $n_{x,y}$ times in the orientation $[x,y]$ and $n_{y,x}$ times in the orientation $[y,x]$. Since we can only guess one order, we will have either $n_{x,y}$ or $n_{y,x}$ successful guesses for $\\lbrace x,y\\rbrace $ when guessing by token. The unweighted token score (UO) and weighted token score (WO) are the macro and micro averages of this accuracy.", + "If predicting by type, let $S$ be the lists such that the by-token prediction is successful at least half of the time. Then the unweighted type score (UT) and weighted type score (WT) are the macro and micro averages of $S$.", + "Basic Features. We first use predictors based on rules that have previously been proposed in the literature: word length, number of phonemes, number of syllables, alphabetical order, and frequency. We collect all binomials but make predictions only on binomials appearing at least 30 times total, stratified by subreddit. However, none of these features appear to be particularly predictive across the board (Table TABREF15). A simple linear regression model predicts close to random, which bolsters the evidence that these classical rules for frozen binomials are not predictive for general binomials.", + "Perhaps the oldest suggestion to explain binomial orderings is that if there are two words A and B, and A is monosyllabic and B is disyllabic, then A comes before B BIBREF0. Within r/politics, we gathered an estimate of number of syllables for each word as given by a variation on the CMU Pronouncing Dictionary BIBREF27 (Tables TABREF16 and TABREF17). In a weak sense, Jespersen was correct that monosyllabic words come before disyllabic words more often than not; and more generally, shorter words come before longer words more often than not. However, as predictors, these principles are close to random guessing.", + "Paired Predictions. Another measure of predictive power is predicting which of two binomials has higher asymmetry. In this case, we take two binomials with very different asymmetry and try to predict which has higher asymmetry by our measures (we use the top-1000 and bottom-1000 binomials in terms of asymmetry for these tasks). For instance, we may predict that [`red', `turquoise'] is more asymmetric than [`red', `blue'] because the differences in lengths is more extreme. Overall, the basic predictors from the literature are not very successful (Table TABREF18).", + "Word Embeddings. If we turn to more modern approaches to text analysis, one of the most common is word embeddings BIBREF16. Word embeddings assign a vector $x_i$ to each word $i$ in the corpus, such that the relative position of these vectors in space encode information lingustically relevant relationships among the words. Using the Google News word embeddings, via a simple logistic model, we produce a vector $v^*$ and predict the ordering of a binomial on words $i$ and $j$ from $v^* \\cdot (x_i - x_j)$. In this sense, $v^*$ can be thought of as a \u201csweep-line\u201d direction through the space containing the word vectors, such that the ordering along this sweep-line is the predicted ordering of all binomials in the corpus. This yields surprisingly accurate results, with accuracy ranging from 70% to 85% across various subreddits (Table TABREF20), and 80-100% accuracy on frozen binomials. This is by far the best prediction method we tested. It is important to note that not all words in our binomials could be associated with an embedding, so it was necessary to remove binomials containing words such as names or slang. However, retesting our basic features on this data set did not show any improvement, implying that the drastic change in predictive power is not due to the changed data set." + ], + [ + "Proper nouns, and names in particular, have been a focus within the literature on frozen binomials BIBREF8, BIBREF5, BIBREF18, BIBREF3, BIBREF13, BIBREF9, BIBREF6, BIBREF19, BIBREF20, BIBREF28, but these studies have largely concentrated on the effect of gender in ordering BIBREF8, BIBREF5, BIBREF18, BIBREF3, BIBREF13, BIBREF9, BIBREF6, BIBREF19, BIBREF20. With Reddit data, however, we have many conversations about large numbers of celebrities, with significant background information on each. As such, we can investigate proper nouns in three subreddits: r/nba, r/nfl, and r/politics. The names we used are from NBA and NFL players (1970\u20132019) and congresspeople (pre-1800 and 2000\u20132019) respectively. We also investigated names of entities for which users might feel a strong sense of identification, such as a team or political group they support, or a subreddit to which they subscribe. We hypothesized that the group with which the user identifies the most would come first in binomial orderings. Inspired by the `Me First Principle', we call this the Proximity Principle." + ], + [ + "First, we examined names in r/nba. One advantage of using NBA players is that we have detailed statistics for ever player in every year. We tested a number of these statistics, and while all of them predicted statistically significant numbers ($p <$ 1e-6) of binomials, they were still not very predictive in a practical sense (Table TABREF23). The best predictor was actually how often the player's team was mentioned. Interestingly, the unigram frequency (number of times the player's name was mentioned overall) was not a good predictor. It is relevant to these observations that some team subreddits (and thus, presumably, fanbases) are significantly larger than others." + ], + [ + "Additionally, we also investigated lists of names of sports teams and subreddits as proper nouns. In this case we exploit an interesting structure of the r/nba subreddit which is not evident at scale in other subreddits we examined. In addition to r/nba, there exists a number of subreddits that are affiliated with a particular NBA team, with the purpose of allowing discussion between fans of that team. This implies that most users in a team subreddit are fans of that team. We are then able to look for lists of NBA teams by name, city, and abbreviation. We found 2520 instances of the subreddit team coming first, and 1894 instances of the subreddit team coming second. While this is not a particularly strong predictor, correctly predicting 57% of lists, it is one of the strongest we found, and a clear illustration of the Proximity Principle.", + "We can do a similar calculation with subreddit names, by looking between subreddits. While the team subreddits are not large enough for this calculation, many of the other subreddits are. We find that lists of subreddits in r/nba that include `r/nba' often start with `r/nba', and a similar result holds for r/nfl (Table TABREF25).", + "While NBA team subreddits show a fairly strong preference to name themselves first, this preference is slightly less strong among sport subreddits, and even less strong among politics subreddits. One potential factor here is that r/politics is a more general subreddit, while the rest are more specific \u2014 perhaps akin to r/nba and the team subreddits." + ], + [ + "In our case, political names are drawn from every congressperson (and their nicknames) in both houses of the US Congress through the 2018 election. It is worth noting that one of these people is Philadelph Van Trump. It is presumed that most references to `trump' refer to Donald Trump. There may be additional instances of mistaken identities. We restrict the names to only congresspeople that served before 1801 or after 1999, also including `trump'.", + "One might guess that political subreddits refer to politicians of their preferred party first. However, this was not the case, as Republicans are mentioned first only about 43%\u201346% of the time in all subreddits (Table TABREF27). On the other hand, the Proximity Principle does seem to come into play when discussing ideology. For instance, r/politics \u2014 a left-leaning subreddit \u2014 is more likely to say `democrats and republicans' while the other political subreddits in our study \u2014 which are right-leaning \u2014 are more likely to say `republicans and democrats'.", + "Another relevant measure for lists of proper nouns is the ratio of the number of list instances containing a name to the unigram frequency of that name. We restrict our investigation to names that are not also English words, and only names that have a unigram frequency of at least 30. The average ratio is 0.0535, but there is significant variation across names. It is conceivable that this list ratio is revealing about how often people are talked about alone instead of in company." + ], + [ + "While Reddit provides a very large corpus of informal text, McGuire and McGuire make a distinct separation between informal and formal text BIBREF28. As such, we briefly analyze highly stylized wine reviews and news articles from a diverse set of publications. Both data sets follow the same basic principles outlined above." + ], + [ + "Wine reviews are a highly stylized form of text. In this case reviews are often just a few sentences, and they use a specialized vocabulary meant for wine tasting. While one might hypothesize that such stylized text exhibits more frozen binomials, this is not the case (Tab TABREF28). There is some evidence of an additional freezing effect in binomials such as ('aromas', 'flavors') and ('scents', 'flavors') which both are frozen in the wine reviews, but are not frozen on Reddit. However, this does not seem to have a more general effect. Additionally, there are a number of binomials which appear frozen on Reddit, but have low asymmetry in the wine reviews, such as ['lemon', 'lime']." + ], + [ + "We focused our analysis on NYT, Buzzfeed, Reuters, CNN, the Washington Post, NPR, Breitbart, and the Atlantic. Much like in political subreddits, one might expect to see a split between various publications based upon ideology. However, this is not obviously the case. While there are certainly examples of binomials that seem to differ significantly for one publication or for a group of publications (Buzzfeed, in particular, frequently goes against the grain), there does not seem to be a sharp divide. Individual examples are difficult to draw conclusions from, but can suggest trends. (`China', `Russia') is a particularly controversial binomial. While the publications vary quite a bit, only Breitbart has an ordinality of above 0.5. In fact, country pairs are among the most controversial binomials within the publications (e.g. (`iraq', `syria'), (`afghanisatan', `iraq')), while most other highly controversial binomials reflect other political structures, such as (`house', `senate'), (`migrants', 'refugees'), and (`left', `right'). That so many controversial binomials reflect politics could point to subtle political or ideological differences between the publications. Additionally, the close similarity between Breitbart and more mainstream publications could be due to a similar effect we saw with r/The_Donald - mainly large amounts of quoted text." + ], + [ + "We can discover new structure in binomial orderings by taking a more global view. We do this by building directed graphs based on ordinality. In these graphs, nodes are words and an arrow from A to B indicates that there are at least 30 lists containing A and B and that those lists have order [A,B] at least 50% of the time. For our visualizations, the size of the node indicates how many distinct lists the word appears in,and color indicates how many list instances contain the word in total.", + "If we examine the global structure for r/nba, we can pinpoint a number of patterns (Fig. FIGREF31). First, most nodes within the purple circle correspond to names, while most nodes outside of it are not names. The cluster of circles in the lower left are a combination of numbers and years, where dark green corresponds to numbers, purple corresponds to years, and pink corresponds years represented as two-digit numbers (e.g., `96'). On the right, the brown circle contains adjectives, while above the blue circle contains heights (e.g., 6'5\"), and in the two circles in the lower middle, the left contains cities while the right contains team names. The darkest red node in the center of the graph corresponds to `lebron'.", + "Constructing a similar graph for our wines dataset, we can see clusters of words. In Fig FIGREF32, the colors represent clusters as formed through modularity. These clusters are quite distinct. Green nodes mostly refer to the structure or body of a wine, red are adjectives describing taste, teal and purple are fruits, dark green is wine varietals, gold is senses, and light blue is time (e.g. `year', `decade', etc.)", + "We can also consider the graph as we change the threshold of asymmetry for which an edge is included. If the asymmetry is large enough, the graph is acyclic, and we can consider how small the ordinality threshold must be in order to introduce a cycle. These cycles reveal the non-global ordering of binomials. The graph for r/nba begins to show cycles with a threshold asymmetry of 0.97. Three cycles exist at this threshold: [`ball', `catch', `shooter'], [`court', `pass', `set', `athleticism'], and [`court', `plays', `set', `athleticism'].", + "Restricting the nodes to be names is also revealing. Acyclic graphs in this context suggest a global partial hierarchy of individuals. For r/nba, the graph is no longer acyclic at an asymmetry threshold of 0.76, with the cycle [`blake', `jordan', `bryant', `kobe']. Similarly, the graph for r/nfl (only including names) is acyclic until the threshold reaches 0.73 with cycles [`tannehill', `miller', `jj watt', `aaron rodgers', `brady'], and [`hoyer', `savage', `watson', `hopkins', `miller', `jj watt', `aaron rodgers', `brady'].", + "Figure FIGREF33 shows these graphs for the three political subreddits, where the nodes are the 30 most common politician names. The graph visualizations immediately show that these communities view politicians differently. We can also consider cycles in these graphs and find that the graph is completely acyclic when the asymmetry threshold is at least 0.9. Again, this suggests that, at least among frozen binomials, there is in fact a global partial order of names that might signal hierarchy. (Including non-names, though, causes the r/politics graph to never be acyclic for any asymmetry threshold, since the cycle [`furious', `benghazi', `fast'] consists of completely frozen binomials.) We find similar results for r/Conservative and r/Libertarian, which are acyclic with thresholds of 0.58 and 0.66, respectively. Some of these cycles at high asymmetry might be due to English words that are also names (e.g. `law'), but one particularly notable cycle from r/Conservative is [`rubio', `bush', `obama', `trump', `cruz']." + ], + [ + "Binomials are the most studied type of list, but trinomials \u2014 lists of three \u2014 are also common enough in our dataset to analyze. Studying trinomials adds new aspects to the set of questions: for example, while binomials have only two possible orderings, trinomials have six possible orderings. However, very few trinomials show up in all six orderings. In fact, many trinomials show up in exactly one ordering: about 36% of trinomials being completely frozen amongst trinomials appearing at least 30 times in the data. To get a baseline comparison, we found an equal number of the most common binomials, and then subsampled instances of those binomials to equate the number of instances with the trinomials. In this case, only 21% of binomials are frozen. For trinomials that show up in at least two orderings, it is most common for the last word to keep the same position (e.g., [a, b, c] and [b, a, c]). For example, in our data, [`fraud', `waste', `abuse'] appears 34 times, and [`waste', `fraud', `abuse'] appears 20 times. This may partially be explained by many lists that contain words such as `other', `whatever', or `more'; for instance, [`smarter', `better', `more'] and [`better', `smarter', `more'] are the only two orderings we observe for this set of three words.", + "Additionally, each trinomial [a, b, c] contains three binomials within it: [a, b], [b, c], and [a, c]. It is natural to compare orderings of {a, b} in general with orderings of occurrences of {a, b} that lie inside trinomials. We use this comparison to define the compatibility of {a, b}, as follows.", + "Compatibility Let {a, b} be a binomial with dominant ordering [a, b]; that is, [a, b] is at least as frequent as [b, a]. We define the compatibility of {a, b} to be the fraction of instances of {a, b} occurring inside trinomials that have the order [a,b].", + "There are only a few cases where binomials have compatibility less than 0.5, and for most binomials, the asymmetry is remarkably consistent between binomials and trinomials (Fig. FIGREF37). In general, asymmetry is larger than compatibility \u2014 this occurs for 4569 binomials, compared to 3575 where compatibility was greater and 690 where the two values are the same. An extreme example is the binomial {`fairness', `accuracy'}, which has asymmetry 0.77 and compatibility 0.22. It would be natural to consider these questions for tetranomials and longer lists, but these are rarer in our data and correspondingly harder to draw conclusions from." + ], + [ + "Analyzing binomial orderings on a large scale has led to surprising results. Although most binomials are not frozen in the traditional sense, there is little movement in their ordinality across time or communities. A list that appears in the order [A, B] 60% of the time in one subreddit in one year is likely to show up as [A, B] very close to 60% of the time in all subreddits in all years. This suggests that binomial order should be predictable, but there is evidence that this is difficult: the most common theories on frozen binomial ordering were largely ineffective at predicting binomial ordering in general.", + "Given the challenge in predicting orderings, we searched for methods or principles that could yield better performance, and identified two promising approaches. First, models built on standard word embeddings produce predictions of binomial orders that are much more effective than simpler existing theories. Second, we established the Proximity Principle: the proper noun with which a speaker identifies more will tend to come first. This is evidenced when commenters refer to their sports team first, or politicians refer to their party first. Further analysis of the global structure of binomials reveals interesting patterns and a surprising acyclic nature in names. Analysis of longer lists in the form of multinomials suggests that the rules governing their orders may be different.", + "We have also found promising results in some special cases. We expect that more domain-specific studies will offer rich structure.", + "It is a challenge to adapt the long history of work on the question of frozen binomials to the large, messy environment of online text and social media. However, such data sources offer a unique opportunity to re-explore and redefine these questions. It seems that binomial orderings offer new insights into language, culture, and human cognition. Understanding what changes in these highly stable conventions mean \u2014 and whether or not they can be predicted \u2014 is an interesting avenue for future research." + ], + [ + "The authors thank members of the Cornell AI, Policy, and Practice Group, and (alphabetically by first name) Cristian Danescu-Niculescu-Mizil, Ian Lomeli, Justine Zhang, and Kate Donahue for aid in accessing data and their thoughtful insight. This research was supported by NSF Award DMS-1830274, ARO Award W911NF19-1-0057, a Simons Investigator Award, a Vannevar Bush Faculty Fellowship, and ARO MURI." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0551/instruction.md b/qasper-0551/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..e2e03fd08b5e18c4f8cc9c5923df2a06ad5f566b --- /dev/null +++ b/qasper-0551/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Frozen Binomials on the Web: Word Ordering and Language Conventions in Online Text + +Question: What types of various community texts have been investigated for exploring global structure of binomials? \ No newline at end of file diff --git a/qasper-0556/instruction.md b/qasper-0556/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..a92a8e75afb2986f43b64c796984d2297822bc53 --- /dev/null +++ b/qasper-0556/instruction.md @@ -0,0 +1,150 @@ +Name of Paper: Frozen Binomials on the Web: Word Ordering and Language Conventions in Online Text + +Question: What online text resources are used to test binomial lists? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Introduction ::: Related Work", + "Data", + "Dimensions of Binomials", + "Dimensions of Binomials ::: Definitions", + "Dimensions of Binomials ::: Dimensions", + "Models And Predictions", + "Models And Predictions ::: Stability of Asymmetry", + "Models And Predictions ::: Prediction Results", + "Proper Nouns and the Proximity Principle", + "Proper Nouns and the Proximity Principle ::: NBA Names", + "Proper Nouns and the Proximity Principle ::: Subreddit and team names", + "Proper Nouns and the Proximity Principle ::: Political Names", + "Formal Text", + "Formal Text ::: Wine", + "Formal Text ::: News", + "Global Structure", + "Multinomials", + "Discussion", + "Acknowledgements" + ], + "paragraphs": [ + [ + "Lists are extremely common in text and speech, and the ordering of items in a list can often reveal information. For instance, orderings can denote relative importance, such as on a to-do list, or signal status, as is the case for author lists of scholarly publications. In other cases, orderings might come from cultural or historical conventions. For example, `red, white, and blue' is a specific ordering of colors that is recognizable to those familiar with American culture.", + "The orderings of lists in text and speech is a subject that has been repeatedly touched upon for more than a century. By far the most frequently studied aspect of list ordering is the binomial, a list of two words usually separated by a conjunction such as `and' or `or', which is the focus of our paper. The academic treatment of binomial orderings dates back more than a century to Jespersen BIBREF0, who proposed in 1905 that the ordering of many common English binomials could be predicted by the rhythm of the words. In the case of a binomial consisting of a monosyllable and a disyllable, the prediction was that the monosyllable would appear first followed by the conjunction `and'. The idea was that this would give a much more standard and familiar syllable stress to the overall phrase, e.g., the binomial `bread and butter' would have the preferable rhythm compared to `butter and bread.'", + "This type of analysis is meaningful when the two words in the binomial nearly always appear in the same ordering. Binomials like this that appear in strictly one order (perhaps within the confines of some text corpus), are commonly termed frozen binomials BIBREF1, BIBREF2. Examples of frozen binomials include `salt and pepper' and `pros and cons', and explanations for their ordering in English and other languages have become increasingly complex. Early work focused almost exclusively on common frozen binomials, often drawn from everyday speech. More recent work has expanded this view to include nearly frozen binomials, binomials from large data sets such as books, and binomials of particular types such as food, names, and descriptors BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8. Additionally, explanations have increasingly focused on meaning rather than just sound, implying value systems inherent to the speaker or the culture of the language's speakers (one such example is that men are usually listed before women in English BIBREF9). The fact that purely phonetic explanations have been insufficient suggests that list orderings rely at least partially on semantics, and it has previously been suggested that these semantics could be revealing about the culture in which the speech takes place BIBREF3. Thus, it is possible that understanding these orderings could reveal biases or values held by the speaker.", + "Overall, this prior research has largely been confined to pristine examples, often relying on small samples of lists to form conclusions. Many early studies simply drew a small sample of what the author(s) considered some of the more representative or prominent binomials in whatever language they were studying BIBREF10, BIBREF1, BIBREF11, BIBREF0, BIBREF12, BIBREF13, BIBREF14, BIBREF15, BIBREF3. Other researchers have used books or news articles BIBREF2, BIBREF4, or small samples from the Web (web search results and Google books) BIBREF5. Many of these have lacked a large-scale text corpus and have relied on a focused set of statistics about word orderings.", + "Thus, despite the long history of this line of inquiry, there is an opportunity to extend it significantly by examining a broad range of questions about binomials coming from a large corpus of online text data produced organically by many people. Such an analysis could produce at least two types of benefits. First, such a study could help us learn about cultural phenomena embedded in word orderings and how they vary across communities and over time. Second, such an analysis could become a case study for the extension of theories developed at small scales in this domain to a much larger context.", + "The present work: Binomials in large-scale online text. In this work, we use data from large-scale Internet text corpora to study binomials at a massive scale, drawing on text created by millions of users. Our approach is more wholesale than prior work - we focus on all binomials of sufficient frequency, without first restricting to small samples of binomials that might be frozen. We draw our data from news publications, wine reviews, and Reddit, which in addition to large volume, also let us characterize binomials in new ways, and analyze differences in binomial orderings across communities and over time. Furthermore, the subject matter on Reddit leads to many lists about people and organizations that lets us study orderings of proper names \u2014 a key setting for word ordering which has been difficult to study by other means.", + "We begin our analysis by introducing several new key measures for the study of binomials, including a quantity we call asymmetry that measures how frequently a given binomial appears in some ordering. By looking at the distribution of asymmetries across a wide range of binomials, we find that most binomials are not frozen, barring a few strong exceptions. At the same time, there may still be an ordering preference. For example, `10 and 20' is not a frozen binomial; instead, the binomial ordering `10 and 20' appears 60% of the time and `20 and 10' appears 40% of time.", + "We also address temporal and community structure in collections of binomials. While it has been recognized that the orderings of binomials may change over time or between communities BIBREF5, BIBREF10, BIBREF1, BIBREF13, BIBREF14, BIBREF15, there has been little analysis of this change. We develop new metrics for the agreement of binomial orderings across communities and the movement of binomial orderings over time. Using subreddits as communities, these metrics reveal variations in orderings, some of which suggest cultural change influencing language. For example, in one community, we find that over a period of 10 years, the binomial `son and daughter' went from nearly frozen to appearing in that order only 64% of the time.", + "While these changes do happen, they are generally quite rare. Most binomials \u2014 frozen or not \u2014 are ordered in one way about the same percentage of the time, regardless of community or the year. We develop a null model to determine how much variation in binomial orderings we might expect across communities and across time, if binomial orderings were randomly ordered according to global asymmetry values. We find that there is less variation across time and communities in the data compared to this model, implying that binomial orderings are indeed remarkably stable.", + "Given this stability, one might expect that the dominant ordinality of a given binomial is still predictable, even if the binomial is not frozen. For example, one might expect that the global frequency of a single word or the number of syllables in a word would predict ordering in many cases. However, we find that these simple predictors are quite poor at determining binomial ordering.", + "On the other hand, we find that a notion of `proximity' is robust at predicting ordering in some cases. Here, the idea is that the person producing the text will list the word that is conceptually \u201ccloser\u201d to them first \u2014 a phenomenon related to a \u201cMe First\u201d principle of binomial orderings suggested by Cooper and Ross BIBREF3. One way in which we study this notion of proximity is through sports team subreddits. For example, we find that when two NBA team names form a binomial on a specific team's subreddit, the team that is the subject of the subreddit tends to appear first.", + "The other source of improved predictions comes from using word embeddings BIBREF16: we find that a model based on the positions of words in a standard pre-trained word embedding can be a remarkably reliable predictor of binomial orderings. While not applicable to all words, such as names, this type of model is strongly predictive in most cases.", + "Since binomial orderings are in general difficult to predict individually, we explore a new way of representing the global binomial ordering structure, we form a directed graph where an edge from $i$ to $j$ means that $i$ tends to come before $j$ in binomials. These graphs show tendencies across the English language and also reveal peculiarities in the language of particular communities. For instance, in a graph formed from the binomials in a sports community, the names of sports teams and cities are closely clustered, showing that they are often used together in binomials. Similarly, we identify clusters of names, numbers, and years. The presence of cycles in these graphs are also informative. For example, cycles are rare in graphs formed from proper names in politics, suggesting a possible hierarchy of names, and at the same time very common for other binomials. This suggests that no such hierarchy exists for most of the English language, further complicating attempts to predict binomial order.", + "Finally, we expand our work to include multinomials, which are lists of more than two words. There already appears to be more structure in trinomials (lists of three) compared to binomials. Trinomials are likely to appear in exactly one order, and when they appear in more than one order the last word is almost always the same across all instances. For instance, in one section of our Reddit data, `Fraud, Waste, and Abuse' appears 34 times, and `Waste, Fraud, and Abuse' appears 20 times. This could point to, for example, recency principles being more important in lists of three than in lists of two. While multinomials were in principle part of the scope of past research in this area, they were difficult to study in smaller corpora, suggesting another benefit of working at our current scale." + ], + [ + "Interest in list orderings spans the last century BIBREF10, BIBREF1, with a focus almost exclusively on binomials. This research has primarily investigated frozen binomials, also called irreversible binomials, fixed coordinates, and fixed conjuncts BIBREF11, although some work has also looked at non-coordinate freezes where the individual words are nonsensical by themselves (e.g., `dribs and drabs') BIBREF11. One study has directly addressed mostly frozen binomials BIBREF5, and we expand the scope of this paper by exploring the general question of how frequently binomials appear in a particular order. Early research investigated languages other than English BIBREF1, BIBREF10, but most recent research has worked almost exclusively with English. Overall, this prior research can be separated into three basic categories \u2014 phonological rules, semantic rules, and metadata rules.", + "Phonology. The earliest research on binomial orderings proposed mostly phonological explanations, particularly rhythm BIBREF0, BIBREF12. Another highly supported proposal is Panini's Law, which claims that words with fewer syllables come first BIBREF17; we find only very mild preference for this type of ordering. Cooper and Ross's work expands these to a large list of rules, many overlapping, and suggests that they can compound BIBREF3; a number of subsequent papers have expanded on their work BIBREF11, BIBREF15, BIBREF9, BIBREF17.", + "Semantics. There have also been a number of semantic explanations, mostly in the form of categorical tendencies (such as `desirable before undesirable') that may have cultural differences BIBREF10, BIBREF1. The most influential of these may be the `Me First' principle codified by Cooper and Ross. This suggests that the first word of a binomial tends to follow a hierarchy that favors `here', `now', present generation, adult, male, and positive. Additional hierarchies also include a hierarchy of food, plants vs. animals, etc. BIBREF3.", + "Frequency. More recently, it has been proposed that the more cognitively accessible word might come first, which often means the word the author sees or uses most frequently BIBREF18. There has also been debate on whether frequency may encompass most phonological and semantic rules that have been previously proposed BIBREF13, BIBREF4. We find that frequency is in general a poor predictor of word ordering.", + "Combinations. Given the number of theories, there have also been attempts to give a hierarchy of rules and study their interactions BIBREF4, BIBREF5. This research has complemented the proposals of Cooper and Ross BIBREF3. These types of hierarchies are also presented as explanations for the likelihood of a binomial becoming frozen BIBREF5.", + "Names. Work on the orderings of names has been dominated by a single phenomenon: men's names usually come before women's names. Explanations range from a power differential, to men being more `agentic' within `Me First', to men's names being more common or even exhibiting more of the phonological features of words that usually come first BIBREF8, BIBREF5, BIBREF18, BIBREF3, BIBREF13, BIBREF9, BIBREF19, BIBREF6. However, it has also been demonstrated that this preference may be affected by the author's own gender and relationship with the people named BIBREF6, BIBREF19, as well as context more generally BIBREF20.", + "Orderings on the Web. List orderings have also been explored in other Web data, specifically on the ordering of tags applied to images BIBREF21. There is evidence that these tags are ordered intentionally by users, and that a bias to order tag A before tag B may be influenced by historical precedent in that environment but also by the relative importance of A and B BIBREF21. Further work also demonstrates that exploiting the order of tags on images can improve models that rank those images BIBREF22." + ], + [ + "We take our data mostly from Reddit, a large social media website divided into subcommunities called `subreddits' or `subs'. Each subreddit has a theme (usually clearly expressed in its name), and we have focused our study on subreddits primarily in sports and politics, in part because of the richness of proper names in these domains: r/nba, r/nfl, r/politics, r/Conservative, r/Libertarian, r/The_Donald, r/food, along with a variety of NBA team subreddits (e.g., r/rockets for the Houston Rockets). Apart from the team-specific and food subreddits, these are among the largest and most heavily used subreddits BIBREF23. We gather text data from comments made by users in discussion threads. In all cases, we have data from when the subreddit started until mid-2018. (Data was contributed by Cristian Danescu-Niculescu-Mizil.) Reddit in general, and the subreddits we examined in particular, are rapidly growing, both in terms of number of users and number of comments.", + "Some of the subreddits we looked at (particularly sports subreddits) exhibited very distinctive `seasons', where commenting spikes (Fig. FIGREF2). These align with, e.g., the season of the given sport. When studying data across time, our convention is to bin the data by year, but we adjust the starting point of a year based on these seasons. Specifically, a year starts in May for r/nfl, August for r/nba, and February for all politics subreddits.", + "We use two methods to identify lists from user comments: `All Words' and `Names Only', with the latter focusing on proper names. In both cases, we collect a number of lists and discard lists for any pair of words that appear fewer than 30 times within the time frame that we examined (see Table TABREF3 for summary statistics).", + "The All Words method simply searches for two words $A$ and $B$ separated by `and' or `or', where a word is merely a series of characters separated by a space or punctuation. This process only captures lists of length two, or binomials. We then filter out lists containing words from a collection of stop-words that, by their grammatical role or formatting structure, are almost exclusively involved in false positive lists. No metadata is captured for these lists beyond the month and year of posting.", + "The Names Only method uses a curated list of full names relevant to the subreddit, focusing on sports and politics. For sports, we collected names of all NBA and NFL player active during 1980\u20132019 from basketball-reference.com and pro-football-reference.com. For politics, we collected the names of congresspeople from the @unitedstates project BIBREF24. To form lists, we search for any combination of any part of these names such that at least two partial names are separated by `and', `or', `v.s.', `vs', or `/' and the rest are separated by `,'. While we included a variety of separators, about 83% of lists include only `and', about 17% include `or' and the rest of the separators are negligible. Most lists that we retrieve in this way are of length 2, but we also found lists up to length 40 (Fig. FIGREF5). Finally, we also captured full metadata for these lists, including a timestamp, the user, any flairs attributed to the user (short custom text that appears next to the username), and other information.", + "We additionally used wine reviews and a variety of news paper articles for additional analysis. The wine data gives reviews of wine from WineEnthusiast and is hosted on Kaggle BIBREF25. While not specifically dated, the reviews were scraped between June and November of 2017. There are 20 different reviewers included, but the amount of reviews each has ranges from tens to thousands. The news data consists of news articles pulled from a variety of sources, including (in random order) the New York Times, Breitbart, CNN, the Atlantic, Buzzfeed News, National Review, New York Post, NPR, Reuters, and the Washington Post. The articles are primarily from 2016 and early 2017 with a few from 2015. The articles are scraped from home-page headline and RSS feeds BIBREF26. Metadata was limited for both of these data sets." + ], + [ + "In this paper we introduce a new framework to interpret binomials, based on three properties: asymmetry (how frozen a binomial is), movement (how binomial orderings change over time), and agreement (how consistent binomial orderings are between communities), which we will visualize as a cube with three dimensions. Again, prior work has focused essentially entirely on asymmetry, and we argue that this can only really be understood in the context of the other two dimensions.", + "For this paper we will use the convention {A,B} to refer to an unordered pair of words, and [A,B] to refer to an ordered pair where A comes before B. We say that [A,B] and [B,A] are the two possible orientations of {A,B}." + ], + [ + "Previous work has one main measure of binomials \u2014 their `frozen-ness'. A binomial is `frozen' if it always appears with a particular order. For example, if the pair {`arrow', `bow'} always occurs as [`bow', `arrow'] and never as [`arrow', `bow'], then it is frozen. This leaves open the question of how describe the large number of binomials that are not frozen. To address this point, we instead consider the ordinality of a list, or how often the list is `in order' according to some arbitrary underlying reference order. Unless otherwise specified, the underlying order is assumed to be alphabetical. If the list [`cat', `dog'] appears 40 times and the list [`dog', `cat'] 10 times, then the list {`cat', `dog'} would have an ordinality of 0.8.", + "Let $n_{x,y}$ be the number of times the ordered list $[x,y]$ appears, and let $f_{x,y} = n_{x,y} / (n_{x,y} + n_{y,x})$ be the fraction of times that the unordered version of the list appears in that order. We formalize ordinality as follows. [Ordinality] Given an ordering $<$ on words (by default, we assume alphabetical ordering), the ordinality $o_{x,y}$ of the pair $\\lbrace x,y\\rbrace $ is equal to $f_{x,y}$ if $x < y$ and $f_{y,x}$ otherwise.", + "Similarly, we introduce the concept of asymmetry in the context of binomials, which is how often the word appears in its dominant order. In our framework, a `frozen' list is one with ordinality 0 or 1 and would be considered a high asymmetry list, with asymmetry of 1. A list that appears as [`A', `B'] half of the time and [`B', `A'] half of the time (or with ordinality 0.5) would be considered a low asymmetry list, with asymmetry of 0.", + "[Asymmetry] The asymmetry of an unordered list $\\lbrace x,y\\rbrace $ is $A_{x,y} = 2 \\cdot \\vert o_{x,y} - 0.5 \\vert $.", + "The Reddit data described above gives us access to new dimensions of binomials not previously addressed. We define movement as how the ordinality of a list changes over time [Movement] Let $o_{x,y,t}$ be the ordinality of an unordered list $\\lbrace x,y\\rbrace $ for data in year $t \\in T$. The movement of $\\lbrace x,y\\rbrace $ is $M_{x,y} = \\max _{t \\in T} o_{x,y,t} - \\min _{t \\in T} o_{x,y,t}$. And agreement describes how the ordinality of a list differs between different communities. [Agreement] Let $o_{x,y,c}$ be the ordinality of an unordered list ${x,y}$ for data in community (subreddit) $c \\in C$. The agreement of $\\lbrace x,y\\rbrace $ is $A_{x,y} = 1 - (\\max _{c \\in C} o_{x,y,c} - \\min _{c \\in C} o_{x,y,c})$." + ], + [ + "Let the point $(A,M,G)_{x,y}$ be a vector of the asymmetry, movement, and agreement for some unordered list $\\lbrace x,y\\rbrace $. These vectors then define a 3-dimensional space in which each list occupies a point. Since our measures for asymmetry, agreement, and movement are all defined from 0 to 1, their domains form a unit cube (Fig. FIGREF8). The corners of this cube correspond to points with coordinates are entirely made up of 0s or 1s. By examining points near the corners of this cube, we can get a better understanding of the range of binomials. Some corners are natural \u2014 it is easy to imagine a high asymmetry, low movement, high agreement binomial \u2014 such as {`arrow', `bow'} from earlier. On the other hand, we have found no good examples of a high asymmetry, low movement, low agreement binomial. There are a few unusual examples, such as {10, 20}, which has 0.4 asymmetry, 0.2 movement, and 0.1 agreement and is clearly visible as an isolated point in Fig. FIGREF8.", + "Asymmetry. While a majority of binomials have low asymmetry, almost all previous work has focused exclusively on high-asymmetry binomials. In fact, asymmetry is roughly normally distributed across binomials with an additional increase of highly asymmetric binomials (Fig. FIGREF9). This implies that previous work has overlooked the vast majority of binomials, and an investigation into whether rules proposed for highly asymmetric binomials also functions for other binomials is a core piece of our analysis.", + "Movement. The vast majority of binomials have low movement. However, the exceptions to this can be very informative. Within r/nba a few of these pairs show clear change in linguistics and/or culture. The binomial [`rpm', `vorp'] (a pair of basketball statistics) started at 0.74 ordinality and within three years dropped to 0.32 ordinality, showing a potential change in users' representation of how these statistics relate to each other. In r/politics, [`daughter', `son'] moved from 0.07 ordinality to 0.36 ordinality over ten years. This may represent a cultural shift in how users refer to children, or a shift in topics discussed relating to children. And in r/politics, ['dems', 'obama'] went from 0.75 ordinality to 0.43 ordinality from 2009\u20132018, potentially reflecting changes in Obama's role as a defining feature of the Democratic Party. Meanwhile the ratio of unigram frequency of `dems' to `obama' actually increased from 10% to 20% from 2010 to 2017. Similarly, [`fdr', `lincoln'] moved from 0.49 ordinality to 0.17 ordinality from 2015\u20132018. This is particularly interesting, since in 2016 `fdr' had a unigram frequency 20% higher than `lincoln', but in 2017 they are almost the same. This suggests that movement could be unrelated to unigram frequency changes. Note also that the covariance for movement across subreddits is quite low TABREF10, and movement in one subreddit is not necessarily reflected by movement in another.", + "Agreement. Most binomials have high agreement (Table TABREF11) but again the counterexamples are informative. For instance, [`score', `kick'] has ordinality of 0.921 in r/nba and 0.204 in r/nfl. This likely points to the fact that American football includes field goals. A less obvious example is the list [`ceiling', `floor']. In r/nba and r/nfl, it has ordinality 0.44, and in r/politics, it has ordinality 0.27.", + "There are also differences among proper nouns. One example is [`france', `israel'], which has ordinality 0.6 in r/politics, 0.16 in r/Libertarian, and 0.51 in r/The_Donald (and the list does not appear in r/Conservative). And the list [`romney', `trump'] has ordinality 0.48 in r/poltics, 0.55 in r/The_Donald, and 0.73 in r/Conservative." + ], + [ + "In this section, we establish a null model under which different communities or time slices have the same probability of ordering a binomial in a particular way. With this, we would expect to see variation in binomial asymmetry. We find that our data shows smaller variation than this null model predicts, suggesting that binomial orderings are extremely stable across communities and time. From this, we might also expect that orderings are predictable; but we find that standard predictors in fact have limited success." + ], + [ + "Recall that the asymmetry of binomials with respect to alphabetic order (excluding frozen binomials) is roughly normal centered around $0.5$ (Fig. FIGREF9). One way of seeing this type of distribution would be if binomials are ordered randomly, with $p=0.5$ for each order. In this case, if each instance $l$ of a binomial $\\lbrace x,y\\rbrace $ takes value 0 (non-alphabetical ordering) or 1 (alphabetical ordering), then $l \\sim \\text{Bernoulli}(0.5)$. If $\\lbrace x,y\\rbrace $ appears $n$ times, then the number of instances of value 1 is distributed by $W \\sim \\text{Bin}(n, 0.5)$, and $W / n$ is approximately normally distributed with mean 0.5.", + "One way to test this behavior is to first estimate $p$ for each list within each community. If the differences in these estimates are not normal, then the above model is incorrect. We first omit frozen binomials before any analysis. Let $L$ be a set of unordered lists and $C$ be a set of communities. We estimate $p$ for list $l \\in L$ in community $c \\in C$ by $\\hat{p}_{l,c} = o_{l,c}$, the ordinality of $l$ in $C$. Next, for all $l \\in L$ let $p^*_{l} = \\max _{c \\in C}(\\hat{p}_{l, c}) - \\min _{ c \\in C}(\\hat{p}_{l, c})$. The distribution of $p^*_{l}$ over $l \\in L$ has median 0, mean 0.0145, and standard deviation 0.0344. We can perform a similar analysis over time. Define $Y$ as our set of years, and $\\hat{p}_{l, y} = o_{l,y}$ for $y \\in Y$ our estimates. The distribution of $p^{\\prime }_{l} = \\max _{y \\in Y}(\\hat{p}_{l, y}) - \\min _{y \\in Y}(\\hat{p}_{l, y})$ over $l \\in L$ has median 0.0216, mean 0.0685, and standard deviation 0.0856. The fact that $p$ varies very little across both time and communities suggests that there is some $p_l$ for each $l \\in L$ that is consistent across time and communities, which is not the case in the null model, where these values would be normally distributed.", + "We also used a bootstrapping technique to understand the mean variance in ordinality for lists over communities and years. Specifically, let $o_{l, c, y}$ be the ordinality of list $l$ in community $c$ and year $y$, $O_l$ be the set of $o_{l,c,y}$ for a given list $l$, and $s_l$ be the standard deviation of $O_l$. Finally, let $\\bar{s}$ be the average of the $s_l$. We re-sample data by randomizing the order of each binomial instance, sampling its orderings by a binomial random variable with success probability equal to its ordinality across all seasons and communities ($p_l$). We repeated this process to get samples estimates $\\lbrace \\bar{s}_1, \\ldots , \\bar{s}_{k}\\rbrace $, where $k$ is the size of the set of seasons and communities. These averages range from 0.0277 to 0.0278 and are approximately normally distributed (each is a mean over an approximately normal scaled Binomial random variable). However, $\\bar{s} = 0.0253$ for our non-randomized data. This is significantly smaller than the randomized data and implies that the true variation in $p_l$ across time and communities is even smaller than a binomial distribution would predict. One possible explanation for this is that each instance of $l$ is not actually independent, but is in fact anti-correlated, violating one of the conditions of the binomial distribution. An explanation for that could be that users attempt to draw attention by intentionally going against the typical ordering BIBREF1, but it is an open question what the true model is and why the variation is so low. Regardless, it is clear that the orientation of binomials varies very little across years and communities (Fig. FIGREF13)." + ], + [ + "Given the stability of binomials within our data, we now try to predict their ordering. We consider deterministic or rule-based methods that predict the order for a given binomial. We use two classes of evaluation measures for success on this task: (i) by token \u2014 judging each instance of a binomial separately; and (ii) by type \u2014 judging all instances of a particular binomial together. We further characterize these into weighted and unweighted.", + "To formalize these notions, first consider any unordered list $\\lbrace x,y\\rbrace $ that appears $n_{x,y}$ times in the orientation $[x,y]$ and $n_{y,x}$ times in the orientation $[y,x]$. Since we can only guess one order, we will have either $n_{x,y}$ or $n_{y,x}$ successful guesses for $\\lbrace x,y\\rbrace $ when guessing by token. The unweighted token score (UO) and weighted token score (WO) are the macro and micro averages of this accuracy.", + "If predicting by type, let $S$ be the lists such that the by-token prediction is successful at least half of the time. Then the unweighted type score (UT) and weighted type score (WT) are the macro and micro averages of $S$.", + "Basic Features. We first use predictors based on rules that have previously been proposed in the literature: word length, number of phonemes, number of syllables, alphabetical order, and frequency. We collect all binomials but make predictions only on binomials appearing at least 30 times total, stratified by subreddit. However, none of these features appear to be particularly predictive across the board (Table TABREF15). A simple linear regression model predicts close to random, which bolsters the evidence that these classical rules for frozen binomials are not predictive for general binomials.", + "Perhaps the oldest suggestion to explain binomial orderings is that if there are two words A and B, and A is monosyllabic and B is disyllabic, then A comes before B BIBREF0. Within r/politics, we gathered an estimate of number of syllables for each word as given by a variation on the CMU Pronouncing Dictionary BIBREF27 (Tables TABREF16 and TABREF17). In a weak sense, Jespersen was correct that monosyllabic words come before disyllabic words more often than not; and more generally, shorter words come before longer words more often than not. However, as predictors, these principles are close to random guessing.", + "Paired Predictions. Another measure of predictive power is predicting which of two binomials has higher asymmetry. In this case, we take two binomials with very different asymmetry and try to predict which has higher asymmetry by our measures (we use the top-1000 and bottom-1000 binomials in terms of asymmetry for these tasks). For instance, we may predict that [`red', `turquoise'] is more asymmetric than [`red', `blue'] because the differences in lengths is more extreme. Overall, the basic predictors from the literature are not very successful (Table TABREF18).", + "Word Embeddings. If we turn to more modern approaches to text analysis, one of the most common is word embeddings BIBREF16. Word embeddings assign a vector $x_i$ to each word $i$ in the corpus, such that the relative position of these vectors in space encode information lingustically relevant relationships among the words. Using the Google News word embeddings, via a simple logistic model, we produce a vector $v^*$ and predict the ordering of a binomial on words $i$ and $j$ from $v^* \\cdot (x_i - x_j)$. In this sense, $v^*$ can be thought of as a \u201csweep-line\u201d direction through the space containing the word vectors, such that the ordering along this sweep-line is the predicted ordering of all binomials in the corpus. This yields surprisingly accurate results, with accuracy ranging from 70% to 85% across various subreddits (Table TABREF20), and 80-100% accuracy on frozen binomials. This is by far the best prediction method we tested. It is important to note that not all words in our binomials could be associated with an embedding, so it was necessary to remove binomials containing words such as names or slang. However, retesting our basic features on this data set did not show any improvement, implying that the drastic change in predictive power is not due to the changed data set." + ], + [ + "Proper nouns, and names in particular, have been a focus within the literature on frozen binomials BIBREF8, BIBREF5, BIBREF18, BIBREF3, BIBREF13, BIBREF9, BIBREF6, BIBREF19, BIBREF20, BIBREF28, but these studies have largely concentrated on the effect of gender in ordering BIBREF8, BIBREF5, BIBREF18, BIBREF3, BIBREF13, BIBREF9, BIBREF6, BIBREF19, BIBREF20. With Reddit data, however, we have many conversations about large numbers of celebrities, with significant background information on each. As such, we can investigate proper nouns in three subreddits: r/nba, r/nfl, and r/politics. The names we used are from NBA and NFL players (1970\u20132019) and congresspeople (pre-1800 and 2000\u20132019) respectively. We also investigated names of entities for which users might feel a strong sense of identification, such as a team or political group they support, or a subreddit to which they subscribe. We hypothesized that the group with which the user identifies the most would come first in binomial orderings. Inspired by the `Me First Principle', we call this the Proximity Principle." + ], + [ + "First, we examined names in r/nba. One advantage of using NBA players is that we have detailed statistics for ever player in every year. We tested a number of these statistics, and while all of them predicted statistically significant numbers ($p <$ 1e-6) of binomials, they were still not very predictive in a practical sense (Table TABREF23). The best predictor was actually how often the player's team was mentioned. Interestingly, the unigram frequency (number of times the player's name was mentioned overall) was not a good predictor. It is relevant to these observations that some team subreddits (and thus, presumably, fanbases) are significantly larger than others." + ], + [ + "Additionally, we also investigated lists of names of sports teams and subreddits as proper nouns. In this case we exploit an interesting structure of the r/nba subreddit which is not evident at scale in other subreddits we examined. In addition to r/nba, there exists a number of subreddits that are affiliated with a particular NBA team, with the purpose of allowing discussion between fans of that team. This implies that most users in a team subreddit are fans of that team. We are then able to look for lists of NBA teams by name, city, and abbreviation. We found 2520 instances of the subreddit team coming first, and 1894 instances of the subreddit team coming second. While this is not a particularly strong predictor, correctly predicting 57% of lists, it is one of the strongest we found, and a clear illustration of the Proximity Principle.", + "We can do a similar calculation with subreddit names, by looking between subreddits. While the team subreddits are not large enough for this calculation, many of the other subreddits are. We find that lists of subreddits in r/nba that include `r/nba' often start with `r/nba', and a similar result holds for r/nfl (Table TABREF25).", + "While NBA team subreddits show a fairly strong preference to name themselves first, this preference is slightly less strong among sport subreddits, and even less strong among politics subreddits. One potential factor here is that r/politics is a more general subreddit, while the rest are more specific \u2014 perhaps akin to r/nba and the team subreddits." + ], + [ + "In our case, political names are drawn from every congressperson (and their nicknames) in both houses of the US Congress through the 2018 election. It is worth noting that one of these people is Philadelph Van Trump. It is presumed that most references to `trump' refer to Donald Trump. There may be additional instances of mistaken identities. We restrict the names to only congresspeople that served before 1801 or after 1999, also including `trump'.", + "One might guess that political subreddits refer to politicians of their preferred party first. However, this was not the case, as Republicans are mentioned first only about 43%\u201346% of the time in all subreddits (Table TABREF27). On the other hand, the Proximity Principle does seem to come into play when discussing ideology. For instance, r/politics \u2014 a left-leaning subreddit \u2014 is more likely to say `democrats and republicans' while the other political subreddits in our study \u2014 which are right-leaning \u2014 are more likely to say `republicans and democrats'.", + "Another relevant measure for lists of proper nouns is the ratio of the number of list instances containing a name to the unigram frequency of that name. We restrict our investigation to names that are not also English words, and only names that have a unigram frequency of at least 30. The average ratio is 0.0535, but there is significant variation across names. It is conceivable that this list ratio is revealing about how often people are talked about alone instead of in company." + ], + [ + "While Reddit provides a very large corpus of informal text, McGuire and McGuire make a distinct separation between informal and formal text BIBREF28. As such, we briefly analyze highly stylized wine reviews and news articles from a diverse set of publications. Both data sets follow the same basic principles outlined above." + ], + [ + "Wine reviews are a highly stylized form of text. In this case reviews are often just a few sentences, and they use a specialized vocabulary meant for wine tasting. While one might hypothesize that such stylized text exhibits more frozen binomials, this is not the case (Tab TABREF28). There is some evidence of an additional freezing effect in binomials such as ('aromas', 'flavors') and ('scents', 'flavors') which both are frozen in the wine reviews, but are not frozen on Reddit. However, this does not seem to have a more general effect. Additionally, there are a number of binomials which appear frozen on Reddit, but have low asymmetry in the wine reviews, such as ['lemon', 'lime']." + ], + [ + "We focused our analysis on NYT, Buzzfeed, Reuters, CNN, the Washington Post, NPR, Breitbart, and the Atlantic. Much like in political subreddits, one might expect to see a split between various publications based upon ideology. However, this is not obviously the case. While there are certainly examples of binomials that seem to differ significantly for one publication or for a group of publications (Buzzfeed, in particular, frequently goes against the grain), there does not seem to be a sharp divide. Individual examples are difficult to draw conclusions from, but can suggest trends. (`China', `Russia') is a particularly controversial binomial. While the publications vary quite a bit, only Breitbart has an ordinality of above 0.5. In fact, country pairs are among the most controversial binomials within the publications (e.g. (`iraq', `syria'), (`afghanisatan', `iraq')), while most other highly controversial binomials reflect other political structures, such as (`house', `senate'), (`migrants', 'refugees'), and (`left', `right'). That so many controversial binomials reflect politics could point to subtle political or ideological differences between the publications. Additionally, the close similarity between Breitbart and more mainstream publications could be due to a similar effect we saw with r/The_Donald - mainly large amounts of quoted text." + ], + [ + "We can discover new structure in binomial orderings by taking a more global view. We do this by building directed graphs based on ordinality. In these graphs, nodes are words and an arrow from A to B indicates that there are at least 30 lists containing A and B and that those lists have order [A,B] at least 50% of the time. For our visualizations, the size of the node indicates how many distinct lists the word appears in,and color indicates how many list instances contain the word in total.", + "If we examine the global structure for r/nba, we can pinpoint a number of patterns (Fig. FIGREF31). First, most nodes within the purple circle correspond to names, while most nodes outside of it are not names. The cluster of circles in the lower left are a combination of numbers and years, where dark green corresponds to numbers, purple corresponds to years, and pink corresponds years represented as two-digit numbers (e.g., `96'). On the right, the brown circle contains adjectives, while above the blue circle contains heights (e.g., 6'5\"), and in the two circles in the lower middle, the left contains cities while the right contains team names. The darkest red node in the center of the graph corresponds to `lebron'.", + "Constructing a similar graph for our wines dataset, we can see clusters of words. In Fig FIGREF32, the colors represent clusters as formed through modularity. These clusters are quite distinct. Green nodes mostly refer to the structure or body of a wine, red are adjectives describing taste, teal and purple are fruits, dark green is wine varietals, gold is senses, and light blue is time (e.g. `year', `decade', etc.)", + "We can also consider the graph as we change the threshold of asymmetry for which an edge is included. If the asymmetry is large enough, the graph is acyclic, and we can consider how small the ordinality threshold must be in order to introduce a cycle. These cycles reveal the non-global ordering of binomials. The graph for r/nba begins to show cycles with a threshold asymmetry of 0.97. Three cycles exist at this threshold: [`ball', `catch', `shooter'], [`court', `pass', `set', `athleticism'], and [`court', `plays', `set', `athleticism'].", + "Restricting the nodes to be names is also revealing. Acyclic graphs in this context suggest a global partial hierarchy of individuals. For r/nba, the graph is no longer acyclic at an asymmetry threshold of 0.76, with the cycle [`blake', `jordan', `bryant', `kobe']. Similarly, the graph for r/nfl (only including names) is acyclic until the threshold reaches 0.73 with cycles [`tannehill', `miller', `jj watt', `aaron rodgers', `brady'], and [`hoyer', `savage', `watson', `hopkins', `miller', `jj watt', `aaron rodgers', `brady'].", + "Figure FIGREF33 shows these graphs for the three political subreddits, where the nodes are the 30 most common politician names. The graph visualizations immediately show that these communities view politicians differently. We can also consider cycles in these graphs and find that the graph is completely acyclic when the asymmetry threshold is at least 0.9. Again, this suggests that, at least among frozen binomials, there is in fact a global partial order of names that might signal hierarchy. (Including non-names, though, causes the r/politics graph to never be acyclic for any asymmetry threshold, since the cycle [`furious', `benghazi', `fast'] consists of completely frozen binomials.) We find similar results for r/Conservative and r/Libertarian, which are acyclic with thresholds of 0.58 and 0.66, respectively. Some of these cycles at high asymmetry might be due to English words that are also names (e.g. `law'), but one particularly notable cycle from r/Conservative is [`rubio', `bush', `obama', `trump', `cruz']." + ], + [ + "Binomials are the most studied type of list, but trinomials \u2014 lists of three \u2014 are also common enough in our dataset to analyze. Studying trinomials adds new aspects to the set of questions: for example, while binomials have only two possible orderings, trinomials have six possible orderings. However, very few trinomials show up in all six orderings. In fact, many trinomials show up in exactly one ordering: about 36% of trinomials being completely frozen amongst trinomials appearing at least 30 times in the data. To get a baseline comparison, we found an equal number of the most common binomials, and then subsampled instances of those binomials to equate the number of instances with the trinomials. In this case, only 21% of binomials are frozen. For trinomials that show up in at least two orderings, it is most common for the last word to keep the same position (e.g., [a, b, c] and [b, a, c]). For example, in our data, [`fraud', `waste', `abuse'] appears 34 times, and [`waste', `fraud', `abuse'] appears 20 times. This may partially be explained by many lists that contain words such as `other', `whatever', or `more'; for instance, [`smarter', `better', `more'] and [`better', `smarter', `more'] are the only two orderings we observe for this set of three words.", + "Additionally, each trinomial [a, b, c] contains three binomials within it: [a, b], [b, c], and [a, c]. It is natural to compare orderings of {a, b} in general with orderings of occurrences of {a, b} that lie inside trinomials. We use this comparison to define the compatibility of {a, b}, as follows.", + "Compatibility Let {a, b} be a binomial with dominant ordering [a, b]; that is, [a, b] is at least as frequent as [b, a]. We define the compatibility of {a, b} to be the fraction of instances of {a, b} occurring inside trinomials that have the order [a,b].", + "There are only a few cases where binomials have compatibility less than 0.5, and for most binomials, the asymmetry is remarkably consistent between binomials and trinomials (Fig. FIGREF37). In general, asymmetry is larger than compatibility \u2014 this occurs for 4569 binomials, compared to 3575 where compatibility was greater and 690 where the two values are the same. An extreme example is the binomial {`fairness', `accuracy'}, which has asymmetry 0.77 and compatibility 0.22. It would be natural to consider these questions for tetranomials and longer lists, but these are rarer in our data and correspondingly harder to draw conclusions from." + ], + [ + "Analyzing binomial orderings on a large scale has led to surprising results. Although most binomials are not frozen in the traditional sense, there is little movement in their ordinality across time or communities. A list that appears in the order [A, B] 60% of the time in one subreddit in one year is likely to show up as [A, B] very close to 60% of the time in all subreddits in all years. This suggests that binomial order should be predictable, but there is evidence that this is difficult: the most common theories on frozen binomial ordering were largely ineffective at predicting binomial ordering in general.", + "Given the challenge in predicting orderings, we searched for methods or principles that could yield better performance, and identified two promising approaches. First, models built on standard word embeddings produce predictions of binomial orders that are much more effective than simpler existing theories. Second, we established the Proximity Principle: the proper noun with which a speaker identifies more will tend to come first. This is evidenced when commenters refer to their sports team first, or politicians refer to their party first. Further analysis of the global structure of binomials reveals interesting patterns and a surprising acyclic nature in names. Analysis of longer lists in the form of multinomials suggests that the rules governing their orders may be different.", + "We have also found promising results in some special cases. We expect that more domain-specific studies will offer rich structure.", + "It is a challenge to adapt the long history of work on the question of frozen binomials to the large, messy environment of online text and social media. However, such data sources offer a unique opportunity to re-explore and redefine these questions. It seems that binomial orderings offer new insights into language, culture, and human cognition. Understanding what changes in these highly stable conventions mean \u2014 and whether or not they can be predicted \u2014 is an interesting avenue for future research." + ], + [ + "The authors thank members of the Cornell AI, Policy, and Practice Group, and (alphabetically by first name) Cristian Danescu-Niculescu-Mizil, Ian Lomeli, Justine Zhang, and Kate Donahue for aid in accessing data and their thoughtful insight. This research was supported by NSF Award DMS-1830274, ARO Award W911NF19-1-0057, a Simons Investigator Award, a Vannevar Bush Faculty Fellowship, and ARO MURI." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0557/instruction.md b/qasper-0557/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..4227e36a07754c20df59ef3728835e1bd98390f2 --- /dev/null +++ b/qasper-0557/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Casting Light on Invisible Cities: Computationally Engaging with Literary Criticism + +Question: How do they model a city description using embeddings? \ No newline at end of file diff --git a/qasper-0559/instruction.md b/qasper-0559/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..4189b986f7216c90e5af2d6e3ddac1f265ecbc37 --- /dev/null +++ b/qasper-0559/instruction.md @@ -0,0 +1,64 @@ +Name of Paper: Casting Light on Invisible Cities: Computationally Engaging with Literary Criticism + +Question: Which clustering method do they use to cluster city description embeddings? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Literary analyses of Invisible Cities", + "A Computational Analysis", + "Embedding city descriptions", + "Clustering city representations", + "Evaluating clustering assignments", + "Quantitative comparison", + "Examining the learned clusters", + "Related work", + "Conclusion", + "Acknowledgement" + ], + "paragraphs": [ + [ + "Literary critics form interpretations of meaning in works of literature. Building computational models that can help form and test these interpretations is a fundamental goal of digital humanities research BIBREF0 . Within natural language processing, most previous work that engages with literature relies on \u201cdistant reading\u201d BIBREF1 , which involves discovering high-level patterns from large collections of stories BIBREF2 , BIBREF3 . We depart from this trend by showing that computational techniques can also engage with literary criticism at a closer distance: concretely, we use recent advances in text representation learning to test a single literary theory about the novel Invisible Cities by Italo Calvino.", + "Framed as a dialogue between the traveler Marco Polo and the emperor Kublai Khan, Invisible Cities consists of 55 prose poems, each of which describes an imaginary city. Calvino categorizes these cities into eleven thematic groups that deal with human emotions (e.g., desires, memories), general objects (eyes, sky, signs), and unusual properties (continuous, hidden, thin). Many critics argue that Calvino's labels are not meaningful, while others believe that there is a distinct thematic separation between the groups, including the author himself BIBREF4 . The unique structure of this novel \u2014 each city's description is short and self-contained (Figure FIGREF1 ) \u2014 allows us to computationally examine this debate.", + "As the book is too small to train any models, we leverage recent advances in large-scale language model-based representations BIBREF5 , BIBREF6 to compute a representation of each city. We feed these representations into a clustering algorithm that produces exactly eleven clusters of five cities each and evaluate them against both Calvino's original labels and crowdsourced human judgments. While the overall correlation with Calvino's labels is low, both computers and humans can reliably identify some thematic groups associated with concrete objects.", + "While prior work has computationally analyzed a single book BIBREF7 , our work goes beyond simple word frequency or n-gram counts by leveraging the power of pretrained language models to engage with literary criticism. Admittedly, our approach and evaluations are specific to Invisible Cities, but we believe that similar analyses of more conventionally-structured novels could become possible as text representation methods improve. We also highlight two challenges of applying computational methods to literary criticisms: (1) text representation methods are imperfect, especially when given writing as complex as Calvino's; and (2) evaluation is difficult because there is no consensus among literary critics on a single \u201ccorrect\u201d interpretation." + ], + [ + "Before describing our method and results, we first review critical opinions on both sides of whether Calvino's thematic groups meaningfully characterize his city descriptions." + ], + [ + "We focus on measuring to what extent computers can recover Calvino's thematic groupings when given just raw text of the city descriptions. At a high level, our approach (Figure FIGREF4 ) involves (1) computing a vector representation for every city and (2) performing unsupervised clustering of these representations. The rest of this section describes both of these steps in more detail." + ], + [ + "While each of the city descriptions is relatively short, Calvino's writing is filled with rare words, complex syntactic structures, and figurative language. Capturing the essential components of each city in a single vector is thus not as simple as it is with more standard forms of text. Nevertheless, we hope that representations from language models trained over billions of words of text can extract some meaningful semantics from these descriptions. We experiment with three different pretrained representations: ELMo BIBREF5 , BERT BIBREF6 , and GloVe BIBREF18 . To produce a single city embedding, we compute the TF-IDF weighted element-wise mean of the token-level representations. For all pretrained methods, we additionally reduce the dimensionality of the city embeddings to 40 using PCA for increased compatibility with our clustering algorithm." + ], + [ + "Given 55 city representations, how do we group them into eleven clusters of five cities each? Initially, we experimented with a graph-based community detection algorithm that maximizes cluster modularity BIBREF20 , but we found no simple way to constrain this method to produce a specific number of equally-sized clusters. The brute force approach of enumerating all possible cluster assignments is intractable given the large search space ( INLINEFORM0 possible assignments). We devise a simple clustering algorithm to approximate this process. First, we initialize with random cluster assignments and define \u201ccluster strength\u201d to be the relative difference between \u201cintra-group\u201d Euclidean distance and \u201cinter-group\u201d Euclidean distance. Then, we iteratively propose random exchanges of memberships, only accepting these proposals when the cluster strength increases, until convergence. To evaluate the quality of the computationally-derived clusters against those of Calvino, we measure cluster purity BIBREF21 : given a set of predicted clusters INLINEFORM1 and ground-truth clusters INLINEFORM2 that both partition a set of INLINEFORM3 data points, INLINEFORM4 " + ], + [ + "While the results from the above section allow us to compare our three computational methods against each other, we additionally collect human judgments to further ground our results. In this section, we first describe our human experiment before quantitatively analyzing our results." + ], + [ + "We compare clusters computed on different representations using community purity; additionally, we compare these computational methods to humans by their accuracy on the odd-one-out task.", + "City representations computed using language model-based representation (ELMo and BERT) achieve significantly higher purity than a clustering induced from random representations, indicating that there is at least some meaningful coherence to Calvino's thematic groups (first row of Table TABREF11 ). ELMo representations yield the highest purity among the three methods, which is surprising as BERT is a bigger model trained on data from books (among other domains). Both ELMo and BERT outperform GloVe, which intuitively makes sense because the latter do not model the order or structure of the words in each description.", + "While the purity of our methods is higher than that of a random clustering, it is still far below 1. To provide additional context to these results, we now switch to our \u201codd-one-out\u201d task and compare directly to human performance. For each triplet of cities, we identify the intruder as the city with the maximum Euclidean distance from the other two. Interestingly, crowd workers achieve only slightly higher accuracy than ELMo city representations; their interannotator agreement is also low, which indicates that close reading to analyze literary coherence between multiple texts is a difficult task, even for human annotators. Overall, results from both computational and human approaches suggests that the author-assigned labels are not entirely arbitrary, as we can reliably recover some of the thematic groups." + ], + [ + "Our quantitative results suggest that while vector-based city representations capture some thematic similarities, there is much room for improvement. In this section, we first investigate whether the learned clusters provide evidence for any arguments put forth by literary critics on the novel. Then, we explore possible reasons that the learned clusters deviate from Calvino's." + ], + [ + "Most previous work within the NLP community applies distant reading BIBREF1 to large collections of books, focusing on modeling different aspects of narratives such as plots and event sequences BIBREF22 , BIBREF23 , BIBREF24 , BIBREF25 , characters BIBREF2 , BIBREF26 , BIBREF27 , BIBREF28 , and narrative similarity BIBREF3 . In the same vein, researchers in computational literary analysis have combined statistical techniques and linguistics theories to perform quantitative analysis on large narrative texts BIBREF29 , BIBREF30 , BIBREF31 , BIBREF32 , BIBREF33 , but these attempts largely rely on techniques such as word counting, topic modeling, and naive Bayes classifiers and are therefore not able to capture the meaning of sentences or paragraphs BIBREF34 . While these works discover general patterns from multiple literary works, we are the first to use cutting-edge NLP techniques to engage with specific literary criticism about a single narrative.", + "There has been other computational work that focuses on just a single book or a small number of books, much of it focused on network analysis: BIBREF35 extract character social networks from Alice in Wonderland, while BIBREF36 recover social networks from 19th century British novels. BIBREF37 disentangles multiple narrative threads within the novel Infinite Jest, while BIBREF7 provides several automated statistical methods for close reading and test them on the award-winning novel Cloud Atlas (2004). Compared to this work, we push further on modeling the content of the narrative by leveraging pretrained language models." + ], + [ + "Our work takes a first step towards computationally engaging with literary criticism on a single book using state-of-the-art text representation methods. While we demonstrate that NLP techniques can be used to support literary analyses and obtain new insights, they also have clear limitations (e.g., in understanding abstract themes). As text representation methods become more powerful, we hope that (1) computational tools will become useful for analyzing novels with more conventional structures, and (2) literary criticism will be used as a testbed for evaluating representations." + ], + [ + "We thank the anonymous reviewers for their insightful comments. Additionally, we thank Nader Akoury, Garrett Bernstein, Chenghao Lv, Ari Kobren, Kalpesh Krishna, Saumya Lal, Tu Vu, Zhichao Yang, Mengxue Zhang and the UMass NLP group for suggestions that improved the paper's clarity, coverage of related work, and analysis experiments." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0561/instruction.md b/qasper-0561/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..51c922e5dfec5d20c1167f719718c9b774cebbea --- /dev/null +++ b/qasper-0561/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Scalable and Accurate Dialogue State Tracking via Hierarchical Sequence Generation + +Question: What are the performance metrics used? \ No newline at end of file diff --git a/qasper-0566/instruction.md b/qasper-0566/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..e91695544faaeb08887cd6ff75a4da1e2686f862 --- /dev/null +++ b/qasper-0566/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: A Simple Method for Commonsense Reasoning + +Question: Which of their training domains improves performance the most? \ No newline at end of file diff --git a/qasper-0568/instruction.md b/qasper-0568/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..60788914ab9e3354877c2936425e5afd6a092599 --- /dev/null +++ b/qasper-0568/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology + +Question: Why does not the approach from English work on other languages? \ No newline at end of file diff --git a/qasper-0569/instruction.md b/qasper-0569/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..69bf6452dd0f74769c556455fc2d51a1ddc1d223 --- /dev/null +++ b/qasper-0569/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology + +Question: How do they measure grammaticality? \ No newline at end of file diff --git a/qasper-0592/instruction.md b/qasper-0592/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..b6da9a87924636328305e367d20deff0c047bffc --- /dev/null +++ b/qasper-0592/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Shallow Discourse Annotation for Chinese TED Talks + +Question: Which inter-annotator metric do they use? \ No newline at end of file diff --git a/qasper-0593/instruction.md b/qasper-0593/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..d3a1444f6ac830d01dbef224fa65bca810446f07 --- /dev/null +++ b/qasper-0593/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Shallow Discourse Annotation for Chinese TED Talks + +Question: How high is the inter-annotator agreement? \ No newline at end of file diff --git a/qasper-0595/instruction.md b/qasper-0595/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..dfbc1391c543c49f5571766095d1a9dc93d2b0ce --- /dev/null +++ b/qasper-0595/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: The Role of Pragmatic and Discourse Context in Determining Argument Impact + +Question: How better are results compared to baseline models? \ No newline at end of file diff --git a/qasper-0611/instruction.md b/qasper-0611/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..e05549f4bfa1f53ce3cb9a99563a379d9a638289 --- /dev/null +++ b/qasper-0611/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Mapping (Dis-)Information Flow about the MH17 Plane Crash + +Question: How can the classifier facilitate the annotation task for human annotators? \ No newline at end of file diff --git a/qasper-0616/instruction.md b/qasper-0616/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..4378a9cabeb27bd88793d49dfe2ff9fda62ca64e --- /dev/null +++ b/qasper-0616/instruction.md @@ -0,0 +1,129 @@ +Name of Paper: Mapping (Dis-)Information Flow about the MH17 Plane Crash + +Question: What languages are included in the dataset? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Introduction ::: MH17 Related (Dis-)Information Flow on Twitter", + "Introduction ::: Contributions", + "Competing Narratives about the MH17 Crash", + "Dataset", + "Classification Models", + "Classification Models ::: Hashtag-Based Baseline", + "Classification Models ::: Logistic Regression Classifier", + "Classification Models ::: Convolutional Neural Network Classifier", + "Experimental Setup", + "Experimental Setup ::: Tweet Preprocessing", + "Experimental Setup ::: Evaluation Metrics", + "Results", + "Results ::: Comparison Between Models", + "Results ::: Per-Class Performance", + "Data Augmentation Experiments using Cross-Lingual Transfer", + "Error Analysis", + "Error Analysis ::: Category I Errors", + "Error Analysis ::: Category II Errors", + "Error Analysis ::: Category III Errors", + "Integrating Automatic Predictions into the Retweet Network", + "Integrating Automatic Predictions into the Retweet Network ::: Predicting Polarized Edges", + "Conclusion", + "Acknowledgements" + ], + "paragraphs": [ + [ + "Digital media enables fast sharing of information, including various forms of false or deceptive information. Hence, besides bringing the obvious advantage of broadening information access for everyone, digital media can also be misused for campaigns that spread disinformation about specific events, or campaigns that are targeted at specific individuals or governments. Disinformation, in this case, refers to intentionally misleading content BIBREF0. A prominent case of a disinformation campaign are the efforts of the Russian government to control information during the Russia-Ukraine crisis BIBREF1. One of the most important events during the crisis was the crash of Malaysian Airlines (MH17) flight on July 17, 2014. The plane crashed on its way from Amsterdam to Kuala Lumpur over Ukrainian territory, causing the death of 298 civilians. The event immediately led to the circulation of competing narratives about who was responsible for the crash (see Section SECREF2), with the two most prominent narratives being that the plane was either shot down by the Ukrainian military, or by Russian separatists in Ukraine supported by the Russian government BIBREF2. The latter theory was confirmed by findings of an international investigation team. In this work, information that opposes these findings by promoting other theories about the crash is considered disinformation. When studying disinformation, however, it is important to acknowledge that our fact checkers (in this case the international investigation team) may be wrong, which is why we focus on both of the narratives in our study.", + "MH17 is a highly important case in the context of international relations, because the tragedy has not only increased Western, political pressure against Russia, but may also continue putting the government's global image at stake. In 2020, at least four individuals connected to the Russian separatist movement will face murder charges for their involvement in the MH17 crash BIBREF3, which is why one can expect the waves of disinformation about MH17 to continue spreading. The purpose of this work is to develop an approach that may help both practitioners and scholars of political science, international relations and political communication to detect and measure the scope of MH17-related disinformation.", + "Several studies analyse the framing of the crash and the spread of (dis)information about the event in terms of pro-Russian or pro-Ukrainian framing. These studies analyse information based on manually labeled content, such as television transcripts BIBREF2 or tweets BIBREF4, BIBREF5. Restricting the analysis to manually labeled content ensures a high quality of annotations, but prohibits analysis from being extended to the full amount of available data. Another widely used method for classifying misleading content is to use distant annotations, for example to classify a tweet based on the domain of a URL that is shared by the tweet, or a hashtag that is contained in the tweet BIBREF6, BIBREF7, BIBREF8. Often, this approach treats content from uncredible sources as misleading (e.g. misinformation, disinformation or fake news). This methods enables researchers to scale up the number of observations without having to evaluate the fact value of each piece of content from low-quality sources. However, the approach fails to address an important issue: Not all content from uncredible sources is necessarily misleading or false and not all content from credible sources is true. As often emphasized in the propaganda literature, established media outlets too are vulnerable to state-driven disinformation campaigns, even if they are regarded as credible sources BIBREF9, BIBREF10, BIBREF11.", + "In order to scale annotations that go beyond metadata to larger datasets, Natural Language Processing (NLP) models can be used to automatically label text content. For example, several works developed classifiers for annotating text content with frame labels that can subsequently be used for large-scale content analysis BIBREF12, BIBREF13, BIBREF14, BIBREF15, BIBREF16, BIBREF17, BIBREF18, BIBREF19. Similarly, automatically labeling attitudes expressed in text BIBREF20, BIBREF21, BIBREF22, BIBREF23 can aid the analysis of disinformation and misinformation spread BIBREF24. In this work, we examine to which extent such classifiers can be used to detect pro-Russian framing related to the MH17 crash, and to which extent classifier predictions can be relied on for analysing information flow on Twitter." + ], + [ + "We focus our classification efforts on a Twitter dataset introduced in BIBREF4, that was collected to investigate the flow of MH17-related information on Twitter, focusing on the question who is distributing (dis-)information. In their analysis, the authors found that citizens are active distributors, which contradicts the widely adopted view that the information campaign is only driven by the state and that citizens do not have an active role.", + "To arrive at this conclusion, the authors manually labeled a subset of the tweets in the dataset with pro-Russian/pro-Ukrainian frames and build a retweet network, which has Twitter users as nodes and edges between two nodes if a retweet occurred between the two associated users. An edge was considered as polarized (either pro-Russian or pro-Ukrainian), if at least one retweet between the two users connected by the edge was pro-Russian/pro-Ukrainian. Then, the amount of polarized edges between users with different profiles (e.g. citizen, journalist, state organ) was computed.", + "Labeling more data via automatic classification (or computer-assisted annotation) of tweets could serve an analysis as the one presented in BIBREF4 in two ways. First, more edges could be labeled. Second, edges could be labeled with higher precision, i.e. by taking more tweets comprised by the edge into account. For example, one could decide to only label an edge as polarized if at least half of the retweets between the users were pro-Ukrainian/pro-Russian." + ], + [ + "We evaluate different classifiers that predict frames for unlabeled tweets in BIBREF4's dataset, in order to increase the number of polarized edges in the retweet network derived from the data. This is challenging due to a skewed data distribution and the small amount of training data for the pro-Russian class. We try to combat the data sparsity using a data augmentation approach, but have to report a negative result as we find that data augmentation in this particular case does not improve classification results. While our best neural classifier clearly outperforms a hashtag-based baseline, generating high quality predictions for the pro-Russian class is difficult: In order to make predictions at a precision level of 80%, recall has to be decreased to 23%. Finally, we examine the applicability of the classifier for finding new polarized edges in a retweet network and show how, with manual filtering, the number of pro-Russian edges can be increased by 29%. We make our code, trained models and predictions publicly available." + ], + [ + "We briefly summarize the timeline around the crash of MH17 and some of the dominant narratives present in the dataset. On July 17, 2014, the MH17 flight crashed over Donetsk Oblast in Ukraine. The region was at that time part of an armed conflict between pro-Russian separatists and the Ukrainian military, one of the unrests following the Ukrainian revolution and the annexation of Crimea by the Russian government. The territory in which the plane fell down was controlled by pro-Russian separatists.", + "Right after the crash, two main narratives were propagated: Western media claimed that the plane was shot down by pro-Russian separatists, whereas the Russian government claimed that the Ukrainian military was responsible. Two organisations were tasked with investigating the causes of the crash, the Dutch Safety Board (DSB) and the Dutch-led joint investigation team (JIT). Their final reports were released in October 2015 and September 2016, respectively, and conclude that the plane had been shot down by a missile launched by a BUK surface-to-air system. The BUK was stationed in an area controlled by pro-Russian separatists when the missile was launched, and had been transported there from Russia and returned to Russia after the incident. These findings are denied by the Russian government until now. There are several other crash-related reports that are frequently mentioned throughout the dataset. One is a report by Almaz-Antey, the Russian company that manufactured the BUK, which rejects the DSB findings based on mismatch of technical evidence. Several reports backing up the Dutch findings were released by the investigative journalism website Bellingcat.", + "The crash also sparked the circulation of several alternative theories, many of them promoted in Russian media BIBREF2, e.g. that the plane was downed by Ukrainian SU25 military jets, that the plane attack was meant to hit Putin\u2019s plane that was allegedly traveling the same route earlier that day, and that the bodies found in the plane had already been dead before the crash." + ], + [ + "For our classification experiments, we use the MH17 Twitter dataset introduced by BIBREF4, a dataset collected in order to study the flow of (dis)information about the MH17 plane crash on Twitter. It contains tweets collected based on keyword search that were posted between July 17, 2014 (the day of the plane crash) and December 9, 2016.", + "BIBREF4 provide annotations for a subset of the English tweets contained in the dataset. A tweet is annotated with one of three classes that indicate the framing of the tweet with respect to responsibility for the plane crash. A tweet can either be pro-Russian (Ukrainian authorities, NATO or EU countries are explicitly or implicitly held responsible, or the tweet states that Russia is not responsible), pro-Ukrainian (the Russian Federation or Russian separatists in Ukraine are explicitly or implicitly held responsible, or the tweet states that Ukraine is not responsible) or neutral (neither Ukraine nor Russia or any others are blamed). Example tweets for each category can be found in Table TABREF9. These examples illustrate that the framing annotations do not reflect general polarity, but polarity with respect to responsibility to the crash. For example, even though the last example in the table is in general pro-Ukrainian, as it displays the separatists in a bad light, the tweet does not focus on responsibility for the crash. Hence the it is labeled as neutral. Table TABREF8 shows the label distribution of the annotated portion of the data as well as the total amount of original tweets, and original tweets plus their retweets/duplicates in the network. A retweet is a repost of another user's original tweet, indicated by a specific syntax (RT @username: ). We consider as duplicate a tweet with text that is identical to an original tweet after preprocessing (see Section SECREF18). For our classification experiments, we exclusively consider original tweets, but model predictions can then be propagated to retweets and duplicates." + ], + [ + "For our classification experiments, we compare three classifiers, a hashtag-based baseline, a logistic regression classifier and a convolutional neural network (CNN)." + ], + [ + "Hashtags are often used as a means to assess the content of a tweet BIBREF25, BIBREF26, BIBREF27. We identify hashtags indicative of a class in the annotated dataset using the pointwise mutual information (pmi) between a hashtag $hs$ and a class $c$, which is defined as", + "We then predict the class for unseen tweets as the class that has the highest pmi score for the hashtags contained in the tweet. Tweets without hashtag (5% of the tweets in the development set) or with multiple hashtags leading to conflicting predictions (5% of the tweets in the development set) are labeled randomly. We refer to to this baseline as hs_pmi." + ], + [ + "As non-neural baseline we use a logistic regression model. We compute input representations for tweets as the average over pre-trained word embedding vectors for all words in the tweet. We use fasttext embeddings BIBREF28 that were pre-trained on Wikipedia." + ], + [ + "As neural classification model, we use a convolutional neural network (CNN) BIBREF29, which has previously shown good results for tweet classification BIBREF30, BIBREF27. The model performs 1d convolutions over a sequence of word embeddings. We use the same pre-trained fasttext embeddings as for the logistic regression model. We use a model with one convolutional layer and a relu activation function, and one max pooling layer. The number of filters is 100 and the filter size is set to 4." + ], + [ + "We evaluate the classification models using 10-fold cross validation, i.e. we produce 10 different datasplits by randomly sampling 60% of the data for training, 20% for development and 20% for testing. For each fold, we train each of the models described in Section SECREF4 on the training set and measure performance on the test set. For the CNN and LogReg models, we upsample the training examples such that each class has as many instances as the largest class (Neutral). The final reported scores are averages over the 10 splits." + ], + [ + "Before embedding the tweets, we replace urls, retweet syntax (RT @user_name: ) and @mentions (@user_name) by placeholders. We lowercase all text and tokenize sentences using the StandfordNLP pipeline BIBREF31. If a tweet contains multiple sentences, these are concatenated. Finally, we remove all tokens that contain non-alphanumeric symbols (except for dashes and hashtags) and strip the hashtags from each token, in order to increase the number of words that are represented by a pre-trained word embedding." + ], + [ + "We report performance as F1-scores, which is the harmonic mean between precision and recall. As the class distribution is highly skewed and we are mainly interested in accurately classifying the classes with low support (pro-Russian and pro-Ukrainian), we report macro-averages over the classes. In addition to F1-scores, we report the area under the precision-recall curve (AUC). We compute an AUC score for each class by converting the classification task into a one-vs-all classification task." + ], + [ + "The results of our classification experiments are presented in Table TABREF21. Figure FIGREF22 shows the per-class precision-recall curves for the LogReg and CNN models as well as the confusion matrices between classes." + ], + [ + "We observe that the hashtag baseline performs poorly and does not improve over the random baseline. The CNN classifier outperforms the baselines as well as the LogReg model. It shows the highest improvement over the LogReg for the pro-Russian class. Looking at the confusion matrices, we observe that for the LogReg model, the fraction of True Positives is equal between the pro-Russian and the pro-Ukrainian class. The CNN model produces a higher amount of correct predictions for the pro-Ukrainian than for the pro-Russian class. The absolute number of pro-Russian True Positives is lower for the CNN, but so is in return the amount of misclassifications between the pro-Russian and pro-Ukrainian class." + ], + [ + "With respect to the per class performance, we observe a similar trend across models, which is that the models perform best for the neutral class, whereas performance is lower for the pro-Ukrainian and pro-Russian classes. All models perform worst on the pro-Russian class, which might be due to the fact that it is the class with the fewest instances in the dataset.", + "Considering these results, we conclude that the CNN is the best performing model and also the classifier that best serves our goals, as we want to produce accurate predictions for the pro-Russian and pro-Ukrainian class without confusing between them. Even though the CNN can improve over the other models, the classification performance for the pro-Russian and pro-Ukrainian class is rather low. One obvious reason for this might be the small amount of training data, in particular for the pro-Russian class.", + "In the following, we briefly report a negative result on an attempt to combat the data sparseness with cross-lingual transfer. We then perform an error analysis on the CNN classifications to shed light on the difficulties of the task." + ], + [ + "The annotations in the MH17 dataset are highly imbalanced, with as few as 512 annotated examples for the pro-Russian class. As the annotated examples were sampled from the dataset at random, we assume that there are only few tweets with pro-Russian stance in the dataset. This observation is in line with studies that showed that the amount of disinformation on Twitter is in fact small BIBREF6, BIBREF8. In order to find more pro-Russian training examples, we turn to a resource that we expect to contain large amounts of pro-Russian (dis)information. The Elections integrity dataset was released by Twitter in 2018 and contains the tweets and account information for 3,841 accounts that are believed to be Russian trolls financed by the Russian government. While most tweets posted after late 2014 are in English language and focus on topics around the US elections, the earlier tweets in the dataset are primarily in Russian language and focus on the Ukraine crisis BIBREF33. One feature of the dataset observed by BIBREF33 is that several hashtags show high peakedness BIBREF34, i.e. they are posted with high frequency but only during short intervals, while others are persistent during time.", + "We find two hashtags in the Elections integrity dataset with high peakedness that were exclusively posted within 2 days after the MH17 crash and that seem to be pro-Russian in the context of responsibility for the MH17 crash: russian #\u041a\u0438\u0435\u0432\u0421\u043a\u0430\u0436\u0438\u041f\u0440\u0430\u0432\u0434\u0443 (Kiew tell the truth) and russian #\u041a\u0438\u0435\u0432\u0441\u0431\u0438\u043b\u0431\u043e\u0438\u043d\u0433 (Kiew made the plane go down). We collect all tweets with these two hashtags, resulting in 9,809 Russian tweets that we try to use as additional training data for the pro-Russian class in the MH17 dataset. We experiment with cross-lingual transfer by embedding tweets via aligned English and Russian word embeddings. However, so far results for the cross-lingual models do not improve over the CNN model trained on only English data. This might be due to the fact that the additional Russian tweets rather contain a general pro-Russian frame than specifically talking about the crash, but needs further investigation." + ], + [ + "In order to integrate automatically labeled examples into a network analysis that studies the flow of polarized information in the network, we need to produce high precision predictions for the pro-Russian and the pro-Ukrainian class. Polarized tweets that are incorrectly classified as neutral will hurt an analysis much less than neutral tweets that are erroneously classified as pro-Russian or pro-Ukrainian. However, the worst type of confusion is between the pro-Russian and pro-Ukrainian class. In order to gain insights into why these confusions happen, we manually inspect incorrectly predicted examples that are confused between the pro-Russian and pro-Ukrainian class. We analyse the misclassifications in the development set of all 10 runs, which results in 73 False Positives of pro-Ukrainian tweets being classified as pro-Russian (referred to as pro-Russian False Positives), and 88 False Positives of pro-Russian tweets being classified as pro-Ukrainian (referred to as pro-Ukrainian False Positives). We can identify three main cases for which the model produces an error:", + "the correct class can be directly inferred from the text content easily, even without background knowledge", + "the correct class can be inferred from the text content, given that event-specific knowledge is provided", + "the correct class can be inferred from the text content if the text is interpreted correctly", + "For the pro-Russian False Positives, we find that 42% of the errors are category I and II errors, respectively, and 15% of category III. For the pro-Ukrainian False Positives, we find 48% category I errors, 33% category II errors and and 13% category III errors. Table TABREF28 presents examples for each of the error categories in both sets which we will discuss in the following." + ], + [ + "Category I errors could easily be classified by humans following the annotation guidelines (see Section SECREF3). One difficulty can be seen in example f). Even though no background knowledge is needed to interpret the content, interpretation is difficult because of the convoluted syntax of the tweet. For the other examples it is unclear why the model would have difficulties with classifying them." + ], + [ + "Category II errors can only be classified with event-specific background knowledge. Examples g), i) and k) relate to the theory that a Ukrainian SU25 fighter jet shot down the plane in air. Correct interpretation of these tweets depends on knowledge about the SU25 fighter jet. In order to correctly interpret example j) as pro-Russian, it has to be known that the bellingcat report is pro-Ukrainian. Example l) relates to the theory that the shoot down was a false flag operation run by Western countries and the bodies in the plane were already dead before the crash. In order to correctly interpret example m), the identity of Kolomoisky has to be known. He is an anti-separatist Ukrainian billionaire, hence his involvement points to the Ukrainian government being responsible for the crash." + ], + [ + "Category III errors occur for examples that can only be classified by correctly interpreting the tweet authors' intention. Interpretation is difficult due to phenomena such as irony as in examples n) and o). While the irony is indicated in example n) through the use of the hashtag #LOL, there is no explicit indication in example o).", + "Interpretation of example q) is conditioned on world knowledge as well as the understanding of the speakers beliefs. Example r) is pro-Russian as it questions the validity of the assumption AC360 is making, but we only know that because we know that the assumption is absurd. Example s) requires to evaluate that the speaker thinks people on site are trusted more than people at home.", + "From the error analysis, we conclude that category I errors need further investigation, as here the model makes mistakes on seemingly easy instances. This might be due to the model not being able to correctly represent Twitter specific language or unknown words, such as Eukraine in example e). Category II and III errors are harder to avoid and could be improved by applying reasoning BIBREF36 or irony detection methods BIBREF37." + ], + [ + "Finally, we apply the CNN classifier to label new edges in BIBREF4's retweet network, which is shown in Figure FIGREF35. The retweet network is a graph that contains users as nodes and an edge between two users if the users are retweeting each other. In order to track the flow of polarized information, BIBREF4 label an edge as polarized if at least one tweet contained in the edge was manually annotated as pro-Russian or pro-Ukrainian. While the network shows a clear polarization, only a small subset of the edges present in the network are labeled (see Table TABREF38).", + "Automatic polarity prediction of tweets can help the analysis in two ways. Either, we can label a previously unlabeled edge, or we can verify/confirm the manual labeling of an edge, by labeling additional tweets that are comprised in the edge." + ], + [ + "In order to get high precision predictions for unlabeled tweets, we choose the probability thresholds for predicting a pro-Russian or pro-Ukrainian tweet such that the classifier would achieve 80% precision on the test splits (recall at this precision level is 23%). Table TABREF38 shows the amount of polarized edges we can predict at this precision level. Upon manual inspection, we however find that the quality of predictions is lower than estimated. Hence, we manually re-annotate the pro-Russian and pro-Ukrainian predictions according to the official annotation guidelines used by BIBREF4. This way, we can label 77 new pro-Russian edges by looking at 415 tweets, which means that 19% of the candidates are hits. For the pro-Ukrainian class, we can label 110 new edges by looking at 611 tweets (18% hits). Hence even though the quality of the classifier predictions is too low to be integrated into the network analysis right away, the classifier drastically facilitates the annotation process for human annotators compared to annotating unfiltered tweets (from the original labels we infer that for unfiltered tweets, only 6% are hits for the pro-Russian class, and 11% for the pro-Ukrainian class)." + ], + [ + "In this work, we investigated the usefulness of text classifiers to detect pro-Russian and pro-Ukrainian framing in tweets related to the MH17 crash, and to which extent classifier predictions can be relied on for producing high quality annotations. From our classification experiments, we conclude that the real-world applicability of text classifiers for labeling polarized tweets in a retweet network is restricted to pre-filtering tweets for manual annotation. However, if used as a filter, the classifier can significantly speed up the annotation process, making large-scale content analysis more feasible." + ], + [ + "We thank the anonymous reviewers for their helpful comments. The research was carried out as part of the \u2018Digital Disinformation\u2019 project, which was directed by Rebecca Adler-Nissen and funded by the Carlsberg Foundation (project number CF16-0012)." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0618/instruction.md b/qasper-0618/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..ad11a8dc561f39cd073f768d2b05004c103ea876 --- /dev/null +++ b/qasper-0618/instruction.md @@ -0,0 +1,129 @@ +Name of Paper: Mapping (Dis-)Information Flow about the MH17 Plane Crash + +Question: What proxies for data annotation were used in previous datasets? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Introduction ::: MH17 Related (Dis-)Information Flow on Twitter", + "Introduction ::: Contributions", + "Competing Narratives about the MH17 Crash", + "Dataset", + "Classification Models", + "Classification Models ::: Hashtag-Based Baseline", + "Classification Models ::: Logistic Regression Classifier", + "Classification Models ::: Convolutional Neural Network Classifier", + "Experimental Setup", + "Experimental Setup ::: Tweet Preprocessing", + "Experimental Setup ::: Evaluation Metrics", + "Results", + "Results ::: Comparison Between Models", + "Results ::: Per-Class Performance", + "Data Augmentation Experiments using Cross-Lingual Transfer", + "Error Analysis", + "Error Analysis ::: Category I Errors", + "Error Analysis ::: Category II Errors", + "Error Analysis ::: Category III Errors", + "Integrating Automatic Predictions into the Retweet Network", + "Integrating Automatic Predictions into the Retweet Network ::: Predicting Polarized Edges", + "Conclusion", + "Acknowledgements" + ], + "paragraphs": [ + [ + "Digital media enables fast sharing of information, including various forms of false or deceptive information. Hence, besides bringing the obvious advantage of broadening information access for everyone, digital media can also be misused for campaigns that spread disinformation about specific events, or campaigns that are targeted at specific individuals or governments. Disinformation, in this case, refers to intentionally misleading content BIBREF0. A prominent case of a disinformation campaign are the efforts of the Russian government to control information during the Russia-Ukraine crisis BIBREF1. One of the most important events during the crisis was the crash of Malaysian Airlines (MH17) flight on July 17, 2014. The plane crashed on its way from Amsterdam to Kuala Lumpur over Ukrainian territory, causing the death of 298 civilians. The event immediately led to the circulation of competing narratives about who was responsible for the crash (see Section SECREF2), with the two most prominent narratives being that the plane was either shot down by the Ukrainian military, or by Russian separatists in Ukraine supported by the Russian government BIBREF2. The latter theory was confirmed by findings of an international investigation team. In this work, information that opposes these findings by promoting other theories about the crash is considered disinformation. When studying disinformation, however, it is important to acknowledge that our fact checkers (in this case the international investigation team) may be wrong, which is why we focus on both of the narratives in our study.", + "MH17 is a highly important case in the context of international relations, because the tragedy has not only increased Western, political pressure against Russia, but may also continue putting the government's global image at stake. In 2020, at least four individuals connected to the Russian separatist movement will face murder charges for their involvement in the MH17 crash BIBREF3, which is why one can expect the waves of disinformation about MH17 to continue spreading. The purpose of this work is to develop an approach that may help both practitioners and scholars of political science, international relations and political communication to detect and measure the scope of MH17-related disinformation.", + "Several studies analyse the framing of the crash and the spread of (dis)information about the event in terms of pro-Russian or pro-Ukrainian framing. These studies analyse information based on manually labeled content, such as television transcripts BIBREF2 or tweets BIBREF4, BIBREF5. Restricting the analysis to manually labeled content ensures a high quality of annotations, but prohibits analysis from being extended to the full amount of available data. Another widely used method for classifying misleading content is to use distant annotations, for example to classify a tweet based on the domain of a URL that is shared by the tweet, or a hashtag that is contained in the tweet BIBREF6, BIBREF7, BIBREF8. Often, this approach treats content from uncredible sources as misleading (e.g. misinformation, disinformation or fake news). This methods enables researchers to scale up the number of observations without having to evaluate the fact value of each piece of content from low-quality sources. However, the approach fails to address an important issue: Not all content from uncredible sources is necessarily misleading or false and not all content from credible sources is true. As often emphasized in the propaganda literature, established media outlets too are vulnerable to state-driven disinformation campaigns, even if they are regarded as credible sources BIBREF9, BIBREF10, BIBREF11.", + "In order to scale annotations that go beyond metadata to larger datasets, Natural Language Processing (NLP) models can be used to automatically label text content. For example, several works developed classifiers for annotating text content with frame labels that can subsequently be used for large-scale content analysis BIBREF12, BIBREF13, BIBREF14, BIBREF15, BIBREF16, BIBREF17, BIBREF18, BIBREF19. Similarly, automatically labeling attitudes expressed in text BIBREF20, BIBREF21, BIBREF22, BIBREF23 can aid the analysis of disinformation and misinformation spread BIBREF24. In this work, we examine to which extent such classifiers can be used to detect pro-Russian framing related to the MH17 crash, and to which extent classifier predictions can be relied on for analysing information flow on Twitter." + ], + [ + "We focus our classification efforts on a Twitter dataset introduced in BIBREF4, that was collected to investigate the flow of MH17-related information on Twitter, focusing on the question who is distributing (dis-)information. In their analysis, the authors found that citizens are active distributors, which contradicts the widely adopted view that the information campaign is only driven by the state and that citizens do not have an active role.", + "To arrive at this conclusion, the authors manually labeled a subset of the tweets in the dataset with pro-Russian/pro-Ukrainian frames and build a retweet network, which has Twitter users as nodes and edges between two nodes if a retweet occurred between the two associated users. An edge was considered as polarized (either pro-Russian or pro-Ukrainian), if at least one retweet between the two users connected by the edge was pro-Russian/pro-Ukrainian. Then, the amount of polarized edges between users with different profiles (e.g. citizen, journalist, state organ) was computed.", + "Labeling more data via automatic classification (or computer-assisted annotation) of tweets could serve an analysis as the one presented in BIBREF4 in two ways. First, more edges could be labeled. Second, edges could be labeled with higher precision, i.e. by taking more tweets comprised by the edge into account. For example, one could decide to only label an edge as polarized if at least half of the retweets between the users were pro-Ukrainian/pro-Russian." + ], + [ + "We evaluate different classifiers that predict frames for unlabeled tweets in BIBREF4's dataset, in order to increase the number of polarized edges in the retweet network derived from the data. This is challenging due to a skewed data distribution and the small amount of training data for the pro-Russian class. We try to combat the data sparsity using a data augmentation approach, but have to report a negative result as we find that data augmentation in this particular case does not improve classification results. While our best neural classifier clearly outperforms a hashtag-based baseline, generating high quality predictions for the pro-Russian class is difficult: In order to make predictions at a precision level of 80%, recall has to be decreased to 23%. Finally, we examine the applicability of the classifier for finding new polarized edges in a retweet network and show how, with manual filtering, the number of pro-Russian edges can be increased by 29%. We make our code, trained models and predictions publicly available." + ], + [ + "We briefly summarize the timeline around the crash of MH17 and some of the dominant narratives present in the dataset. On July 17, 2014, the MH17 flight crashed over Donetsk Oblast in Ukraine. The region was at that time part of an armed conflict between pro-Russian separatists and the Ukrainian military, one of the unrests following the Ukrainian revolution and the annexation of Crimea by the Russian government. The territory in which the plane fell down was controlled by pro-Russian separatists.", + "Right after the crash, two main narratives were propagated: Western media claimed that the plane was shot down by pro-Russian separatists, whereas the Russian government claimed that the Ukrainian military was responsible. Two organisations were tasked with investigating the causes of the crash, the Dutch Safety Board (DSB) and the Dutch-led joint investigation team (JIT). Their final reports were released in October 2015 and September 2016, respectively, and conclude that the plane had been shot down by a missile launched by a BUK surface-to-air system. The BUK was stationed in an area controlled by pro-Russian separatists when the missile was launched, and had been transported there from Russia and returned to Russia after the incident. These findings are denied by the Russian government until now. There are several other crash-related reports that are frequently mentioned throughout the dataset. One is a report by Almaz-Antey, the Russian company that manufactured the BUK, which rejects the DSB findings based on mismatch of technical evidence. Several reports backing up the Dutch findings were released by the investigative journalism website Bellingcat.", + "The crash also sparked the circulation of several alternative theories, many of them promoted in Russian media BIBREF2, e.g. that the plane was downed by Ukrainian SU25 military jets, that the plane attack was meant to hit Putin\u2019s plane that was allegedly traveling the same route earlier that day, and that the bodies found in the plane had already been dead before the crash." + ], + [ + "For our classification experiments, we use the MH17 Twitter dataset introduced by BIBREF4, a dataset collected in order to study the flow of (dis)information about the MH17 plane crash on Twitter. It contains tweets collected based on keyword search that were posted between July 17, 2014 (the day of the plane crash) and December 9, 2016.", + "BIBREF4 provide annotations for a subset of the English tweets contained in the dataset. A tweet is annotated with one of three classes that indicate the framing of the tweet with respect to responsibility for the plane crash. A tweet can either be pro-Russian (Ukrainian authorities, NATO or EU countries are explicitly or implicitly held responsible, or the tweet states that Russia is not responsible), pro-Ukrainian (the Russian Federation or Russian separatists in Ukraine are explicitly or implicitly held responsible, or the tweet states that Ukraine is not responsible) or neutral (neither Ukraine nor Russia or any others are blamed). Example tweets for each category can be found in Table TABREF9. These examples illustrate that the framing annotations do not reflect general polarity, but polarity with respect to responsibility to the crash. For example, even though the last example in the table is in general pro-Ukrainian, as it displays the separatists in a bad light, the tweet does not focus on responsibility for the crash. Hence the it is labeled as neutral. Table TABREF8 shows the label distribution of the annotated portion of the data as well as the total amount of original tweets, and original tweets plus their retweets/duplicates in the network. A retweet is a repost of another user's original tweet, indicated by a specific syntax (RT @username: ). We consider as duplicate a tweet with text that is identical to an original tweet after preprocessing (see Section SECREF18). For our classification experiments, we exclusively consider original tweets, but model predictions can then be propagated to retweets and duplicates." + ], + [ + "For our classification experiments, we compare three classifiers, a hashtag-based baseline, a logistic regression classifier and a convolutional neural network (CNN)." + ], + [ + "Hashtags are often used as a means to assess the content of a tweet BIBREF25, BIBREF26, BIBREF27. We identify hashtags indicative of a class in the annotated dataset using the pointwise mutual information (pmi) between a hashtag $hs$ and a class $c$, which is defined as", + "We then predict the class for unseen tweets as the class that has the highest pmi score for the hashtags contained in the tweet. Tweets without hashtag (5% of the tweets in the development set) or with multiple hashtags leading to conflicting predictions (5% of the tweets in the development set) are labeled randomly. We refer to to this baseline as hs_pmi." + ], + [ + "As non-neural baseline we use a logistic regression model. We compute input representations for tweets as the average over pre-trained word embedding vectors for all words in the tweet. We use fasttext embeddings BIBREF28 that were pre-trained on Wikipedia." + ], + [ + "As neural classification model, we use a convolutional neural network (CNN) BIBREF29, which has previously shown good results for tweet classification BIBREF30, BIBREF27. The model performs 1d convolutions over a sequence of word embeddings. We use the same pre-trained fasttext embeddings as for the logistic regression model. We use a model with one convolutional layer and a relu activation function, and one max pooling layer. The number of filters is 100 and the filter size is set to 4." + ], + [ + "We evaluate the classification models using 10-fold cross validation, i.e. we produce 10 different datasplits by randomly sampling 60% of the data for training, 20% for development and 20% for testing. For each fold, we train each of the models described in Section SECREF4 on the training set and measure performance on the test set. For the CNN and LogReg models, we upsample the training examples such that each class has as many instances as the largest class (Neutral). The final reported scores are averages over the 10 splits." + ], + [ + "Before embedding the tweets, we replace urls, retweet syntax (RT @user_name: ) and @mentions (@user_name) by placeholders. We lowercase all text and tokenize sentences using the StandfordNLP pipeline BIBREF31. If a tweet contains multiple sentences, these are concatenated. Finally, we remove all tokens that contain non-alphanumeric symbols (except for dashes and hashtags) and strip the hashtags from each token, in order to increase the number of words that are represented by a pre-trained word embedding." + ], + [ + "We report performance as F1-scores, which is the harmonic mean between precision and recall. As the class distribution is highly skewed and we are mainly interested in accurately classifying the classes with low support (pro-Russian and pro-Ukrainian), we report macro-averages over the classes. In addition to F1-scores, we report the area under the precision-recall curve (AUC). We compute an AUC score for each class by converting the classification task into a one-vs-all classification task." + ], + [ + "The results of our classification experiments are presented in Table TABREF21. Figure FIGREF22 shows the per-class precision-recall curves for the LogReg and CNN models as well as the confusion matrices between classes." + ], + [ + "We observe that the hashtag baseline performs poorly and does not improve over the random baseline. The CNN classifier outperforms the baselines as well as the LogReg model. It shows the highest improvement over the LogReg for the pro-Russian class. Looking at the confusion matrices, we observe that for the LogReg model, the fraction of True Positives is equal between the pro-Russian and the pro-Ukrainian class. The CNN model produces a higher amount of correct predictions for the pro-Ukrainian than for the pro-Russian class. The absolute number of pro-Russian True Positives is lower for the CNN, but so is in return the amount of misclassifications between the pro-Russian and pro-Ukrainian class." + ], + [ + "With respect to the per class performance, we observe a similar trend across models, which is that the models perform best for the neutral class, whereas performance is lower for the pro-Ukrainian and pro-Russian classes. All models perform worst on the pro-Russian class, which might be due to the fact that it is the class with the fewest instances in the dataset.", + "Considering these results, we conclude that the CNN is the best performing model and also the classifier that best serves our goals, as we want to produce accurate predictions for the pro-Russian and pro-Ukrainian class without confusing between them. Even though the CNN can improve over the other models, the classification performance for the pro-Russian and pro-Ukrainian class is rather low. One obvious reason for this might be the small amount of training data, in particular for the pro-Russian class.", + "In the following, we briefly report a negative result on an attempt to combat the data sparseness with cross-lingual transfer. We then perform an error analysis on the CNN classifications to shed light on the difficulties of the task." + ], + [ + "The annotations in the MH17 dataset are highly imbalanced, with as few as 512 annotated examples for the pro-Russian class. As the annotated examples were sampled from the dataset at random, we assume that there are only few tweets with pro-Russian stance in the dataset. This observation is in line with studies that showed that the amount of disinformation on Twitter is in fact small BIBREF6, BIBREF8. In order to find more pro-Russian training examples, we turn to a resource that we expect to contain large amounts of pro-Russian (dis)information. The Elections integrity dataset was released by Twitter in 2018 and contains the tweets and account information for 3,841 accounts that are believed to be Russian trolls financed by the Russian government. While most tweets posted after late 2014 are in English language and focus on topics around the US elections, the earlier tweets in the dataset are primarily in Russian language and focus on the Ukraine crisis BIBREF33. One feature of the dataset observed by BIBREF33 is that several hashtags show high peakedness BIBREF34, i.e. they are posted with high frequency but only during short intervals, while others are persistent during time.", + "We find two hashtags in the Elections integrity dataset with high peakedness that were exclusively posted within 2 days after the MH17 crash and that seem to be pro-Russian in the context of responsibility for the MH17 crash: russian #\u041a\u0438\u0435\u0432\u0421\u043a\u0430\u0436\u0438\u041f\u0440\u0430\u0432\u0434\u0443 (Kiew tell the truth) and russian #\u041a\u0438\u0435\u0432\u0441\u0431\u0438\u043b\u0431\u043e\u0438\u043d\u0433 (Kiew made the plane go down). We collect all tweets with these two hashtags, resulting in 9,809 Russian tweets that we try to use as additional training data for the pro-Russian class in the MH17 dataset. We experiment with cross-lingual transfer by embedding tweets via aligned English and Russian word embeddings. However, so far results for the cross-lingual models do not improve over the CNN model trained on only English data. This might be due to the fact that the additional Russian tweets rather contain a general pro-Russian frame than specifically talking about the crash, but needs further investigation." + ], + [ + "In order to integrate automatically labeled examples into a network analysis that studies the flow of polarized information in the network, we need to produce high precision predictions for the pro-Russian and the pro-Ukrainian class. Polarized tweets that are incorrectly classified as neutral will hurt an analysis much less than neutral tweets that are erroneously classified as pro-Russian or pro-Ukrainian. However, the worst type of confusion is between the pro-Russian and pro-Ukrainian class. In order to gain insights into why these confusions happen, we manually inspect incorrectly predicted examples that are confused between the pro-Russian and pro-Ukrainian class. We analyse the misclassifications in the development set of all 10 runs, which results in 73 False Positives of pro-Ukrainian tweets being classified as pro-Russian (referred to as pro-Russian False Positives), and 88 False Positives of pro-Russian tweets being classified as pro-Ukrainian (referred to as pro-Ukrainian False Positives). We can identify three main cases for which the model produces an error:", + "the correct class can be directly inferred from the text content easily, even without background knowledge", + "the correct class can be inferred from the text content, given that event-specific knowledge is provided", + "the correct class can be inferred from the text content if the text is interpreted correctly", + "For the pro-Russian False Positives, we find that 42% of the errors are category I and II errors, respectively, and 15% of category III. For the pro-Ukrainian False Positives, we find 48% category I errors, 33% category II errors and and 13% category III errors. Table TABREF28 presents examples for each of the error categories in both sets which we will discuss in the following." + ], + [ + "Category I errors could easily be classified by humans following the annotation guidelines (see Section SECREF3). One difficulty can be seen in example f). Even though no background knowledge is needed to interpret the content, interpretation is difficult because of the convoluted syntax of the tweet. For the other examples it is unclear why the model would have difficulties with classifying them." + ], + [ + "Category II errors can only be classified with event-specific background knowledge. Examples g), i) and k) relate to the theory that a Ukrainian SU25 fighter jet shot down the plane in air. Correct interpretation of these tweets depends on knowledge about the SU25 fighter jet. In order to correctly interpret example j) as pro-Russian, it has to be known that the bellingcat report is pro-Ukrainian. Example l) relates to the theory that the shoot down was a false flag operation run by Western countries and the bodies in the plane were already dead before the crash. In order to correctly interpret example m), the identity of Kolomoisky has to be known. He is an anti-separatist Ukrainian billionaire, hence his involvement points to the Ukrainian government being responsible for the crash." + ], + [ + "Category III errors occur for examples that can only be classified by correctly interpreting the tweet authors' intention. Interpretation is difficult due to phenomena such as irony as in examples n) and o). While the irony is indicated in example n) through the use of the hashtag #LOL, there is no explicit indication in example o).", + "Interpretation of example q) is conditioned on world knowledge as well as the understanding of the speakers beliefs. Example r) is pro-Russian as it questions the validity of the assumption AC360 is making, but we only know that because we know that the assumption is absurd. Example s) requires to evaluate that the speaker thinks people on site are trusted more than people at home.", + "From the error analysis, we conclude that category I errors need further investigation, as here the model makes mistakes on seemingly easy instances. This might be due to the model not being able to correctly represent Twitter specific language or unknown words, such as Eukraine in example e). Category II and III errors are harder to avoid and could be improved by applying reasoning BIBREF36 or irony detection methods BIBREF37." + ], + [ + "Finally, we apply the CNN classifier to label new edges in BIBREF4's retweet network, which is shown in Figure FIGREF35. The retweet network is a graph that contains users as nodes and an edge between two users if the users are retweeting each other. In order to track the flow of polarized information, BIBREF4 label an edge as polarized if at least one tweet contained in the edge was manually annotated as pro-Russian or pro-Ukrainian. While the network shows a clear polarization, only a small subset of the edges present in the network are labeled (see Table TABREF38).", + "Automatic polarity prediction of tweets can help the analysis in two ways. Either, we can label a previously unlabeled edge, or we can verify/confirm the manual labeling of an edge, by labeling additional tweets that are comprised in the edge." + ], + [ + "In order to get high precision predictions for unlabeled tweets, we choose the probability thresholds for predicting a pro-Russian or pro-Ukrainian tweet such that the classifier would achieve 80% precision on the test splits (recall at this precision level is 23%). Table TABREF38 shows the amount of polarized edges we can predict at this precision level. Upon manual inspection, we however find that the quality of predictions is lower than estimated. Hence, we manually re-annotate the pro-Russian and pro-Ukrainian predictions according to the official annotation guidelines used by BIBREF4. This way, we can label 77 new pro-Russian edges by looking at 415 tweets, which means that 19% of the candidates are hits. For the pro-Ukrainian class, we can label 110 new edges by looking at 611 tweets (18% hits). Hence even though the quality of the classifier predictions is too low to be integrated into the network analysis right away, the classifier drastically facilitates the annotation process for human annotators compared to annotating unfiltered tweets (from the original labels we infer that for unfiltered tweets, only 6% are hits for the pro-Russian class, and 11% for the pro-Ukrainian class)." + ], + [ + "In this work, we investigated the usefulness of text classifiers to detect pro-Russian and pro-Ukrainian framing in tweets related to the MH17 crash, and to which extent classifier predictions can be relied on for producing high quality annotations. From our classification experiments, we conclude that the real-world applicability of text classifiers for labeling polarized tweets in a retweet network is restricted to pre-filtering tweets for manual annotation. However, if used as a filter, the classifier can significantly speed up the annotation process, making large-scale content analysis more feasible." + ], + [ + "We thank the anonymous reviewers for their helpful comments. The research was carried out as part of the \u2018Digital Disinformation\u2019 project, which was directed by Rebecca Adler-Nissen and funded by the Carlsberg Foundation (project number CF16-0012)." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0620/instruction.md b/qasper-0620/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..ad4c0a1edbad44bd4285a6fe4a8d1c05d121ef86 --- /dev/null +++ b/qasper-0620/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Conversational Intent Understanding for Passengers in Autonomous Vehicles + +Question: What is the size of their collected dataset? \ No newline at end of file diff --git a/qasper-0627/instruction.md b/qasper-0627/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..e6aec8b6dcd2b50a1e204be8bcd45d7a15c4212c --- /dev/null +++ b/qasper-0627/instruction.md @@ -0,0 +1,108 @@ +Name of Paper: Predictive Embeddings for Hate Speech Detection on Twitter + +Question: Do they report results only on English data? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work", + "Data", + "Transformed Word Embedding Model (TWEM)", + "Word Embeddings", + "Pooling", + "Output", + "Experimental Setup", + "Results and Discussion", + "Error Analysis", + "Conclusion", + "Supplemental Material", + "Preprocessing", + "Embedding Analysis" + ], + "paragraphs": [ + [ + "The increasing popularity of social media platforms like Twitter for both personal and political communication BIBREF0 has seen a well-acknowledged rise in the presence of toxic and abusive speech on these platforms BIBREF1 , BIBREF2 . Although the terms of services on these platforms typically forbid hateful and harassing speech, enforcing these rules has proved challenging, as identifying hate speech speech at scale is still a largely unsolved problem in the NLP community. BIBREF3 , for example, identify many ambiguities in classifying abusive communications, and highlight the difficulty of clearly defining the parameters of such speech. This problem is compounded by the fact that identifying abusive or harassing speech is a challenge for humans as well as automated systems.", + "Despite the lack of consensus around what constitutes abusive speech, some definition of hate speech must be used to build automated systems to address it. We rely on BIBREF4 's definition of hate speech, specifically: \u201clanguage that is used to express hatred towards a targeted group or is intended to be derogatory, to humiliate, or to insult the members of the group.\u201d", + "In this paper, we present a neural classification system that uses minimal preprocessing to take advantage of a modified Simple Word Embeddings-based Model BIBREF5 to predict the occurrence of hate speech. Our classifier features:", + "In the following sections, we discuss related work on hate speech classification, followed by a description of the datasets, methods and results of our study." + ], + [ + "Many efforts have been made to classify hate speech using data scraped from online message forums and popular social media sites such as Twitter and Facebook. BIBREF3 applied a logistic regression model that used one- to four-character n-grams for classification of tweets labeled as racist, sexist or neither. BIBREF4 experimented in classification of hateful as well as offensive but not hateful tweets. They applied a logistic regression classifier with L2 regularization using word level n-grams and various part-of-speech, sentiment, and tweet-level metadata features.", + "Additional projects have built upon the data sets created by Waseem and/or Davidson. For example, BIBREF6 used a neural network approach with two binary classifiers: one to predict the presence abusive speech more generally, and another to discern the form of abusive speech.", + " BIBREF7 , meanwhile, used pre-trained word2vec embeddings, which were then fed into a convolutional neural network (CNN) with max pooling to produce input vectors for a Gated Recurrent Unit (GRU) neural network. Other researchers have experimented with using metadata features from tweets. BIBREF8 built a classifier composed of two separate neural networks, one for the text and the other for metadata of the Twitter user, that were trained jointly in interleaved fashion. Both networks used in combination - and especially when trained using transfer learning - achieved higher F1 scores than either neural network classifier alone.", + "In contrast to the methods described above, our approach relies on a simple word embedding (SWEM)-based architecture BIBREF5 , reducing the number of required parameters and length of training required, while still yielding improved performance and resilience across related classification tasks. Moreover, our network is able to learn flexible vector representations that demonstrate associations among words typically used in hateful communication. Finally, while metadata-based augmentation is intriguing, here we sought to develop an approach that would function well even in cases where such additional data was missing due to the deletion, suspension, or deactivation of accounts." + ], + [ + "In this paper, we use three data sets from the literature to train and evaluate our own classifier. Although all address the category of hateful speech, they used different strategies of labeling the collected data. Table TABREF5 shows the characteristics of the datasets.", + "Data collected by BIBREF3 , which we term the Sexist/Racist (SR) data set, was collected using an initial Twitter search followed by analysis and filtering by the authors and their team who identified 17 common phrases, hashtags, and users that were indicative of abusive speech. BIBREF4 collected the HATE dataset by searching for tweets using a lexicon provided by Hatebase.org. The final data set we used, which we call HAR, was collected by BIBREF9 ; we removed all retweets reducing the dataset to 20,000 tweets. Tweets were labeled as \u201cHarrassing\u201d or \u201cNon-Harrassing\u201d; hate speech was not explicitly labeled, but treated as an unlabeled subset of the broader \u201cHarrassing\u201d category BIBREF9 ." + ], + [ + "Our training set consists of INLINEFORM0 examples INLINEFORM1 where the input INLINEFORM2 is a sequence of tokens INLINEFORM3 , and the output INLINEFORM4 is the numerical class for the hate speech class. Each input instance represents a Twitter post and thus, is not limited to a single sentence.", + "We modify the SWEM-concat BIBREF5 architecture to allow better handling of infrequent and unknown words and to capture non-linear word combinations." + ], + [ + "Each token in the input is mapped to an embedding. We used the 300 dimensional embeddings for all our experiments, so each word INLINEFORM0 is mapped to INLINEFORM1 . We denote the full embedded sequence as INLINEFORM2 . We then transform each word embedding by applying 300 dimensional 1-layer Multi Layer Perceptron (MLP) INLINEFORM3 with a Rectified Liner Unit (ReLU) activation to form an updated embedding space INLINEFORM4 . We find this better handles unseen or rare tokens in our training data by projecting the pretrained embedding into a space that the encoder can understand." + ], + [ + "We make use of two pooling methods on the updated embedding space INLINEFORM0 . We employ a max pooling operation on INLINEFORM1 to capture salient word features from our input; this representation is denoted as INLINEFORM2 . This forces words that are highly indicative of hate speech to higher positive values within the updated embedding space. We also average the embeddings INLINEFORM3 to capture the overall meaning of the sentence, denoted as INLINEFORM4 , which provides a strong conditional factor in conjunction with the max pooling output. This also helps regularize gradient updates from the max pooling operation." + ], + [ + "We concatenate INLINEFORM0 and INLINEFORM1 to form a document representation INLINEFORM2 and feed the representation into a 50 node 2 layer MLP followed by ReLU Activation to allow for increased nonlinear representation learning. This representation forms the preterminal layer and is passed to a fully connected softmax layer whose output is the probability distribution over labels." + ], + [ + "We tokenize the data using Spacy BIBREF10 . We use 300 Dimensional Glove Common Crawl Embeddings (840B Token) BIBREF11 and fine tune them for the task. We experimented extensively with pre-processing variants and our results showed better performance without lemmatization and lower-casing (see supplement for details). We pad each input to 50 words. We train using RMSprop with a learning rate of .001 and a batch size of 512. We add dropout with a drop rate of 0.1 in the final layer to reduce overfitting BIBREF12 , batch size, and input length empirically through random hyperparameter search.", + "All of our results are produced from 10-fold cross validation to allow comparison with previous results. We trained a logistic regression baseline model (line 1 in Table TABREF10 ) using character ngrams and word unigrams using TF*IDF weighting BIBREF13 , to provide a baseline since HAR has no reported results. For the SR and HATE datasets, the authors reported their trained best logistic regression model's results on their respective datasets.", + "SR: Sexist/Racist BIBREF3 , HATE: Hate BIBREF4 HAR: Harassment BIBREF9 " + ], + [ + "The approach we have developed establishes a new state of the art for classifying hate speech, outperforming previous results by as much as 12 F1 points. Table TABREF10 illustrates the robustness of our method, which often outperform previous results, measured by weighted F1. ", + "Using the Approximate Randomization (AR) Test BIBREF14 , we perform significance testing using a 75/25 train and test split", + "to compare against BIBREF3 and BIBREF4 , whose models we re-implemented. We found 0.001 significance compared to both methods. We also include in-depth precision and recall results for all three datasets in the supplement.", + "Our results indicate better performance than several more complex approaches, including BIBREF4 's best model (which used word and part-of-speech ngrams, sentiment, readability, text, and Twitter specific features), BIBREF6 (which used two fold classification and a hybrid of word and character CNNs, using approximately twice the parameters we use excluding the word embeddings) and even recent work by BIBREF8 , (whose best model relies on GRUs, metadata including popularity, network reciprocity, and subscribed lists).", + "On the SR dataset, we outperform BIBREF8 's text based model by 3 F1 points, while just falling short of the Text + Metadata Interleaved Training model. While we appreciate the potential added value of metadata, we believe a tweet-only classifier has merits because retrieving features from the social graph is not always tractable in production settings. Excluding the embedding weights, our model requires 100k parameters , while BIBREF8 requires 250k parameters." + ], + [ + "False negatives", + "Many of the false negatives we see are specific references to characters in the TV show \u201cMy Kitchen Rules\u201d, rather than something about women in general. Such examples may be innocuous in isolation but could potentially be sexist or racist in context. While this may be a limitation of considering only the content of the tweet, it could also be a mislabel.", + "Debra are now my most hated team on #mkr after least night's ep. Snakes in the grass those two.", + "Along these lines, we also see correct predictions of innocuous speech, but find data mislabeled as hate speech:", + "@LoveAndLonging ...how is that example \"sexism\"?", + "@amberhasalamb ...in what way?", + "Another case our classifier misses is problematic speech within a hashtag:", + ":D @nkrause11 Dudes who go to culinary school: #why #findawife #notsexist :)", + "This limitation could be potentially improved through the use of character convolutions or subword tokenization.", + "False Positives", + "In certain cases, our model seems to be learning user names instead of semantic content:", + "RT @GrantLeeStone: @MT8_9 I don't even know what that is, or where it's from. Was that supposed to be funny? It wasn't.", + "Since the bulk of our model's weights are in the embedding and embedding-transformation matrices, we cluster the SR vocabulary using these transformed embeddings to clarify our intuitions about the model ( TABREF14 ). We elaborate on our clustering approach in the supplement. We see that the model learned general semantic groupings of words associated with hate speech as well as specific idiosyncrasies related to the dataset itself (e.g. katieandnikki)" + ], + [ + "Despite minimal tuning of hyper-parameters, fewer weight parameters, minimal text preprocessing, and no additional metadata, the model performs remarkably well on standard hate speech datasets. Our clustering analysis adds interpretability enabling inspection of results.", + "Our results indicate that the majority of recent deep learning models in hate speech may rely on word embeddings for the bulk of predictive power and the addition of sequence-based parameters provide minimal utility. Sequence based approaches are typically important when phenomena such as negation, co-reference, and context-dependent phrases are salient in the text and thus, we suspect these cases are in the minority for publicly available datasets. We think it would be valuable to study the occurrence of such linguistic phenomena in existing datasets and construct new datasets that have a better representation of subtle forms of hate speech. In the future, we plan to investigate character based representations, using character CNNs and highway layers BIBREF15 along with word embeddings to allow robust representations for sparse words such as hashtags." + ], + [ + "We experimented with several different preprocessing variants and were surprised to find that reducing preprocessing improved the performance on the task for all of our tasks. We go through each preprocessing variant with an example and then describe our analysis to compare and evaluate each of them." + ], + [ + "Original text", + "RT @AGuyNamed_Nick Now, I'm not sexist in any way shape or form but I think women are better at gift wrapping. It's the XX chromosome thing", + "Tokenize (Basic Tokenize: Keeps case and words intact with limited sanitizing)", + "RT @AGuyNamed_Nick Now , I 'm not sexist in any way shape or form but I think women are better at gift wrapping . It 's the XX chromosome thing", + "Tokenize Lowercase: Lowercase the basic tokenize scheme", + "rt @aguynamed_nick now , i 'm not sexist in any way shape or form but i think women are better at gift wrapping . it 's the xx chromosome thing", + "Token Replace: Replaces entities and user names with placeholder)", + "ENT USER now , I 'm not sexist in any way shape or form but I think women are better at gift wrapping . It 's the xx chromosome thing", + "Token Replace Lowercase: Lowercase the Token Replace Scheme", + "ENT USER now , i 'm not sexist in any way shape or form but i think women are better at gift wrapping . it 's the xx chromosome thing", + "We did analysis on a validation set across multiple datasets to find that the \"Tokenize\" scheme was by far the best. We believe that keeping the case in tact provides useful information about the user. For example, saying something in all CAPS is a useful signal that the model can take advantage of." + ], + [ + "Since our method was a simple word embedding based model, we explored the learned embedding space to analyze results. For this analysis, we only use the max pooling part of our architecture to help analyze the learned embedding space because it encourages salient words to increase their values to be selected. We projected the original pre-trained embeddings to the learned space using the time distributed MLP. We summed the embedding dimensions for each word and sorted by the sum in descending order to find the 1000 most salient word embeddings from our vocabulary. We then ran PCA BIBREF16 to reduce the dimensionality of the projected embeddings from 300 dimensions to 75 dimensions. This captured about 60% of the variance. Finally, we ran K means clustering for INLINEFORM0 clusters to organize the most salient embeddings in the projected space.", + "The learned clusters from the SR vocabulary were very illuminating (see Table TABREF14 ); they gave insights to how hate speech surfaced in the datasets. One clear grouping we found is the misogynistic and pornographic group, which contained words like breasts, blonds, and skank. Two other clusters had references to geopolitical and religious issues in the Middle East and disparaging and resentful epithets that could be seen as having an intellectual tone. This hints towards the subtle pedagogic forms of hate speech that surface. We ran silhouette analysis BIBREF17 on the learned clusters to find that the clusters from the learned representations had a 35% higher silhouette coefficient using the projected embeddings compared to the clusters created from the original pre-trained embeddings. This reinforces the claim that our training process pushed hate-speech related words together, and words from other clusters further away, thus, structuring the embedding space effectively for detecting hate speech." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0629/instruction.md b/qasper-0629/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..f981bd611b13212390df1a1b9b6f44aa6fd2e0b3 --- /dev/null +++ b/qasper-0629/instruction.md @@ -0,0 +1,108 @@ +Name of Paper: Predictive Embeddings for Hate Speech Detection on Twitter + +Question: What embedding algorithm and dimension size are used? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work", + "Data", + "Transformed Word Embedding Model (TWEM)", + "Word Embeddings", + "Pooling", + "Output", + "Experimental Setup", + "Results and Discussion", + "Error Analysis", + "Conclusion", + "Supplemental Material", + "Preprocessing", + "Embedding Analysis" + ], + "paragraphs": [ + [ + "The increasing popularity of social media platforms like Twitter for both personal and political communication BIBREF0 has seen a well-acknowledged rise in the presence of toxic and abusive speech on these platforms BIBREF1 , BIBREF2 . Although the terms of services on these platforms typically forbid hateful and harassing speech, enforcing these rules has proved challenging, as identifying hate speech speech at scale is still a largely unsolved problem in the NLP community. BIBREF3 , for example, identify many ambiguities in classifying abusive communications, and highlight the difficulty of clearly defining the parameters of such speech. This problem is compounded by the fact that identifying abusive or harassing speech is a challenge for humans as well as automated systems.", + "Despite the lack of consensus around what constitutes abusive speech, some definition of hate speech must be used to build automated systems to address it. We rely on BIBREF4 's definition of hate speech, specifically: \u201clanguage that is used to express hatred towards a targeted group or is intended to be derogatory, to humiliate, or to insult the members of the group.\u201d", + "In this paper, we present a neural classification system that uses minimal preprocessing to take advantage of a modified Simple Word Embeddings-based Model BIBREF5 to predict the occurrence of hate speech. Our classifier features:", + "In the following sections, we discuss related work on hate speech classification, followed by a description of the datasets, methods and results of our study." + ], + [ + "Many efforts have been made to classify hate speech using data scraped from online message forums and popular social media sites such as Twitter and Facebook. BIBREF3 applied a logistic regression model that used one- to four-character n-grams for classification of tweets labeled as racist, sexist or neither. BIBREF4 experimented in classification of hateful as well as offensive but not hateful tweets. They applied a logistic regression classifier with L2 regularization using word level n-grams and various part-of-speech, sentiment, and tweet-level metadata features.", + "Additional projects have built upon the data sets created by Waseem and/or Davidson. For example, BIBREF6 used a neural network approach with two binary classifiers: one to predict the presence abusive speech more generally, and another to discern the form of abusive speech.", + " BIBREF7 , meanwhile, used pre-trained word2vec embeddings, which were then fed into a convolutional neural network (CNN) with max pooling to produce input vectors for a Gated Recurrent Unit (GRU) neural network. Other researchers have experimented with using metadata features from tweets. BIBREF8 built a classifier composed of two separate neural networks, one for the text and the other for metadata of the Twitter user, that were trained jointly in interleaved fashion. Both networks used in combination - and especially when trained using transfer learning - achieved higher F1 scores than either neural network classifier alone.", + "In contrast to the methods described above, our approach relies on a simple word embedding (SWEM)-based architecture BIBREF5 , reducing the number of required parameters and length of training required, while still yielding improved performance and resilience across related classification tasks. Moreover, our network is able to learn flexible vector representations that demonstrate associations among words typically used in hateful communication. Finally, while metadata-based augmentation is intriguing, here we sought to develop an approach that would function well even in cases where such additional data was missing due to the deletion, suspension, or deactivation of accounts." + ], + [ + "In this paper, we use three data sets from the literature to train and evaluate our own classifier. Although all address the category of hateful speech, they used different strategies of labeling the collected data. Table TABREF5 shows the characteristics of the datasets.", + "Data collected by BIBREF3 , which we term the Sexist/Racist (SR) data set, was collected using an initial Twitter search followed by analysis and filtering by the authors and their team who identified 17 common phrases, hashtags, and users that were indicative of abusive speech. BIBREF4 collected the HATE dataset by searching for tweets using a lexicon provided by Hatebase.org. The final data set we used, which we call HAR, was collected by BIBREF9 ; we removed all retweets reducing the dataset to 20,000 tweets. Tweets were labeled as \u201cHarrassing\u201d or \u201cNon-Harrassing\u201d; hate speech was not explicitly labeled, but treated as an unlabeled subset of the broader \u201cHarrassing\u201d category BIBREF9 ." + ], + [ + "Our training set consists of INLINEFORM0 examples INLINEFORM1 where the input INLINEFORM2 is a sequence of tokens INLINEFORM3 , and the output INLINEFORM4 is the numerical class for the hate speech class. Each input instance represents a Twitter post and thus, is not limited to a single sentence.", + "We modify the SWEM-concat BIBREF5 architecture to allow better handling of infrequent and unknown words and to capture non-linear word combinations." + ], + [ + "Each token in the input is mapped to an embedding. We used the 300 dimensional embeddings for all our experiments, so each word INLINEFORM0 is mapped to INLINEFORM1 . We denote the full embedded sequence as INLINEFORM2 . We then transform each word embedding by applying 300 dimensional 1-layer Multi Layer Perceptron (MLP) INLINEFORM3 with a Rectified Liner Unit (ReLU) activation to form an updated embedding space INLINEFORM4 . We find this better handles unseen or rare tokens in our training data by projecting the pretrained embedding into a space that the encoder can understand." + ], + [ + "We make use of two pooling methods on the updated embedding space INLINEFORM0 . We employ a max pooling operation on INLINEFORM1 to capture salient word features from our input; this representation is denoted as INLINEFORM2 . This forces words that are highly indicative of hate speech to higher positive values within the updated embedding space. We also average the embeddings INLINEFORM3 to capture the overall meaning of the sentence, denoted as INLINEFORM4 , which provides a strong conditional factor in conjunction with the max pooling output. This also helps regularize gradient updates from the max pooling operation." + ], + [ + "We concatenate INLINEFORM0 and INLINEFORM1 to form a document representation INLINEFORM2 and feed the representation into a 50 node 2 layer MLP followed by ReLU Activation to allow for increased nonlinear representation learning. This representation forms the preterminal layer and is passed to a fully connected softmax layer whose output is the probability distribution over labels." + ], + [ + "We tokenize the data using Spacy BIBREF10 . We use 300 Dimensional Glove Common Crawl Embeddings (840B Token) BIBREF11 and fine tune them for the task. We experimented extensively with pre-processing variants and our results showed better performance without lemmatization and lower-casing (see supplement for details). We pad each input to 50 words. We train using RMSprop with a learning rate of .001 and a batch size of 512. We add dropout with a drop rate of 0.1 in the final layer to reduce overfitting BIBREF12 , batch size, and input length empirically through random hyperparameter search.", + "All of our results are produced from 10-fold cross validation to allow comparison with previous results. We trained a logistic regression baseline model (line 1 in Table TABREF10 ) using character ngrams and word unigrams using TF*IDF weighting BIBREF13 , to provide a baseline since HAR has no reported results. For the SR and HATE datasets, the authors reported their trained best logistic regression model's results on their respective datasets.", + "SR: Sexist/Racist BIBREF3 , HATE: Hate BIBREF4 HAR: Harassment BIBREF9 " + ], + [ + "The approach we have developed establishes a new state of the art for classifying hate speech, outperforming previous results by as much as 12 F1 points. Table TABREF10 illustrates the robustness of our method, which often outperform previous results, measured by weighted F1. ", + "Using the Approximate Randomization (AR) Test BIBREF14 , we perform significance testing using a 75/25 train and test split", + "to compare against BIBREF3 and BIBREF4 , whose models we re-implemented. We found 0.001 significance compared to both methods. We also include in-depth precision and recall results for all three datasets in the supplement.", + "Our results indicate better performance than several more complex approaches, including BIBREF4 's best model (which used word and part-of-speech ngrams, sentiment, readability, text, and Twitter specific features), BIBREF6 (which used two fold classification and a hybrid of word and character CNNs, using approximately twice the parameters we use excluding the word embeddings) and even recent work by BIBREF8 , (whose best model relies on GRUs, metadata including popularity, network reciprocity, and subscribed lists).", + "On the SR dataset, we outperform BIBREF8 's text based model by 3 F1 points, while just falling short of the Text + Metadata Interleaved Training model. While we appreciate the potential added value of metadata, we believe a tweet-only classifier has merits because retrieving features from the social graph is not always tractable in production settings. Excluding the embedding weights, our model requires 100k parameters , while BIBREF8 requires 250k parameters." + ], + [ + "False negatives", + "Many of the false negatives we see are specific references to characters in the TV show \u201cMy Kitchen Rules\u201d, rather than something about women in general. Such examples may be innocuous in isolation but could potentially be sexist or racist in context. While this may be a limitation of considering only the content of the tweet, it could also be a mislabel.", + "Debra are now my most hated team on #mkr after least night's ep. Snakes in the grass those two.", + "Along these lines, we also see correct predictions of innocuous speech, but find data mislabeled as hate speech:", + "@LoveAndLonging ...how is that example \"sexism\"?", + "@amberhasalamb ...in what way?", + "Another case our classifier misses is problematic speech within a hashtag:", + ":D @nkrause11 Dudes who go to culinary school: #why #findawife #notsexist :)", + "This limitation could be potentially improved through the use of character convolutions or subword tokenization.", + "False Positives", + "In certain cases, our model seems to be learning user names instead of semantic content:", + "RT @GrantLeeStone: @MT8_9 I don't even know what that is, or where it's from. Was that supposed to be funny? It wasn't.", + "Since the bulk of our model's weights are in the embedding and embedding-transformation matrices, we cluster the SR vocabulary using these transformed embeddings to clarify our intuitions about the model ( TABREF14 ). We elaborate on our clustering approach in the supplement. We see that the model learned general semantic groupings of words associated with hate speech as well as specific idiosyncrasies related to the dataset itself (e.g. katieandnikki)" + ], + [ + "Despite minimal tuning of hyper-parameters, fewer weight parameters, minimal text preprocessing, and no additional metadata, the model performs remarkably well on standard hate speech datasets. Our clustering analysis adds interpretability enabling inspection of results.", + "Our results indicate that the majority of recent deep learning models in hate speech may rely on word embeddings for the bulk of predictive power and the addition of sequence-based parameters provide minimal utility. Sequence based approaches are typically important when phenomena such as negation, co-reference, and context-dependent phrases are salient in the text and thus, we suspect these cases are in the minority for publicly available datasets. We think it would be valuable to study the occurrence of such linguistic phenomena in existing datasets and construct new datasets that have a better representation of subtle forms of hate speech. In the future, we plan to investigate character based representations, using character CNNs and highway layers BIBREF15 along with word embeddings to allow robust representations for sparse words such as hashtags." + ], + [ + "We experimented with several different preprocessing variants and were surprised to find that reducing preprocessing improved the performance on the task for all of our tasks. We go through each preprocessing variant with an example and then describe our analysis to compare and evaluate each of them." + ], + [ + "Original text", + "RT @AGuyNamed_Nick Now, I'm not sexist in any way shape or form but I think women are better at gift wrapping. It's the XX chromosome thing", + "Tokenize (Basic Tokenize: Keeps case and words intact with limited sanitizing)", + "RT @AGuyNamed_Nick Now , I 'm not sexist in any way shape or form but I think women are better at gift wrapping . It 's the XX chromosome thing", + "Tokenize Lowercase: Lowercase the basic tokenize scheme", + "rt @aguynamed_nick now , i 'm not sexist in any way shape or form but i think women are better at gift wrapping . it 's the xx chromosome thing", + "Token Replace: Replaces entities and user names with placeholder)", + "ENT USER now , I 'm not sexist in any way shape or form but I think women are better at gift wrapping . It 's the xx chromosome thing", + "Token Replace Lowercase: Lowercase the Token Replace Scheme", + "ENT USER now , i 'm not sexist in any way shape or form but i think women are better at gift wrapping . it 's the xx chromosome thing", + "We did analysis on a validation set across multiple datasets to find that the \"Tokenize\" scheme was by far the best. We believe that keeping the case in tact provides useful information about the user. For example, saying something in all CAPS is a useful signal that the model can take advantage of." + ], + [ + "Since our method was a simple word embedding based model, we explored the learned embedding space to analyze results. For this analysis, we only use the max pooling part of our architecture to help analyze the learned embedding space because it encourages salient words to increase their values to be selected. We projected the original pre-trained embeddings to the learned space using the time distributed MLP. We summed the embedding dimensions for each word and sorted by the sum in descending order to find the 1000 most salient word embeddings from our vocabulary. We then ran PCA BIBREF16 to reduce the dimensionality of the projected embeddings from 300 dimensions to 75 dimensions. This captured about 60% of the variance. Finally, we ran K means clustering for INLINEFORM0 clusters to organize the most salient embeddings in the projected space.", + "The learned clusters from the SR vocabulary were very illuminating (see Table TABREF14 ); they gave insights to how hate speech surfaced in the datasets. One clear grouping we found is the misogynistic and pornographic group, which contained words like breasts, blonds, and skank. Two other clusters had references to geopolitical and religious issues in the Middle East and disparaging and resentful epithets that could be seen as having an intellectual tone. This hints towards the subtle pedagogic forms of hate speech that surface. We ran silhouette analysis BIBREF17 on the learned clusters to find that the clusters from the learned representations had a 35% higher silhouette coefficient using the projected embeddings compared to the clusters created from the original pre-trained embeddings. This reinforces the claim that our training process pushed hate-speech related words together, and words from other clusters further away, thus, structuring the embedding space effectively for detecting hate speech." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0642/instruction.md b/qasper-0642/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..124d752cd99a6baadfa2c31ca6c3088d343b989e --- /dev/null +++ b/qasper-0642/instruction.md @@ -0,0 +1,95 @@ +Name of Paper: Crowdsourcing a High-Quality Gold Standard for QA-SRL + +Question: How are workers trained? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Background \u2014 QA-SRL ::: Specifications", + "Background \u2014 QA-SRL ::: Corpora", + "Annotation and Evaluation Methods ::: Crowdsourcing Methodology ::: Screening and Training", + "Annotation and Evaluation Methods ::: Crowdsourcing Methodology ::: Annotation", + "Annotation and Evaluation Methods ::: Crowdsourcing Methodology ::: Guidelines Refinements", + "Annotation and Evaluation Methods ::: Crowdsourcing Methodology ::: Data & Cost", + "Annotation and Evaluation Methods ::: Evaluation Metrics", + "Annotation and Evaluation Methods ::: Evaluation Metrics ::: Evaluating Redundant Annotations", + "Dataset Quality Analysis ::: Inter-Annotator Agreement (IAA)", + "Dataset Quality Analysis ::: Dataset Assessment and Comparison", + "Dataset Quality Analysis ::: Agreement with PropBank Data", + "Baseline Parser Evaluation", + "Baseline Parser Evaluation ::: Error Analysis", + "Conclusion", + "Supplemental Material ::: The Question Template", + "Supplemental Material ::: Annotation Pipeline", + "Supplemental Material ::: Redundant Parser Output" + ], + "paragraphs": [ + [ + "Semantic Role Labeling (SRL) provides explicit annotation of predicate-argument relations, which have been found useful in various downstream tasks BIBREF0, BIBREF1, BIBREF2, BIBREF3. Question-Answer driven Semantic Role Labeling (QA-SRL) BIBREF4 is an SRL scheme in which roles are captured by natural language questions, while arguments represent their answers, making the annotations intuitive, semantically rich, and easily attainable by laymen. For example, in Table TABREF4, the question Who cut something captures the traditional \u201cagent\u201d role.", + "Previous attempts to annotate QA-SRL initially involved trained annotators BIBREF4 but later resorted to crowdsourcing BIBREF5 to achieve scalability. Naturally, employing crowd workers raises challenges when annotating semantic structures like SRL. As BIBREF5 acknowledged, the main shortage of the large-scale 2018 dataset is the lack of recall, estimated by experts to be in the lower 70s.", + "In light of this and other annotation inconsistencies, we propose an improved QA-SRL crowdsourcing protocol for high-quality annotation, allowing for substantially more reliable performance evaluation of QA-SRL parsers. To address worker quality, we systematically screen workers, provide concise yet effective guidelines, and perform a short training procedure, all within a crowd-sourcing platform. To address coverage, we employ two independent workers plus an additional one for consolidation \u2014 similar to conventional expert-annotation practices. In addition to yielding 25% more roles, our coverage gain is demonstrated by evaluating against expertly annotated data and comparison with PropBank (Section SECREF4). To foster future research, we release an assessed high-quality gold dataset along with our reproducible protocol and evaluation scheme, and report the performance of the existing parser BIBREF5 as a baseline." + ], + [ + "In QA-SRL, a role question adheres to a 7-slot template, with slots corresponding to a WH-word, the verb, auxiliaries, argument placeholders (SUBJ, OBJ), and prepositions, where some slots are optional BIBREF4 (see appendix for examples). Such question captures the corresponding semantic role with a natural easily understood expression. The set of all non-overlapping answers for the question is then considered as the set of arguments associated with that role. This broad question-based definition of roles captures traditional cases of syntactically-linked arguments, but also additional semantic arguments clearly implied by the sentence meaning (see example (2) in Table TABREF4)." + ], + [ + "The original 2015 QA-SRL dataset BIBREF4 was annotated by non-expert workers after completing a brief training procedure. They annotated 7.8K verbs, reporting an average of 2.4 QA pairs per predicate. Even though multiple annotators were shown to produce greater coverage, their released dataset was produced using only a single annotator per verb. In subsequent work, BIBREF5 constructed a large-scale corpus and used it to train a parser. They crowdsourced 133K verbs with 2.0 QA pairs per verb on average. Since crowd-workers had no prior training, quality was established using an additional validation step, where workers had to ascertain the validity of the question, but not of its answers. Instead, the validator provided additional answers, independent of the other annotators. Each verb in the corpus was annotated by a single QA-generating worker and validated by two others.", + "In a reserved part of the corpus (Dense), targeted for parser evaluation, verbs were densely validated with 5 workers, approving questions judged as valid by at least 4/5 validators. Notably, adding validators to the Dense annotation pipeline accounts mostly for precision errors, while role coverage solely relies upon the single generator's set of questions. As both 2015 and 2018 datasets use a single question generator, both struggle with maintaining coverage. Also noteworthy, is that while traditional SRL annotations contain a single authoritative and non-redundant annotation, the 2018 dataset provides the raw annotations of all annotators. These include many overlapping or noisy answers, without settling on consolidation procedures to provide a single gold reference.", + "We found that these characteristics of the dataset impede its utility for future development of parsers." + ], + [ + "Our pool of annotators is selected after several short training rounds, with up to 15 predicates per round, in which they received extensive personal feedback. 1 out of 3 participants were selected after exhibiting good performance, tested against expert annotations." + ], + [ + "We adopt the annotation machinery of BIBREF5 implemented using Amazon's Mechanical Turk, and annotate each predicate by 2 trained workers independently, while a third consolidates their annotations into a final set of roles and arguments. In this consolidation task, the worker validates questions, merges, splits or modifies answers for the same role according to guidelines, and removes redundant roles by picking the more naturally phrased questions. For example, in Table TABREF4 ex. 1, one worker could have chosen \u201c47 people\u201d, while another chose \u201cthe councillor\u201d; in this case the consolidator would include both of those answers. In Section SECREF4, we show that this process yields better coverage. For example annotations, please refer to the appendix." + ], + [ + "We refine the previous guidelines by emphasizing several semantic features: correctly using modal verbs and negations in the question, and choosing answers that coincide with a single entity (example 1 in Table TABREF4)." + ], + [ + "We annotated a sample taken from the Dense set on Wikinews and Wikipedia domains, each with 1000 sentences, equally divided between development and test. QA generating annotators are paid the same as in fitz2018qasrl, while the consolidator is rewarded 5\u00a2 per verb and 3\u00a2 per question. Per predicate, on average, our cost is 54.2\u00a2, yielding 2.9 roles, compared to reported 2.3 valid roles with an approximated cost of 51\u00a2 per predicate for Dense." + ], + [ + "Evaluation in QA-SRL involves aligning predicted and ground truth argument spans and evaluating role label equivalence. Since detecting question paraphrases is still an open challenge, we propose both unlabeled and labeled evaluation metrics.", + "Unlabeled Argument Detection (UA) Inspired by the method presented in BIBREF5, arguments are matched using a span matching criterion of intersection over union $\\ge 0.5$ . To credit each argument only once, we employ maximal bipartite matching between the two sets of arguments, drawing an edge for each pair that passes the above mentioned criterion. The resulting maximal matching determines the true-positive set, while remaining non-aligned arguments become false-positives or false-negatives.", + "Labeled Argument Detection (LA) All aligned arguments from the previous step are inspected for label equivalence, similar to the joint evaluation reported in BIBREF5. There may be many correct questions for a role. For example, What was given to someone? and What has been given by someone? both refer to the same semantic role but diverge in grammatical tense, voice, and presence of a syntactical object or subject. Aiming to avoid judging non-equivalent roles as equivalent, we propose Strict-Match to be an equivalence on the following template slots: WH, SUBJ, OBJ, as well as on negation, voice, and modality extracted from the question. Final reported numbers on labelled argument detection rates are based on bipartite aligned arguments passing Strict-Match. We later manually estimate the rate of correct equivalences missed by this conservative method.", + "As we will see, our evaluation heuristics, adapted from those in BIBREF5, significantly underestimate agreement between annotations, hence reflecting performance lower bounds. Devising more tight evaluation measures remains a challenge for future research." + ], + [ + "We extend our metric for evaluating manual or automatic redundant annotations, like the Dense dataset or the parser in BIBREF5, which predicts argument spans independently of each other. To that end, we ignore predicted arguments that match ground-truth but are not selected by the bipartite matching due to redundancy. After connecting unmatched predicted arguments that overlap, we count one false positive for every connected component to avoid penalizing precision too harshly when predictions are redundant." + ], + [ + "To estimate dataset consistency across different annotations, we measure F1 using our UA metric with 5 generators per predicate. Individual worker-vs-worker agreement yields 79.8 F1 over 10 experiments with 150 predicates, indicating high consistency across our annotators, inline with results by other structured semantic annotations (e.g. BIBREF6). Overall consistency of the dataset is assessed by measuring agreement between different consolidated annotations, obtained by disjoint triplets of workers, which achieves F1 of 84.1 over 4 experiments, each with 35 distinct predicates. Notably, consolidation boosts agreement, suggesting it is a necessity for semantic annotation consistency." + ], + [ + "We assess both our gold standard set and the recent Dense set against an integrated expert annotated sample of 100 predicates. To construct the expert set, we blindly merged the Dense set with our worker annotations and manually corrected them. We further corrected the evaluation decisions, accounting for some automatic evaluation mistakes introduced by the span-matching and question paraphrasing criteria. As seen in Table TABREF19, our gold set yields comparable precision with significantly higher recall, which is in line with our 25% higher yield.", + "Examining disagreements between our gold and Dense, we observe that our workers successfully produced more roles, both implied and explicit. To a lesser extent, they split more arguments into independent answers, as emphasized by our guidelines, an issue which was left under-specified in the previous annotation guidelines." + ], + [ + "It is illuminating to observe the agreement between QA-SRL and PropBank (CoNLL-2009) annotations BIBREF7. In Table TABREF22, we replicate the experiments in BIBREF4 for both our gold set and theirs, over a sample of 200 sentences from Wall Street Journal (agreement evaluation is automatic and the metric is somewhat similar to our UA). We report macro-averaged (over predicates) precision and recall for all roles, including core and adjuncts, while considering the PropBank data as the reference set. Our recall of the PropBank roles is notably high, reconfirming the coverage obtained by our annotation protocol.", + "The measured precision with respect to PropBank is low for adjuncts due to the fact that our annotators were capturing many correct arguments not covered in PropBank. To examine this, we analyzed 100 false positive arguments. Only 32 of those were due to wrong or incomplete QA annotations in our gold, while most others were outside of PropBank's scope, capturing either implied arguments or roles not covered in PropBank. Extrapolating from this manual analysis estimates our true precision (on all roles) to be about 91%, which is consistent with the 88% precision figure in Table TABREF19. Compared with 2015, our QA-SRL gold yielded 1593 annotations, with 989 core and 604 adjuncts, while theirs yielded 1315 annotations, 979 core and 336 adjuncts. Overall, the comparison to PropBank reinforces the quality of our gold dataset and shows its better coverage relative to the 2015 dataset." + ], + [ + "To illustrate the effectiveness of our new gold-standard, we use its Wikinews development set to evaluate the currently available parser from BIBREF5. For each predicate, the parser classifies every span for being an argument, independently of the other spans. Unlike many other SRL systems, this policy often produces outputs with redundant arguments (see appendix for examples). Results for 1200 predicates are reported in Table TABREF23, demonstrating reasonable performance along with substantial room for improvement, especially with respect to coverage. As expected, the parser's recall against our gold is substantially lower than the 84.2 recall reported in BIBREF5 against Dense, due to the limited recall of Dense relative to our gold set." + ], + [ + "We sample and evaluate 50 predicates to detect correct argument and paraphrase pairs that are skipped by the IOU and Strict-Match criteria. Based on this inspection, the parser completely misses 23% of the 154 roles present in the gold-data, out of which, 17% are implied. While the parser correctly predicts 82% of non-implied roles, it skips half of the implied ones." + ], + [ + "We introduced a refined crowdsourcing pipeline and a corresponding evaluation methodology for QA-SRL. It enabled us to release a new gold standard for evaluations, notably of much higher coverage of core and implied roles than the previous Dense evaluation dataset. We believe that our annotation methodology and dataset would facilitate future research on natural semantic annotations and QA-SRL parsing." + ], + [ + "For completeness, we include several examples with some questions restructured into its 7 template slots in Table TABREF26" + ], + [ + "As described in section 3 The consolidator receives two sets of QA annotations and merges them according to the guidelines to produce an exhaustive and consistent QA set. See Table TABREF28 for examples." + ], + [ + "As mentioned in the paper body, the Fitzgerald et al. parser generates redundant role questions and answers. The first two rows in Table TABREF30 illustrate different, partly redundant, argument spans for the same question. The next two rows illustrate two paraphrased questions for the same role. Generating such redundant output might complicate downstream use of the parser output as well as evaluation methodology." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0645/instruction.md b/qasper-0645/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..08a4cdaecc6e2d42fb59dbda89be9a10c5ae3e3c --- /dev/null +++ b/qasper-0645/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Crowdsourcing a High-Quality Gold Standard for QA-SRL + +Question: How big is the dataset? \ No newline at end of file diff --git a/qasper-0673/instruction.md b/qasper-0673/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..5c77ff9590ffe87e18e72917b7c4a3c9699b9807 --- /dev/null +++ b/qasper-0673/instruction.md @@ -0,0 +1,118 @@ +Name of Paper: Lattice CNNs for Matching Based Chinese Question Answering + +Question: Which dataset(s) do they evaluate on? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Lattice CNNs", + "Siamese Architecture", + "Word Lattice", + "Lattice based CNN Layer", + "Experiments", + "Datasets", + "Evaluation Metrics", + "Implementation Details", + "Baselines", + "Results", + "Analysis and Discussions", + "Case Study", + "Related Work", + "Conclusions", + "Acknowledgments" + ], + "paragraphs": [ + [ + "Short text matching plays a critical role in many natural language processing tasks, such as question answering, information retrieval, and so on. However, matching text sequences for Chinese or similar languages often suffers from word segmentation, where there are often no perfect Chinese word segmentation tools that suit every scenario. Text matching usually requires to capture the relatedness between two sequences in multiple granularities. For example, in Figure FIGREF4 , the example phrase is generally tokenized as \u201cChina \u2013 citizen \u2013 life \u2013 quality \u2013 high\u201d, but when we plan to match it with \u201cChinese \u2013 live \u2013 well\u201d, it would be more helpful to have the example segmented into \u201cChinese \u2013 livelihood \u2013 live\u201d than its common segmentation. ", + "Existing efforts use neural network models to improve the matching based on the fact that distributed representations can generalize discrete word features in traditional bag-of-words methods. And there are also works fusing word level and character level information, which, to some extent, could relieve the mismatch between different segmentations, but these solutions still suffer from the original word sequential structures. They usually depend on an existing word tokenization, which has to make segmentation choices at one time, e.g., \u201cZhongGuo\u201d(China) and \u201cZhongGuoRen\u201d(Chinese) when processing \u201cZhongGuoRenMin\u201d(Chinese people). And the blending just conducts at one position in their frameworks.", + "Specific tasks such as question answering (QA) could pose further challenges for short text matching. In document based question answering (DBQA), the matching degree is expected to reflect how likely a sentence can answer a given question, where questions and candidate answer sentences usually come from different sources, and may exhibit significantly different styles or syntactic structures, e.g. queries in web search and sentences in web pages. This could further aggravate the mismatch problems. In knowledge based question answering (KBQA), one of the key tasks is to match relational expressions in questions with knowledge base (KB) predicate phrases, such as \u201cZhuCeDi\u201d(place of incorporation). Here the diversity between the two kinds of expressions is even more significant, where there may be dozens of different verbal expressions in natural language questions corresponding to only one KB predicate phrase. Those expression problems make KBQA a further tough task. Previous works BIBREF0 , BIBREF1 adopt letter-trigrams for the diverse expressions, which is similar to character level of Chinese. And the lattices are combinations of words and characters, so with lattices, we can utilize words information at the same time.", + "Recent advances have put efforts in modeling multi-granularity information for matching. BIBREF2 , BIBREF3 blend words and characters to a simple sequence (in word level), and BIBREF4 utilize multiple convoluational kernel sizes to capture different n-grams. But most characters in Chinese can be seen as words on their own, so combining characters with corresponding words directly may lose the meanings that those characters can express alone. Because of the sequential inputs, they will either lose word level information when conducting on character sequences or have to make segmentation choices.", + "In this paper, we propose a multi-granularity method for short text matching in Chinese question answering which utilizes lattice based CNNs to extract sentence level features over word lattice. Specifically, instead of relying on character or word level sequences, LCNs take word lattices as input, where every possible word and character will be treated equally and have their own context so that they can interact at every layer. For each word in each layer, LCNs can capture different context words in different granularity via pooling methods. To the best of our knowledge, we are the first to introduce word lattice into the text matching tasks. Because of the similar IO structures to original CNNs and the high efficiency, LCNs can be easily adapted to more scenarios where flexible sentence representation modeling is required.", + "We evaluate our LCNs models on two question answering tasks, document based question answering and knowledge based question answering, both in Chinese. Experimental results show that LCNs significantly outperform the state-of-the-art matching methods and other competitive CNNs baselines in both scenarios. We also find that LCNs can better capture the multi-granularity information from plain sentences, and, meanwhile, maintain better de-noising capability than vanilla graphic convolutional neural networks thanks to its dynamic convolutional kernels and gated pooling mechanism." + ], + [ + "Our Lattice CNNs framework is built upon the siamese architecture BIBREF5 , one of the most successful frameworks in text matching, which takes the word lattice format of a pair of sentences as input, and outputs the matching score." + ], + [ + "The siamese architecture and its variant have been widely adopted in sentence matching BIBREF6 , BIBREF3 and matching based question answering BIBREF7 , BIBREF0 , BIBREF8 , that has a symmetrical component to extract high level features from different input channels, which share parameters and map inputs to the same vector space. Then, the sentence representations are merged and compared to output the similarities.", + "For our models, we use multi-layer CNNs for sentence representation. Residual connections BIBREF9 are used between convolutional layers to enrich features and make it easier to train. Then, max-pooling summarizes the global features to get the sentence level representations, which are merged via element-wise multiplication. The matching score is produced by a multi-layer perceptron (MLP) with one hidden layer based on the merged vector. The fusing and matching procedure is formulated as follows: DISPLAYFORM0 ", + "where INLINEFORM0 and INLINEFORM1 are feature vectors of question and candidate (sentence or predicate) separately encoded by CNNs, INLINEFORM2 is the sigmoid function, INLINEFORM3 are parameters, and INLINEFORM4 is element-wise multiplication. The training objective is to minimize the binary cross-entropy loss, defined as: DISPLAYFORM0 ", + "where INLINEFORM0 is the {0,1} label for the INLINEFORM1 training pair.", + "Note that the CNNs in the sentence representation component can be either original CNNs with sequence input or lattice based CNNs with lattice input. Intuitively, in an original CNN layer, several kernels scan every n-gram in a sequence and result in one feature vector, which can be seen as the representation for the center word and will be fed into the following layers. However, each word may have different context words in different granularities in a lattice and may be treated as the center in various kernel spans with same length. Therefore, different from the original CNNs, there could be several feature vectors produced for a given word, which is the key challenge to apply the standard CNNs directly to a lattice input.", + "For the example shown in Figure FIGREF6 , the word \u201ccitizen\u201d is the center word of four text spans with length 3: \u201cChina - citizen - life\u201d, \u201cChina - citizen - alive\u201d, \u201ccountry - citizen - life\u201d, \u201ccountry - citizen - alive\u201d, so four feature vectors will be produced for width-3 convolutional kernels for \u201ccitizen\u201d." + ], + [ + "As shown in Figure FIGREF4 , a word lattice is a directed graph INLINEFORM0 , where INLINEFORM1 represents a node set and INLINEFORM2 represents a edge set. For a sentence in Chinese, which is a sequence of Chinese characters INLINEFORM3 , all of its possible substrings that can be considered as words are treated as vertexes, i.e. INLINEFORM4 . Then, all neighbor words are connected by directed edges according to their positions in the original sentence, i.e. INLINEFORM5 .", + "Here, one of the key issues is how we decide a sequence of characters can be considered as a word. We approach this through an existing lookup vocabulary, which contains frequent words in BaiduBaike. Note that most Chinese characters can be considered as words on their own, thus are included in this vocabulary when they have been used as words on their own in this corpus.", + "However, doing so will inevitably introduce noisy words (e.g., \u201cmiddle\u201d in Figure FIGREF4 ) into word lattices, which will be smoothed by pooling procedures in our model. And the constructed graphs could be disconnected because of a few out-of-vocabulary characters. Thus, we append INLINEFORM0 labels to replace those characters to connect the graph.", + "Obviously, word lattices are collections of characters and all possible words. Therefore, it is not necessary to make explicit decisions regarding specific word segmentations, but just embed all possible information into the lattice and take them to the next CNN layers. The inherent graph structure of a word lattice allows all possible words represented explicitly, no matter the overlapping and nesting cases, and all of them can contribute directly to the sentence representations." + ], + [ + "As we mentioned in previous section, we can not directly apply standard CNNs to take word lattice as input, since there could be multiple feature vectors produced for a given word. Inspired by previous lattice LSTM models BIBREF10 , BIBREF11 , here we propose a lattice based CNN layers to allow standard CNNs to work over word lattice input. Specifically, we utilize pooling mechanisms to merge the feature vectors produced by multiple CNN kernels over different context compositions.", + "Formally, the output feature vector of a lattice CNN layer with kernel size INLINEFORM0 at word INLINEFORM1 in a word lattice INLINEFORM2 can be formulated as Eq EQREF12 : DISPLAYFORM0 ", + "where INLINEFORM0 is the activation function, INLINEFORM1 is the input vector corresponding to word INLINEFORM2 in this layer, ( INLINEFORM3 means the concatenation of these vectors, and INLINEFORM4 are parameters with size INLINEFORM5 , and INLINEFORM6 , respectively. INLINEFORM7 is the input dim and INLINEFORM8 is the output dim. INLINEFORM9 is one of the following pooling functions: max-pooling, ave-pooling, or gated-pooling, which execute the element-wise maximum, element-wise average, and the gated operation, respectively. The gated operation can be formulated as: DISPLAYFORM0 ", + "where INLINEFORM0 are parameters, and INLINEFORM1 are gated weights normalized by a softmax function. Intuitively, the gates represent the importance of the n-gram contexts, and the weighted sum can control the transmission of noisy context words. We perform padding when necessary.", + "For example, in Figure FIGREF6 , when we consider \u201ccitizen\u201d as the center word, and the kernel size is 3, there will be five words and four context compositions involved, as mentioned in the previous section, each marked in different colors. Then, 3 kernels scan on all compositions and produce four 3-dim feature vectors. The gated weights are computed based on those vectors via a dense layer, which can reflect the importance of each context compositions. The output vector of the center word is their weighted sum, where noisy contexts are expected to have lower weights to be smoothed. This pooling over different contexts allows LCNs to work over word lattice input.", + "Word lattice can be seen as directed graphs and modeled by Directed Graph Convolutional networks (DGCs) BIBREF12 , which use poolings on neighboring vertexes that ignore the semantic structure of n-grams. But to some situations, their formulations can be very similar to ours (See Appendix for derivation). For example, if we set the kernel size in LCNs to 3, use linear activations and suppose the pooling mode is average in both LCNs and DGCs, at each word in each layer, the DGCs compute the average of the first order neighbors together with the center word, while the LCNs compute the average of the pre and post words separately and add them to the center word. Empirical results are exhibited in Experiments section.", + "Finally, given a sentence that has been constructed into a word-lattice form, for each node in the lattice, an LCN layer will produce one feature vector similar to original CNNs, which makes it easier to stack multiple LCN layers to obtain more abstract feature representations." + ], + [ + "Our experiments are designed to answer: (1) whether multi-granularity information in word lattice helps in matching based QA tasks, (2) whether LCNs capture the multi-granularity information through lattice well, and (3) how to balance the noisy and informative words introduced by word lattice." + ], + [ + "We conduct experiments on two Chinese question answering datasets from NLPCC-2016 evaluation task BIBREF13 .", + "DBQA is a document based question answering dataset. There are 8.8k questions with 182k question-sentence pairs for training and 6k questions with 123k question-sentence pairs in the test set. In average, each question has 20.6 candidate sentences and 1.04 golden answers. The average length for questions is 15.9 characters, and each candidate sentence has averagely 38.4 characters. Both questions and sentences are natural language sentences, possibly sharing more similar word choices and expressions compared to the KBQA case. But the candidate sentences are extracted from web pages, and are often much longer than the questions, with many irrelevant clauses.", + "KBRE is a knowledge based relation extraction dataset. We follow the same preprocess as BIBREF14 to clean the dataset and replace entity mentions in questions to a special token. There are 14.3k questions with 273k question-predicate pairs in the training set and 9.4k questions with 156k question-predicate pairs for testing. Each question contains only one golden predicate. Each question averagely has 18.1 candidate predicates and 8.1 characters in length, while a KB predicate is only 3.4 characters long on average. Note that a KB predicate is usually a concise phrase, with quite different word choices compared to the natural language questions, which poses different challenges to solve.", + "The vocabulary we use to construct word lattices contains 156k words, including 9.1k single character words. In average, each DBQA question contains 22.3 tokens (words or characters) in its lattice, each DBQA candidate sentence has 55.8 tokens, each KBQA question has 10.7 tokens and each KBQA predicate contains 5.1 tokens." + ], + [ + "For both datasets, we follow the evaluation metrics used in the original evaluation tasks BIBREF13 . For DBQA, P@1 (Precision@1), MAP (Mean Average Precision) and MRR (Mean Reciprocal Rank) are adopted. For KBRE, since only one golden candidate is labeled for each question, only P@1 and MRR are used." + ], + [ + "The word embeddings are trained on the Baidu Baike webpages with Google's word2vector, which are 300-dim and fine tuned during training. In DBQA, we also follow previous works BIBREF15 , BIBREF16 to concatenate additional 1d-indicators with word vectors which denote whether the words are concurrent in both questions and candidate sentences. In each CNN layer, there are 256, 512, and 256 kernels with width 1, 2, and 3, respectively. The size of the hidden layer for MLP is 1024. All activation are ReLU, the dropout rate is 0.5, with a batch size of 64. We optimize with adadelta BIBREF17 with learning rate INLINEFORM0 and decay factor INLINEFORM1 . We only tune the number of convolutional layers from [1, 2, 3] and fix other hyper-parameters. We sample at most 10 negative sentences per question in DBQA and 5 in KBRE. We implement our models in Keras with Tensorflow backend." + ], + [ + "Our first set of baselines uses original CNNs with character (CNN-char) or word inputs. For each sentence, two Chinese word segmenters are used to obtain three different word sequences: jieba (CNN-jieba), and Stanford Chinese word segmenter in CTB (CNN-CTB) and PKU (CNN-PKU) mode.", + "Our second set of baselines combines different word segmentations. Specifically, we concatenate the sentence embeddings from different segment results, which gives four different word+word models: jieba+PKU, PKU+CTB, CTB+jieba, and PKU+CTB+jieba.", + "Inspired by previous works BIBREF2 , BIBREF3 , we also concatenate word and character embeddings at the input level. Specially, when the basic sequence is in word level, each word may be constructed by multiple characters through a pooling operation (Word+Char). Our pilot experiments show that average-pooling is the best for DBQA while max-pooling after a dense layer is the best for KBQA. When the basic sequence is in character level, we simply concatenate the character embedding with its corresponding word embedding (Char+Word), since each character belongs to one word only. Again, when the basic sequence is in character level, we can also concatenate the character embedding with a pooled representation of all words that contain this character in the word lattice (Char+Lattice), where we use max pooling as suggested by our pilot experiments.", + "DGCs BIBREF12 , BIBREF18 are strong baselines that perform CNNs over directed graphs to produce high level representation for each vertex in the graph, which can be used to build a sentence representation via certain pooling operation. We therefore choose to compare with DGC-max (with maximum pooling), DGC-ave (with average pooling), and DGC-gated (with gated pooling), where the gate value is computed using the concatenation of the vertex vector and the center vertex vector through a dense layer. We also implement several state-of-the-art matching models using the open-source project MatchZoo BIBREF19 , where we tune hyper-parameters using grid search, e.g., whether using word or character inputs. Arc1, Arc2, CDSSM are traditional CNNs based matching models proposed by BIBREF20 , BIBREF21 . Arc1 and CDSSM compute the similarity via sentence representations and Arc2 uses the word pair similarities. MV-LSTM BIBREF22 computes the matching score by examining the interaction between the representations from two sentences obtained by a shared BiLSTM encoder. MatchPyramid(MP) BIBREF23 utilizes 2D convolutions and pooling strategies over word pair similarity matrices to compute the matching scores.", + "We also compare with the state-of-the-art models in DBQA BIBREF15 , BIBREF16 ." + ], + [ + "Here, we mainly describe the main results on the DBQA dataset, while we find very similar trends on the KBRE dataset. Table TABREF26 summarizes the main results on the two datasets. We can see that the simple MatchZoo models perform the worst. Although Arc1 and CDSSM are also constructed in the siamese architecture with CNN layers, they do not employ multiple kernel sizes and residual connections, and fail to capture the relatedness in a multi-granularity fashion.", + " BIBREF15 is similar to our word level models (CNN-jieba/PKU/CTB), but outperforms our models by around 3%, since it benefits from an extra interaction layer with fine tuned hyper-parameters. BIBREF16 further incorporates human designed features including POS-tag interaction and TF-IDF scores, achieving state-of-the-art performance in the literature of this DBQA dataset. However, both of them perform worse than our simple CNN-char model, which is a strong baseline because characters, that describe the text in a fine granularity, can relieve word mismatch problem to some extent. And our best LCNs model further outperforms BIBREF16 by .0134 in MRR.", + "For single granularity CNNs, CNN-char performs better than all word level models, because they heavily suffer from word mismatching given one fixed word segmentation result. And the models that utilize different word segmentations can relieve this problem and gain better performance, which can be further improved by the combination of words and characters. The DGCs and LCNs, being able to work on lattice input, outperform all previous models that have sequential inputs, indicating that the word lattice is a more promising form than a single word sequence, and should be better captured by taking the inherent graph structure into account. Although they take the same input, LCNs still perform better than the best DGCs by a margin, showing the advantages of the CNN kernels over multiple n-grams in the lattice structures and the gated pooling strategy.", + "To fairly compare with previous KBQA works, we combine our LCN-ave settings with the entity linking results of the state-of-the-art KBQA model BIBREF14 . The P@1 for question answering of single LCN-ave is 86.31%, which outperforms both the best single model (84.55%) and the best ensembled model (85.40%) in literature." + ], + [ + "As shown in Table TABREF26 , the combined word level models (e.g. CTB+jieba or PKU+CTB) perform better than any word level CNNs with single word segmentation result (e.g. CNN-CTB or CNN-PKU). The main reason is that there are often no perfect Chinese word segmenters and a single improper segmentation decision may harm the matching performance, since that could further make the word mismatching issue worse, while the combination of different word segmentation results can somehow relieve this situation.", + "Furthermore, the models combining words and characters all perform better than PKU+CTB+jieba, because they could be complementary in different granularities. Specifically, Word+Char is still worse than CNN-char, because Chinese characters have rich meanings and compressing several characters to a single word vector will inevitably lose information. Furthermore, the combined sequence of Word+Char still exploits in a word level, which still suffers from the single segmentation decision. On the other side, the Char+Word model is also slightly worse than CNN-char. We think one reason is that the reduplicated word embeddings concatenated with each character vector confuse the CNNs, and perhaps lead to overfitting. But, we can still see that Char+Word performs better than Word+Char, because the former exploits in a character level and the fine-granularity information actually helps to relieve word mismatch. Note that Char+Lattice outperforms Char+Word, and even slightly better than CNN-char. This illustrates that multiple word segmentations are still helpful to further improve the character level strong baseline CNN-char, which may still benefit from word level information in a multi-granularity fashion.", + "In conclusion, the combination between different sequences and information of different granularities can help improve text matching, showing that it is necessary to consider the fashion which considers both characters and more possible words, which perhaps the word lattice can provide.", + "For DGCs with different kinds of pooling operations, average pooling (DGC-ave) performs the best, which delivers similar performance with LCN-ave. While DGC-max performs a little worse, because it ignores the importance of different edges and the maximum operation is more sensitive to noise than the average operation. The DGC-gated performs the worst. Compared with LCN-gated that learns the gate value adaptively from multiple n-gram context, it is harder for DGC to learn the importance of each edge via the node and the center node in the word lattice. It is not surprising that LCN-gated performs much better than GDC-gated, indicating again that n-grams in word lattice play an important role in context modeling, while DGCs are designed for general directed graphs which may not be perfect to work with word lattice.", + "For LCNs with different pooling operations, LCN-max and LCN-ave lead to similar performances, and perform better on KBRE, while LCN-gated is better on DBQA. This may be due to the fact that sentences in DBQA are relatively longer with more irrelevant information which require to filter noisy context, while on KBRE with much shorter predicate phrases, LCN-gated may slightly overfit due to its more complex model structure. Overall, we can see that LCNs perform better than DGCs, thanks to the advantage of better capturing multiple n-grams context in word lattice.", + "To investigate how LCNs utilize multi-granularity more intuitively, we analyze the MRR score against granularities of overlaps between questions and answers in DBQA dataset, which is shown in Figure FIGREF32 . It is demonstrated that CNN-char performs better than CNN-CTB impressively in first few groups where most of the overlaps are single characters which will cause serious word mismatch. With the growing of the length of overlaps, CNN-CTB is catching up and finally overtakes CNN-char even though its overall performance is much lower. This results show that word information is complementary to characters to some extent. The LCN-gated is approaching the CNN-char in first few groups, and outperforms both character and word level models in next groups, where word level information becomes more powerful. This demonstrates that LCNs can effectively take advantages of different granularities, and the combination will not be harmful even when the matching clues present in extreme cases.", + "How to Create Word Lattice In previous experiments, we construct word lattice via an existing lookup vocabulary, which will introduce some noisy words inevitably. Here we construct from various word segmentations with different strategies to investigate the balance between the noisy words and additional information introduced by word lattice. We only use the DBQA dataset because word lattices here are more complex, so the construction strategies have more influence. Pilot experiments show that word lattices constructed based on character sequence perform better, so the strategies in Table TABREF33 are based on CNN-char.", + "From Table TABREF33 , it is shown that all kinds of lattice are better than CNN-char, which also evidence the usage of word information. And among all LCN models, more complex lattice produces better performance in principle, which indicates that LCNs can handle the noisy words well and the influence of noisy words can not cancel the positive information brought by complex lattices. It is also noticeable that LCN-gated is better than LCN-C+20 by a considerable margin, which shows that the words not in general tokenization (e.g. \u201clivelihood\u201d in Fig FIGREF4 ) are potentially useful.", + "LCNs only introduce inappreciable parameters in gated pooling besides the increasing vocabulary, which will not bring a heavy burden. The training speed is about 2.8 batches per second, 5 times slower than original CNNs, and the whole training of a 2-layer LCN-gated on DBQA dataset only takes about 37.5 minutes. The efficiency may be further improved if the network structure builds dynamically with supported frameworks. The fast speed and little parameter increment give LCNs a promising future in more NLP tasks." + ], + [ + "Figure FIGREF37 shows a case study comparing models in different input levels. The word level model is relatively coarse in utilizing informations, and finds a sentence with the longest overlap (5 words, 12 characters). However, it does not realize that the question is about numbers of people, and the \u201cDaoHang\u201d(navigate) in question is a verb, but noun in the sentence. The character level model finds a long sentence which covers most of the characters in question, which shows the power of fine-granularity matching. But without the help of words, it is hard to distinguish the \u201cRen\u201d(people) in \u201cDuoShaoRen\u201d(how many people) and \u201cChuangShiRen\u201d(founder), so it loses the most important information. While in lattice, although overlaps are limited, \u201cWangZhan\u201d(website, \u201cWang\u201d web, \u201cZhan\u201d station) can match \u201cWangZhi\u201d(Internet addresses, \u201cWang\u201d web, \u201cZhi\u201d addresses) and also relate to \u201cDaoHang\u201d(navigate), from which it may infer that \u201cWangZhan\u201d(website) refers to \u201ctao606 seller website navigation\u201d(a website name). Moreover, \u201cYongHu\u201d(user) can match \u201cRen\u201d(people). With cooperations between characters and words, it catches the key points of the question and eliminates the other two candidates, as a result, it finds the correct answer." + ], + [ + "Deep learning models have been widely adopted in natural language sentence matching. Representation based models BIBREF21 , BIBREF7 , BIBREF0 , BIBREF8 encode and compare matching branches in hidden space. Interaction based models BIBREF23 , BIBREF22 , BIBREF3 incorporates interactions features between all word pairs and adopts 2D-convolution to extract matching features. Our models are built upon the representation based architecture, which is better for short text matching.", + "In recent years, many researchers have become interested in utilizing all sorts of external or multi-granularity information in matching tasks. BIBREF24 exploit hidden units in different depths to realize interaction between substrings with different lengths. BIBREF3 join multiple pooling methods in merging sentence level features, BIBREF4 exploit interactions between different lengths of text spans. For those more similar to our work, BIBREF3 also incorporate characters, which is fed into LSTMs and concatenate the outcomes with word embeddings, and BIBREF8 utilize words together with predicate level tokens in KBRE task. However, none of them exploit the multi-granularity information in word lattice in languages like Chinese that do not have space to segment words naturally. Furthermore, our model has no conflicts with most of them except BIBREF3 and could gain further improvement.", + "GCNs BIBREF25 , BIBREF26 and graph-RNNs BIBREF27 , BIBREF28 have extended CNNs and RNNs to model graph information, and DGCs generalize GCNs on directed graphs in the fields of semantic-role labeling BIBREF12 , document dating BIBREF18 , and SQL query embedding BIBREF29 . However, DGCs control information flowing from neighbor vertexes via edge types, while we focus on capturing different contexts for each word in word lattice via convolutional kernels and poolings.", + "Previous works involved Chinese lattice into RNNs for Chinese-English translation BIBREF10 , Chinese named entity recognition BIBREF11 , and Chinese word segmentation BIBREF30 . To the best of our knowledge, we are the first to conduct CNNs on word lattice, and the first to involve word lattice in matching tasks. And we motivate to utilize multi-granularity information in word lattices to relieve word mismatch and diverse expressions in Chinese question answering, while they mainly focus on error propagations from segmenters." + ], + [ + "In this paper, we propose a novel neural network matching method (LCNs) for matching based question answering in Chinese. Rather than relying on a word sequence only, our model takes word lattice as input. By performing CNNs over multiple n-gram context to exploit multi-granularity information, LCNs can relieve the word mismatch challenges. Thorough experiments show that our model can better explore the word lattice via convolutional operations and rich context-aware pooling, thus outperforms the state-of-the-art models and competitive baselines by a large margin. Further analyses exhibit that lattice input takes advantages of word and character level information, and the vocabulary based lattice constructor outperforms the strategies that combine characters and different word segmentations together." + ], + [ + "This work is supported by Natural Science Foundation of China (Grant No. 61672057, 61672058, 61872294); the UK Engineering and Physical Sciences Research Council under grants EP/M01567X/1 (SANDeRs) and EP/M015793/1 (DIVIDEND); and the Royal Society International Collaboration Grant (IE161012). For any correspondence, please contact Yansong Feng." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0674/instruction.md b/qasper-0674/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..d2f43a518c199e8874be703624619c7b41f0c1fc --- /dev/null +++ b/qasper-0674/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: On the coexistence of competing languages + +Question: What languages do they look at? \ No newline at end of file diff --git a/qasper-0680/instruction.md b/qasper-0680/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..91e3601bb26c87fe4a696803de4d008578da2545 --- /dev/null +++ b/qasper-0680/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Speaker-independent classification of phonetic segments from raw ultrasound in child speech + +Question: How many instances does their dataset have? \ No newline at end of file diff --git a/qasper-0687/instruction.md b/qasper-0687/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..77969f91de5d3aef26c9e1b9515be19e03aa3e98 --- /dev/null +++ b/qasper-0687/instruction.md @@ -0,0 +1,104 @@ +Name of Paper: A Multi-Turn Emotionally Engaging Dialog Model + +Question: What two baseline models are used? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work", + "Model", + "Model ::: Hierarchical Attention", + "Model ::: Emotion Encoder", + "Model ::: Decoding", + "Evaluation", + "Evaluation ::: Datasets", + "Evaluation ::: Baselines and Implementation", + "Evaluation ::: Evaluation Metrics", + "Evaluation ::: Evaluation Metrics ::: Human evaluation setup", + "Evaluation ::: Results", + "Evaluation ::: Results ::: Case Study", + "Conclusion and Future Work" + ], + "paragraphs": [ + [ + "Recent development in neural language modeling has generated significant excitement in the open-domain dialog generation community. The success of sequence-to-sequence learning BIBREF0, BIBREF1 in the field of neural machine translation has inspired researchers to apply the recurrent neural network (RNN) encoder-decoder structure to response generation BIBREF2. Specifically, the encoder RNN reads the input message, encodes it into a fixed context vector, and the decoder RNN uses it to generate the response. Shang et al. BIBREF3 applied the same structure combined with attention mechanism BIBREF4 on Twitter-style microblogging data. Following the vanilla sequence-to-sequence structure, various improvements have been made on the neural conversation model\u2014for example, increasing the diversity of the response BIBREF5, BIBREF6, modeling personalities of the speakers BIBREF7, and developing topic aware dialog systems BIBREF8.", + "Some of the recent work aims at incorporating affect information into neural conversational models. While making the responses emotionally richer, existing approaches either explicitly require an emotion label as input BIBREF9, or rely on hand-crafted rules to determine the desired emotion responses BIBREF10, BIBREF11, ignoring the subtle emotional interactions captured in multi-turn conversations, which we believe to be an important aspect of human dialogs. For example, Gottman BIBREF12 found that couples are likely to practice the so called emotional reciprocity. When an argument starts, one partner's angry and aggressive utterance is often met with equally furious and negative utterance, resulting in more heated exchanges. On the other hand, responding with complementary emotions (such as reassurance and sympathy) is more likely to lead to a successful relationship. However, to the best of our knowledge, the psychology and social science literature does not offer clear rules for emotional interaction. It seems such social and emotional intelligence is captured in our conversations. This is why we believe that the data driven approach will have an advantage.", + "In this paper, we propose an end-to-end data driven multi-turn dialog system capable of learning and generating emotionally appropriate and human-like responses with the ultimate goal of reproducing social behaviors that are habitual in human-human conversations. We chose the multi-turn setting because only in such cases is the emotion appropriateness most necessary. To this end, we employ the latest multi-turn dialog model by Xing et al. BIBREF13, but we add an additional emotion RNN to process the emotional information in each history utterance. By leveraging an external text analysis program, we encode the emotion aspects of each utterance into a fixed-sized one-zero vector. This emotion RNN reads and encodes the input affect information, and then uses the final hidden state as the emotion representation vector for the context. When decoding, at each time step, this emotion vector is concatenated with the hidden state of the decoder and passed to the softmax layer to produce the probability distribution over the vocabulary.", + "Thereby, our contributions are threefold. (1) We propose a novel emotion-tracking dialog generation model that learns the emotional interactions directly from the data. This approach is free of human-defined heuristic rules, and hence, is more robust and fundamental than those described in existing work BIBREF9, BIBREF10, BIBREF11. (2) We apply the emotion-tracking mechanism to multi-turn dialogs, which has never been attempted before. Human evaluation shows that our model produces responses that are emotionally more appropriate than the baselines, while slightly improving the language fluency. (3) We illustrate a human-evaluation approach for judging machine-produced emotional dialogs. We consider factors such as the balance of positive and negative sentiments in test dialogs, a well-chosen range of topics, and dialogs that our human evaluators can relate. It is the first time such an approach is designed with consideration for the human judges. Our main goal is to increase the objectivity of the results and reduce judges' mistakes due to out-of-context dialogs they have to evaluate.", + "The rest of the paper unfolds as follows. Section SECREF2 discusses some related work. In Section SECREF3, we give detailed description of the methodology. We present experimental results and some analysis in Section SECREF4. The paper is concluded in Section SECREF5, followed by some future work we plan to do." + ], + [ + "Many early open-domain dialog systems are rule-based and often require expert knowledge to develop. More recent work in response generation seeks data-driven solutions, leveraging on machine learning techniques and the availability of data. Ritter et al. BIBREF14 first applied statistical machine translation (SMT) methods to this area. However, it turns out that bilingual translation and response generation are different. The source and target sentences in translation share the same meaning; thus the words in the two sentences tend to align well with each other. However, for response generation, one could have many equally good responses for a single input. Later studies use the sequence-to-sequence neural framework to model dialogs, followed by various improving work on the quality of the responses, especially the emotional aspects of the conversations.", + "The vanilla RNN encoder-decoder is usually applied to single-turn response generation, where the response is generated based on one single input message. In multi-turn settings, where a context with multiple history utterances is given, the same structure often ignores the hierarchical characteristic of the context. Some recent work addresses this problem by adopting a hierarchical recurrent encoder-decoder (HRED) structure BIBREF15, BIBREF16, BIBREF17. To give attention to different parts of the context while generating responses, Xing et al. BIBREF13 proposed the hierarchical recurrent attention network (HRAN) that uses a hierarchical attention mechanism. However, these multi-turn dialog models do not take into account the turn-taking emotional changes of the dialog.", + "Recent work on incorporating affect information into natural language processing tasks, such as building emotional dialog systems and affect language models, has inspired our current work. For example, the Emotional Chatting Machine (ECM) BIBREF9 takes as input a post and a specified emotional category and generates a response that belongs to the pre-defined emotion category. The main idea is to use an internal memory module to capture the emotion dynamics during decoding, and an external memory module to model emotional expressions explicitly by assigning different probability values to emotional words as opposed to regular words. However, the problem setting requires an emotional label as an input, which might be unpractical in real scenarios. Asghar et al. BIBREF10 proposed to augment the word embeddings with a VAD (valence, arousal, and dominance) affective space by using an external dictionary, and designed three affect-related loss functions, namely minimizing affective dissonance, maximizing affective dissonance, and maximizing affective content. The paper also proposed the affectively diverse beam search during decoding, so that the generated candidate responses are as affectively diverse as possible. However, literature in affective science does not necessarily validate such rules. In fact, the best strategy to speak to an angry customer is the de-escalation strategy (using neutral words to validate anger) rather than employing equally emotional words (minimizing affect dissonance) or words that convey happiness (maximizing affect dissonance). Zhong et al. BIBREF11 proposed a biased attention mechanism on affect-rich words in the input message, also by taking advantage of the VAD embeddings. The model is trained with a weighted cross-entropy loss function, which encourages the generation of emotional words. However, these models only deal with single-turn conversations. More importantly, they all adopt hand-coded emotion responding mechanisms. To our knowledge, we are the first to consider modeling the emotional flow and its appropriateness in a multi-turn dialog system by learning from humans." + ], + [ + "In this paper, we consider the problem of generating response $\\mathbf {y}$ given a context $\\mathbf {X}$ consisting of multiple previous utterances by estimating the probability distribution $p(\\mathbf {y}\\,|\\,\\mathbf {X})$ from a data set $\\mathcal {D}=\\lbrace (\\mathbf {X}^{(i)},\\mathbf {y}^{(i)})\\rbrace _{i=1}^N$ containing $N$ context-response pairs. Here", + "is a sequence of $m_i$ utterances, and", + "is a sequence of $n_{ij}$ words. Similarly,", + "is the response with $T_i$ words.", + "Usually the probability distribution $p(\\mathbf {y}\\,|\\,\\mathbf {X})$ can be modeled by an RNN language model conditioned on $\\mathbf {X}$. When generating the word $y_t$ at time step $t$, the context $\\mathbf {X}$ is encoded into a fixed-sized dialog context vector $\\mathbf {c}_t$ by following the hierarchical attention structure in HRAN BIBREF13. Additionally, we extract the emotion information from the utterances in $\\mathbf {X}$ by leveraging an external text analysis program, and use an RNN to encode it into an emotion context vector $\\mathbf {e}$, which is combined with $\\mathbf {c}_t$ to produce the distribution. The overall architecture of the model is depicted in Figure FIGREF4. We are going to elaborate on how to obtain $\\mathbf {c}_t$ and $\\mathbf {e}$, and how they are combined in the decoding part." + ], + [ + "The hierarchical attention structure involves two encoders to produce the dialog context vector $\\mathbf {c}_t$, namely the word-level encoder and the utterance-level encoder. The word-level encoder is essentially a bidirectional RNN with gated recurrent units (GRU) BIBREF1. For utterance $\\mathbf {x}_j$ in $\\mathbf {X}$ ($j=1,2,\\dots ,m$), the bidirectional encoder produces two hidden states at each word position $k$, the forward hidden state $\\mathbf {h}^\\mathrm {f}_{jk}$ and the backward hidden state $\\mathbf {h}^\\mathrm {b}_{jk}$. The final hidden state $\\mathbf {h}_{jk}$ is then obtained by concatenating the two,", + "The utterance-level encoder is a unidirectional RNN with GRU that goes from the last utterance in the context to the first, with its input at each step as the summary of the corresponding utterance, which is obtained by applying a Bahdanau-style attention mechanism BIBREF4 on the word-level encoder output. More specifically, at decoding step $t$, the summary of utterance $\\mathbf {x}_j$ is a linear combination of $\\mathbf {h}_{jk}$, for $k=1,2,\\dots ,n_j$,", + "Here $\\alpha _{jk}^t$ is the word-level attention score placed on $\\mathbf {h}_{jk}$, and can be calculated as", + "where $\\mathbf {s}_{t-1}$ is the previous hidden state of the decoder, $\\mathbf {\\ell }_{j+1}^t$ is the previous hidden state of the utterance-level encoder, and $\\mathbf {v}_a$, $\\mathbf {U}_a$, $\\mathbf {V}_a$ and $\\mathbf {W}_a$ are word-level attention parameters. The final dialog context vector $\\mathbf {c}_t$ is then obtained as another linear combination of the outputs of the utterance-level encoder $\\mathbf {\\ell }_{j}^t$, for $j=1,2,\\dots ,m$,", + "Here $\\beta _{j}^t$ is the utterance-level attention score placed on $\\mathbf {\\ell }_{j}^t$, and can be calculated as", + "where $\\mathbf {s}_{t-1}$ is the previous hidden state of the decoder, and $\\mathbf {v}_b$, $\\mathbf {U}_b$ and $\\mathbf {W}_b$ are utterance-level attention parameters." + ], + [ + "In order to capture the emotion information carried in the context $\\mathbf {X}$, we utilize an external text analysis program called the Linguistic Inquiry and Word Count (LIWC) BIBREF18. LIWC accepts text files as input, and then compares each word in the input with a user-defined dictionary, assigning it to one or more of the pre-defined psychologically-relevant categories. We make use of five of these categories, related to emotion, namely positive emotion, negative emotion, anxious, angry, and sad. Using the newest version of the program LIWC2015, we are able to map each utterance $\\mathbf {x}_j$ in the context to a six-dimensional indicator vector ${1}(\\mathbf {x}_j)$, with the first five entries corresponding to the five emotion categories, and the last one corresponding to neutral. If any word in $\\mathbf {x}_j$ belongs to one of the five categories, then the corresponding entry in ${1}(\\mathbf {x}_j)$ is set to 1; otherwise, $\\mathbf {x}_j$ is treated as neutral, with the last entry of ${1}(\\mathbf {x}_j)$ set to 1. For example, assuming $\\mathbf {x}_j=$ \u201che is worried about me\u201d, then", + "since the word \u201cworried\u201d is assigned to both negative emotion and anxious. We apply a dense layer with sigmoid activation function on top of ${1}(\\mathbf {x}_j)$ to embed the emotion indicator vector into a continuous space,", + "where $\\mathbf {W}_e$ and $\\mathbf {b}_e$ are trainable parameters. The emotion flow of the context $\\mathbf {X}$ is then modeled by an unidirectional RNN with GRU going from the first utterance in the context to the last, with its input being $\\mathbf {a}_j$ at each step. The final emotion context vector $\\mathbf {e}$ is obtained as the last hidden state of this emotion encoding RNN." + ], + [ + "The probability distribution $p(\\mathbf {y}\\,|\\,\\mathbf {X})$ can be written as", + "We model the probability distribution using an RNN language model along with the emotion context vector $\\mathbf {e}$. Specifically, at time step $t$, the hidden state of the decoder $\\mathbf {s}_t$ is obtained by applying the GRU function,", + "where $\\mathbf {w}_{y_{t-1}}$ is the word embedding of $y_{t-1}$. Similar to Affect-LM BIBREF19, we then define a new feature vector $\\mathbf {o}_t$ by concatenating $\\mathbf {s}_t$ with the emotion context vector $\\mathbf {e}$,", + "on which we apply a softmax layer to obtain a probability distribution over the vocabulary,", + "Each term in Equation (DISPLAY_FORM16) is then given by", + "We use the cross-entropy loss as our objective function" + ], + [ + "We trained our model using two different datasets and compared its performance with HRAN as well as the basic sequence-to-sequence model by performing both offline and online testings." + ], + [ + "We use two different dialog corpora to train our model\u2014the Cornell Movie Dialogs Corpus BIBREF20 and the DailyDialog dataset BIBREF21.", + "Cornell Movie Dialogs Corpus. The dataset contains 83,097 dialogs (220,579 conversational exchanges) extracted from raw movie scripts. In total there are 304,713 utterances.", + "DailyDialog. The dataset is developed by crawling raw data from websites used for language learners to learn English dialogs in daily life. It contains 13,118 dialogs in total.", + "We summarize some of the basic information regarding the two datasets in Table TABREF25.", + "In our experiments, the models are first trained on the Cornell Movie Dialogs Corpus, and then fine-tuned on the DailyDialog dataset. We adopted this training pattern because the Cornell dataset is bigger but noisier, while DailyDialog is smaller but more daily-based. To create a training set and a validation set for each of the two datasets, we take segments of each dialog with number of turns no more than six, to serve as the training/validation examples. Specifically, for each dialog $\\mathbf {D}=(\\mathbf {x}_1,\\mathbf {x}_2,\\dots ,\\mathbf {x}_M)$, we create $M-1$ context-response pairs, namely $\\mathbf {U}_i=(\\mathbf {x}_{s_i},\\dots ,\\mathbf {x}_i)$ and $\\mathbf {y}_i=\\mathbf {x}_{i+1}$, for $i=1,2,\\dots ,M-1$, where $s_i=\\max (1,i-4)$. We filter out those pairs that have at least one utterance with length greater than 30. We also reduce the frequency of those pairs whose responses appear too many times (the threshold is set to 10 for Cornell, and 5 for DailyDialog), to prevent them from dominating the learning procedure. See Table TABREF25 for the sizes of the training and validation sets. The test set consists of 100 dialogs with four turns. We give more detailed description of how we create the test set in Section SECREF31." + ], + [ + "We compared our multi-turn emotionally engaging dialog model (denoted as MEED) with two baselines\u2014the vanilla sequence-to-sequence model (denoted as S2S) and HRAN. We chose S2S and HRAN as baselines because we would like to evaluate our model's capability to keep track of the multi-turn context and to produce emotionally more appropriate responses, respectively. In order to adapt S2S to the multi-turn setting, we concatenate all the history utterances in the context into one.", + "For all the models, the vocabulary consists of 20,000 most frequent words in the Cornell and DailyDialog datasets, plus three extra tokens: for words that do not exist in the vocabulary, indicating the begin of an utterance, and indicating the end of an utterance. Here we summarize the configurations and parameters of our experiments:", + "We set the word embedding size to 256. We initialized the word embeddings in the models with word2vec BIBREF22 vectors first trained on Cornell and then fine-tuned on DailyDialog, consistent with the training procedure of the models.", + "We set the number of hidden units of each RNN to 256, the word-level attention depth to 256, and utterance-level 128. The output size of the emotion embedding layer is 256.", + "We optimized the objective function using the Adam optimizer BIBREF23 with an initial learning rate of 0.001. We stopped training the models when the lowest perplexity on the validation sets was achieved.", + "For prediction, we used beam search BIBREF24 with a beam width of 256." + ], + [ + "The evaluation of chatbots remains an open problem in the field. Recent work BIBREF25 has shown that the automatic evaluation metrics borrowed from machine translation such as BLEU score BIBREF26 tend to align poorly with human judgement. Therefore, in this paper, we mainly adopt human evaluation, along with perplexity, following the existing work." + ], + [ + "To develop a test set for human evaluation, we first selected the emotionally colored dialogs with exactly four turns from the DailyDialog dataset. In the dataset each dialog turn is annotated with a corresponding emotional category, including the neutral one. For our purposes we filtered out only those dialogs where more than a half of utterances have non-neutral emotional labels. This gave us 78 emotionally positive dialogs and 14 emotionally negative dialogs. In order to have a balanced test set with equal number of positive and negative dialogs, we recruited two English-speaking students from our university without any relationship to the authors' lab and instructed them to create five negative dialogs with four turns, as if they were interacting with another human, according to each of the following topics: relationships, entertainment, service, work and study, and everyday situations. Thus each person produced 25 dialogs, and in total we obtained 50 emotionally negative daily dialogs in addition to the 14 already available. To form the test set, we randomly selected 50 emotionally positive and 50 emotionally negative dialogs from the two pools of dialogs described above (78 positive dialogs from DailyDialog, 64 negative dialogs from DailyDialog and human-generated).", + "For human evaluation of the models, we recruited another four English-speaking students from our university without any relationship to the authors' lab to rate the responses generated by the models. Specifically, we randomly shuffled the 100 dialogs in the test set, then we used the first three utterances of each dialog as the input to the three models being compared and let them generate the responses. According to the context given, the raters were instructed to evaluate the quality of the responses based on three criteria: (1) grammatical correctness\u2014whether or not the response is fluent and free of grammatical mistakes; (2) contextual coherence\u2014whether or not the response is context sensitive to the previous dialog history; (3) emotional appropriateness\u2014whether or not the response conveys the right emotion and feels as if it had been produced by a human. For each criterion, the raters gave scores of either 0, 1 or 2, where 0 means bad, 2 means good, and 1 indicates neutral." + ], + [ + "Table TABREF34 gives the perplexity scores obtained by the three models on the two validation sets and the test set. As shown in the table, MEED achieves the lowest perplexity score on all three sets. We also conducted t-test on the perplexity obtained, and results show significant improvements (with $p$-value $<0.05$).", + "Table TABREF34, TABREF35 and TABREF35 summarize the human evaluation results on the responses' grammatical correctness, contextual coherence, and emotional appropriateness, respectively. In the tables, we give the percentage of votes each model received for the three scores, the average score obtained with improvements over S2S, and the agreement score among the raters. Note that we report Fleiss' $\\kappa $ score BIBREF27 for contextual coherence and emotional appropriateness, and Finn's $r$ score BIBREF28 for grammatical correctness. We did not use Fleiss' $\\kappa $ score for grammatical correctness. As agreement is extremely high, this can make Fleiss' $\\kappa $ very sensitive to prevalence BIBREF29. On the contrary, we did not use Finn's $r$ score for contextual coherence and emotional appropriateness because it is only reasonable when the observed variance is significantly less than the chance variance BIBREF30, which did not apply to these two criteria. As shown in the tables, we got high agreement among the raters for grammatical correctness, and fair agreement among the raters for contextual coherence and emotional appropriateness. For grammatical correctness, all three models achieved high scores, which means all models are capable of generating fluent utterances that make sense. For contextual coherence and emotional appropriateness, MEED achieved higher average scores than S2S and HRAN, which means MEED keeps better track of the context and can generate responses that are emotionally more appropriate and natural. We conducted Friedman test BIBREF31 on the human evaluation results, showing the improvements of MEED are significant (with $p$-value $<0.01$)." + ], + [ + "We present four sample dialogs in Table TABREF36, along with the responses generated by the three models. Dialog 1 and 2 are emotionally positive and dialog 3 and 4 are negative. For the first two examples, we can see that MEED is able to generate more emotional content (like \u201cfun\u201d and \u201ccongratulations\u201d) that is appropriate according to the context. For dialog 4, MEED responds in sympathy to the other speaker, which is consistent with the second utterance in the context. On the contrary, HRAN poses a question in reply, contradicting the dialog history." + ], + [ + "According to the Media Equation Theory BIBREF32, people respond to computers socially. This means humans expect talking to computers as they talk to other human beings. This is why we believe reproducing social and conversational intelligence will make social chatbots more believable and socially engaging. In this paper, we propose a multi-turn dialog system capable of generating emotionally appropriate responses, which is the first step toward such a goal. We have demonstrated how to do so by (1) modeling utterances with extra affect vectors, (2) creating an emotional encoding mechanism that learns emotion exchanges in the dataset, (3) curating a multi-turn dialog dataset, and (4) evaluating the model with offline and online experiments.", + "As future work, we would like to investigate the diversity issue of the responses generated, possibly by extending the mutual information objective function BIBREF5 to multi-turn settings. We would also like to evaluate our model on a larger dataset, for example by extracting multi-turn dialogs from the OpenSubtitles corpus BIBREF33." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0688/instruction.md b/qasper-0688/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..a5f3b40e5696fae2a7549ba11a4e554bf2ed39e5 --- /dev/null +++ b/qasper-0688/instruction.md @@ -0,0 +1,127 @@ +Name of Paper: Information Extraction in Illicit Domains + +Question: Do they evaluate on relation extraction? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work", + "Approach", + "Preprocessing", + "Deriving Word Representations", + "Applying High-Recall Recognizers", + "Supervised Contextual Classifier", + "Datasets and Ground-truths", + "System", + "Baselines", + "Setup and Parameters", + "Results", + "Discussion", + "Conclusion" + ], + "paragraphs": [ + [ + "Building knowledge graphs (KG) over Web corpora is an important problem that has galvanized effort from multiple communities over two decades BIBREF0 , BIBREF1 . Automated knowledge graph construction from Web resources involves several different phases. The first phase involves domain discovery, which constitutes identification of sources, followed by crawling and scraping of those sources BIBREF2 . A contemporaneous ontology engineering phase is the identification and design of key classes and properties in the domain of interest (the domain ontology) BIBREF3 .", + "Once a set of (typically unstructured) data sources has been identified, an Information Extraction (IE) system needs to extract structured data from each page in the corpus BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 . In IE systems based on statistical learning, sequence labeling models like Conditional Random Fields (CRFs) can be trained and used for tagging the scraped text from each data source with terms from the domain ontology BIBREF8 , BIBREF7 . With enough data and computational power, deep neural networks can also be used for a range of collective natural language tasks, including chunking and extraction of named entities and relationships BIBREF9 .", + "While IE has been well-studied both for cross-domain Web sources (e.g. Wikipedia) and for traditional domains like biomedicine BIBREF10 , BIBREF11 , it is less well-studied (Section \"Preprocessing\" ) for dynamic domains that undergo frequent changes in content and structure. Such domains include news feeds, social media, advertising, and online marketplaces, but also illicit domains like human trafficking. Automatically constructing knowledge graphs containing important information like ages (of human trafficking victims), locations, prices of services and posting dates over such domains could have widespread social impact, since law enforcement and federal agencies could query such graphs to glean rapid insights BIBREF12 .", + "Illicit domains pose some formidable challenges for traditional IE systems, including deliberate information obfuscation, non-random misspellings of common words, high occurrences of out-of-vocabulary and uncommon words, frequent (and non-random) use of Unicode characters, sparse content and heterogeneous website structure, to only name a few BIBREF12 , BIBREF13 , BIBREF14 . While some of these characteristics are shared by more traditional domains like chat logs and Twitter, both information obfuscation and extreme content heterogeneity are unique to illicit domains. While this paper only considers the human trafficking domain, similar kinds of problems are prevalent in other illicit domains that have a sizable Web (including Dark Web) footprint, including terrorist activity, and sales of illegal weapons and counterfeit goods BIBREF15 .", + "As real-world illustrative examples, consider the text fragments `Hey gentleman im neWYOrk and i'm looking for generous...' and `AVAILABLE NOW! ?? - (4 two 4) six 5 two - 0 9 three 1 - 21'. In the first instance, the correct extraction for a Name attribute is neWYOrk, while in the second instance, the correct extraction for an Age attribute is 21. It is not obvious what features should be engineered in a statistical learning-based IE system to achieve robust performance on such text.", + "To compound the problem, wrapper induction systems from the Web IE literature cannot always be applied in such domains, as many important attributes can only be found in text descriptions, rather than template-based Web extractors that wrappers traditionally rely on BIBREF6 . Constructing an IE system that is robust to these problems is an important first step in delivering structured knowledge bases to investigators and domain experts.", + "In this paper, we study the problem of robust information extraction in dynamic, illicit domains with unstructured content that does not necessarily correspond to a typical natural language model, and that can vary tremendously between different Web domains, a problem denoted more generally as concept drift BIBREF16 . Illicit domains like human trafficking also tend to exhibit a `long tail'; hence, a comprehensive solution should not rely on information extractors being tailored to pages from a small set of Web domains.", + "There are two main technical challenges that such domains present to IE systems. First, as the brief examples above illustrate, feature engineering in such domains is difficult, mainly due to the atypical (and varying) representation of information. Second, investigators and domain experts require a lightweight system that can be quickly bootstrapped. Such a system must be able to generalize from few ( $\\approx $ 10-150) manual annotations, but be incremental from an engineering perspective, especially since a given illicit Web page can quickly (i.e. within hours) become obsolete in the real world, and the search for leads and information is always ongoing. In effect, the system should be designed for streaming data.", + "We propose an information extraction approach that is able to address the challenges above, especially the variance between Web pages and the small training set per attribute, by combining two sequential techniques in a novel paradigm. The overall approach is illustrated in Figure 1 . First, a high-recall recognizer, which could range from an exhaustive Linked Data source like GeoNames (e.g. for extracting locations) to a simple regular expression (e.g. for extracting ages), is applied to each page in the corpus to derive a set of candidate annotations for an attribute per page. In the second step, we train and apply a supervised feature-agnostic classification algorithm, based on learning word representations from random projections, to classify each candidate as correct/incorrect for its attribute.", + "Contributions We summarize our main contributions as follows: (1) We present a lightweight feature-agnostic information extraction system for a highly heterogeneous, illicit domain like human trafficking. Our approach is simple to implement, does not require extensive parameter tuning, infrastructure setup and is incremental with respect to the data, which makes it suitable for deployment in streaming-corpus settings. (2) We show that the approach shows good generalization even when only a small corpus is available after the initial domain-discovery phase, and is robust to the problem of concept drift encountered in large Web corpora. (3) We test our approach extensively on a real-world human trafficking corpus containing hundreds of thousands of Web pages and millions of unique words, many of which are rare and highly domain-specific. Evaluations show that our approach outperforms traditional Named Entity Recognition baselines that require manual feature engineering. Specific empirical highlights are provided below.", + "Empirical highlights Comparisons against CRF baselines based on the latest Stanford Named Entity Resolution system (including pre-trained models as well as new models that we trained on human trafficking data) show that, on average, across five ground-truth datasets, our approach outperforms the next best system on the recall metric by about 6%, and on the F1-measure metric by almost 20% in low-supervision settings (30% training data), and almost 20% on both metrics in high-supervision settings (70% training data). Concerning efficiency, in a serial environment, we are able to derive word representations on a 43 million word corpus in under an hour. Degradation in average F1-Measure score achieved by the system is less than 2% even when the underlying raw corpus expands by a factor of 18, showing that the approach is reasonably robust to concept drift.", + "Structure of the paper Section \"Preprocessing\" describes some related work on Information Extraction. Section \"Approach\" provides details of key modules in our approach. Section \"Evaluations\" describes experimental evaluations, and Section \"Conclusion\" concludes the work." + ], + [ + "Information Extraction (IE) is a well-studied research area both in the Natural Language Processing community and in the World Wide Web, with the reader referred to the survey by Chang et al. for an accessible coverage of Web IE approaches BIBREF17 . In the NLP literature, IE problems have predominantly been studied as Named Entity Recognition and Relationship Extraction BIBREF7 , BIBREF18 . The scope of Web IE has been broad in recent years, extending from wrappers to Open Information Extraction (OpenIE) BIBREF6 , BIBREF19 .", + "In the Semantic Web, domain-specific extraction of entities and properties is a fundamental aspect in constructing instance-rich knowledge bases (from unstructured corpora) that contribute to the Semantic Web vision and to ecosystems like Linked Open Data BIBREF20 , BIBREF21 . A good example of such a system is Lodifier BIBREF22 . This work is along the same lines, in that we are interested in user-specified attributes and wish to construct a knowledge base (KB) with those attribute values using raw Web corpora. However, we are not aware of any IE work in the Semantic Web that has used word representations to accomplish this task, or that has otherwise outperformed state-of-the-art systems without manual feature engineering.", + "The work presented in this paper is structurally similar to the geolocation prediction system (from Twitter) by Han et al. and also ADRMine, an adverse drug reaction (ADR) extraction system from social media BIBREF23 , BIBREF24 . Unlike these works, our system is not optimized for specific attributes like locations and drug reactions, but generalizes to a range of attributes. Also, as mentioned earlier, illicit domains involve challenges not characteristic of social media, notably information obfuscation.", + "In recent years, state-of-the-art results have been achieved in a variety of NLP tasks using word representation methods like neural embeddings BIBREF25 . Unlike the problem covered in this paper, those papers typically assume an existing KB (e.g. Freebase), and attempt to infer additional facts in the KB using word representations. In contrast, we study the problem of constructing and populating a KB per domain-specific attribute from scratch with only a small set of initial annotations from crawled Web corpora.", + "The problem studied in this paper also has certain resemblances to OpenIE BIBREF19 . One assumption in OpenIE systems is that a given fact (codified, for example, as an RDF triple) is observed in multiple pages and contexts, which allows the system to learn new `extraction patterns' and rank facts by confidence. In illicit domains, a `fact' may only be observed once; furthermore, the arcane and high-variance language models employed in the domain makes direct application of any extraction pattern-based approach problematic. To the best of our knowledge, the specific problem of devising feature-agnostic, low-supervision IE approaches for illicit Web domains has not been studied in prior work." + ], + [ + "Figure 1 illustrates the architecture of our approach. The input is a Web corpus containing relevant pages from the domain of interest, and high-recall recognizers (described in Section \"Applying High-Recall Recognizers\" ) typically adapted from freely available Web resources like Github and GeoNames. In keeping with the goals of this work, we do not assume that this initial corpus is static. That is, following an initial short set-up phase, more pages are expected to be added to the corpus in a streaming fashion. Given a set of pre-defined attributes (e.g. City, Name, Age) and around 10-100 manually verified annotations for each attribute, the goal is to learn an IE model that accurately extracts attribute values from each page in the corpus without relying on expert feature engineering. Importantly, while the pages are single-domain (e.g. human trafficking) they are multi-Web domain, meaning that the system must not only handle pages from new websites as they are added to the corpus, but also concept drift in the new pages compared to the initial corpus." + ], + [ + "The first module in Figure 1 is an automated pre-processing algorithm that takes as input a streaming set of HTML pages. In real-world illicit domains, the key information of interest to investigators (e.g. names and ages) typically occurs either in the text or the title of the page, not the template of the website. Even when the information occasionally occurs in a template, it must be appropriately disambiguated to be useful. Wrapper-based IE systems BIBREF6 are often inapplicable as a result. As a first step in building a more suitable IE model, we scrape the text from each HTML website by using a publicly available text extractor called the Readability Text Extractor (RTE). Although multiple tools are available for text extraction from HTML BIBREF26 , our early trials showed that RTE is particularly suitable for noisy Web domains, owing to its tuneability, robustness and support for developers. We tune RTE to achieve high recall, thus ensuring that the relevant text in the page is captured in the scraped text with high probability. Note that, because of the varied structure of websites, such a setting also introduces noise in the scraped text (e.g. wayward HTML tags). Furthermore, unlike natural language documents, scraped text can contain many irrelevant numbers, Unicode and punctuation characters, and may not be regular. Because of the presence of numerous tab and newline markers, there is no obvious natural language sentence structure in the scraped text. In the most general case, we found that RTE returned a set of strings, with each string corresponding to a set of sentences.", + "To serialize the scraped text as a list of tokens, we use the word and sentence tokenizers from the NLTK package on each RTE string output BIBREF27 . We apply the sentence tokenizer first, and to each sentence returned (which often does not correspond to an actual sentence due to rampant use of extraneous punctuation characters) by the sentence tokenizer, we apply the standard NLTK word tokenizer. The final output of this process is a list of tokens. In the rest of this section, this list of tokens is assumed as representing the HTML page from which the requisite attribute values need to be extracted." + ], + [ + "In principle, given some annotated data, a sequence labeling model like a Conditional Random Field (CRF) can be trained and applied on each block of scraped text to extract values for each attribute BIBREF8 , BIBREF7 . In practice, as we empirically demonstrate in Section \"Evaluations\" , CRFs prove to be problematic for illicit domains. First, the size of the training data available for each CRF is relatively small, and because of the nature of illicit domains, methods like distant supervision or crowdsourcing cannot be used in an obvious timely manner to elicit annotations from users. A second problem with CRFs, and other traditional machine learning models, is the careful feature engineering that is required for good performance. With small amounts of training data, good features are essential for generalization. In the case of illicit domains, it is not always clear what features are appropriate for a given attribute. Even common features like capitalization can be misleading, as there are many capitalized words in the text that are not of interest (and vice versa).", + "To alleviate feature engineering and manual annotation effort, we leverage the entire raw corpus in our model learning phase, rather than just the pages that have been annotated. Specifically, we use an unsupervised algorithm to represent each word in the corpus in a low-dimensional vector space. Several algorithms exist in the literature for deriving such representations, including neural embedding algorithms such as Word2vec BIBREF25 and the algorithm by Bollegala et al. BIBREF28 , as well as simpler alternatives BIBREF29 .", + "Given the dynamic nature of streaming illicit-domain data, and the numerous word representation learning algorithms in the literature, we adapted the random indexing (RI) algorithm for deriving contextual word representations BIBREF29 . Random indexing methods mathematically rely on the Johnson-Lindenstrauss Lemma, which states that if points in a vector space are of sufficiently high dimension, then they may be projected into a suitable lower-dimensional space in a way which approximately preserves the distances between the points.", + "The original random indexing algorithm was designed for incremental dimensionality reduction and text mining applications. We adapt this algorithm for learning word representations in illicit domains. Before describing these adaptations, we define some key concepts below.", + "definitionDefinition Given parameters $d \\in \\mathbb {Z}^{+}$ and $r \\in [0, 1]$ , a context vector is defined as a $d-$ dimensional vector, of which exactly $\\lfloor d r \\rfloor $ elements are randomly set to $+1$ , exactly $\\lfloor d r \\rfloor $ elements are randomly set to $-1$ and the remaining $d-2\\lfloor d r \\rfloor $ elements are set to 0.", + "We denote the parameters $d$ and $r$ in the definition above as the dimension and sparsity ratio parameters respectively.", + "Intuitively, a context vector is defined for every atomic unit in the corpus. Let us denote the universe of atomic units as $U$ , assumed to be a partially observed countably infinite set. In the current scenario, every unigram (a single `token') in the dataset is considered an atomic unit. Extending the definition to also include higher-order ngrams is straightforward, but was found to be unnecessary in our early empirical investigations. The universe is only partially observed because of the incompleteness (i.e. streaming, dynamic nature) of the initial corpus.", + "The actual vector space representation of an atomic unit is derived by defining an appropriate context for the unit. Formally, a context is an abstract notion that is used for assigning distributional semantics to the atomic unit. The distributional semantics hypothesis (also called Firth's axiom) states that the semantics of an atomic unit (e.g. a word) is defined by the contexts in which it occurs BIBREF30 .", + "In this paper, we only consider short contexts appropriate for noisy streaming data. In this vein, we define the notion of a $(u, v)$ -context window below:", + "Given a list $t$ of atomic units and an integer position $0= i$ as inputs, and returns True if the tokens contiguously spanning $t[i]:t[j]$ are instances of $A$ , and False otherwise. It is important to note that, per the definition above, a recognizer cannot annotate latent instances that are not directly observed in the list of tokens.", + "Since the `ideal' recognizer is not known, the broad goal of IE is to devise models that approximate it (for a given attribute) with high accuracy. Accuracy is typically measured in terms of precision and recall metrics. We formulate a two-pronged approach whereby, rather than develop a single recognizer that has both high precision and recall (and requires considerable expertise to design), we first obtain a list of candidate annotations that have high recall in expectation, and then use supervised classification in a second step to improve precision of the candidate annotations.", + "More formally, let $R_A$ be denoted as an $\\eta $ -recall recognizer if the expected recall of $R_A$ is at least $\\eta $ . Due to the explosive growth in data, many resources on the Web can be used for bootstrapping recognizers that are `high-recall' in that $\\eta $ is in the range of 90-100%. The high-recall recognizers currently used in the prototype described in this paper (detailed further in Section \"System\" ) rely on knowledge bases (e.g. GeoNames) from Linked Open Data BIBREF20 , dictionaries from the Web and broad heuristics, such as regular expression extractors, found in public Github repositories. In our experience, we found that even students with basic knowledge of GitHub and Linked Open Data sources are able to construct such recognizers. One important reason why constructing such recognizers is relatively hassle-free is because they are typically monotonic i.e. new heuristics and annotation sources can be freely integrated, since we do not worry about precision at this step.", + "We note that in some cases, domain knowledge alone is enough to guarantee 100% recall for well-designed recognizers for certain attributes. In HT, this is true for location attributes like city and state, since advertisements tend to state locations without obfuscation, and we use GeoNames, an exhaustive knowledge base of locations, as our recognizer. Manual inspection of the ground-truth data showed that the recall of utilized recognizers for attributes like Name and Age are also high (in many cases, 100%). Thus, although 100% recall cannot be guaranteed for any recognizer, it is still reasonable to assume that $\\eta $ is high.", + "A much more difficult problem is engineering a recognizer to simultaneously achieve high recall and high precision. Even for recognizers based on curated knowledge bases like GeoNames, many non-locations get annotated as locations. For example, the word `nice' is a city in France, but is also a commonly occurring adjective. Other common words like `for', `hot', `com', `kim' and `bella' also occur in GeoNames as cities and would be annotated. Using a standard Named Entity Recognition system does not always work because of the language modeling problem (e.g. missing capitalization) in illicit domains. In the next section, we show how the context surrounding the annotated word can be used to classify the annotation as correct or incorrect. We note that, because the recognizers are high-recall, a successful classifier would yield both high precision and recall." + ], + [ + "To address the precision problem, we train a classifier using contextual features. Rather than rely on a domain expert to provide a set of hand-crafted features, we derive a feature vector per candidate annotation using the notion of a context window (Definition \"Deriving Word Representations\" ) and the word representation vectors derived in Section \"Deriving Word Representations\" . This process of supervised contextual classification is illustrated in Figure 3 .", + "Specifically, for each annotation (which could comprise multiple contiguous tokens e.g. `Salt Lake City' in the list of tokens representing the website) annotated by a recognizer, we consider the tokens in the $(u, v)$ -context window around the annotation. We aggregate the vectors of those tokens into a single vector by performing an unweighted sum, followed by $l2$ -normalization. We use this aggregate vector as the contextual feature vector for that annotation. Note that, unlike the representation learning phase, where the surrounding context vectors were aggregated into an existing representation vector, the contextual feature vector is obtained by summing the actual representation vectors.", + "For each attribute, a supervised machine learning classifier (e.g. random forest) is trained using between 12-120 labeled annotations, and for new data, the remaining annotations can be classified using the trained classifier. Although the number of dimensions in the feature vectors is quite low compared to tf-idf vectors (hundreds vs. millions), a second round of dimensionality reduction can be applied by using (either supervised or unsupervised) feature selection for further empirical benefits (Section \"Evaluations\" )." + ], + [ + "We train the word representations on four real-world human trafficking datasets of increasing size, the details of which are provided in Table 2 . Since we assume a `streaming' setting in this paper, each larger dataset in Table 2 is a strict superset of the smaller datasets. The largest dataset is itself a subset of the overall human trafficking corpus that was scraped as part of research conducted in the DARPA MEMEX program.", + "Since ground-truth extractions for the corpus are unknown, we randomly sampled websites from the overall corpus, applied four high-recall recognizers described in Section \"System\" , and for each annotated set, manually verified whether the extractions were correct or incorrect for the corresponding attribute. The details of this sampled ground-truth are captured in Table 3 . Each annotation set is named using the format GT-{RawField}-{AnnotationAttribute}, where RawField can be either the HTML title or the scraped text (Section \"Preprocessing\" ). and AnnotationAttribute is the attribute of interest for annotation purposes." + ], + [ + "The overall system requires developing two components for each attribute: a high-recall recognizer and a classifier for pruning annotations. We developed four high-recall recognizers, namely GeoNames-Cities, GeoNames-States, RegEx-Ages and Dictionary-Names. The first two of these relies on the freely available GeoNames dataset BIBREF31 ; we use the entire dataset for our experiments, which involves modeling each GeoNames dictionary as a trie, owing to its large memory footprint. For extracting ages, we rely on simple regular expressions and heuristics that were empirically verified to capture a broad set of age representations. For the name attribute, we gather freely available Name dictionaries on the Web, in multiple countries and languages, and use the dictionaries in a case-insensitive recognition algorithm to locate names in the raw field (i.e. text or title)." + ], + [ + "We use different variants of the Stanford Named Entity Recognition system (NER) as our baselines BIBREF7 . For the first set of baselines, we use two pre-trained models trained on different English language corpora. Specifically, we use the 3-Class and 4-Class pre-trained models. We use the LOCATION class label for determining city and state annotations, and the PERSON label for name annotations. Unfortunately, there is no specific label corresponding to age annotations in the pre-trained models; hence, we do not use the pre-trained models as age annotation baselines.", + "It is also possible to re-train the underlying NER system on a new dataset. For the second set of baselines, therefore, we re-train the NER models by randomly sampling 30% and 70% of each annotation set in Table 3 respectively, with the remaining annotations used for testing. The features and values that were employed in the re-trained models are enumerated in Table 4 . Further documentation on these feature settings may be found on the NERFeatureFactory page. All training and testing experiments were done in ten independent trials. We use default parameter settings, and report average results for each experimental run. Experimentation using other configurations, features and values is left for future studies." + ], + [ + "Parameter tuning System parameters were set as follows. The number of dimensions in Definition \"Deriving Word Representations\" was set at 200, and the sparsity ratio was set at 0.01. These parameters are similar to those suggested in previous word representation papers; they were also found to yield intuitive results on semantic similarity experiments (described further in Section \"Discussion\" ). To avoid the problem of rare words, numbers, punctuation and tags, we used the six compound unit classes earlier described in Table 1 . In all experiments where defining a context was required, we used symmetric $(2,2)$ -context windows; using bigger windows was not found to offer much benefit. We trained a random forest model with default hyperparameters (10 trees, with Gini Impurity as the split criterion) as the supervised classifier, used supervised k-best feature selection with $k$ set to 20 (Section \"Supervised Contextual Classifier\" ), and with the Analysis of Variance (ANOVA) F-statistic between class label and feature used as the feature scoring function.", + "Because of the class skew in Table 3 (i.e. the `positive' class is typically much smaller than the `negative' class) we oversampled the positive class for balanced training of the supervised contextual classifier.", + "Metrics The metrics used for evaluating IE effectiveness are Precision, Recall and F1-measure.", + "Implementation In the interests of demonstrating a reasonably lightweight system, all experiments in this paper were run on a serial iMac with a 4 GHz Intel core i7 processor and 32 GB RAM. All code (except the Stanford NER code) was written in the Python programming language, and has been made available on a public Github repository with documentation and examples. We used Python's Scikit-learn library (v0.18) for the machine learning components of the prototype." + ], + [ + "Performance against baselines Table 5 illustrates system performance on Precision, Recall and F1-Measure metrics against the re-trained and pre-trained baseline models, where the re-trained model and our approach were trained on 30% of the annotations in Table 3 . We used the word representations derived from the D-ALL corpus. On average, the proposed system performs the best on F1-Measure and recall metrics. The re-trained NER is the most precise system, but at the cost of much less recall ( $<$ 30%). The good performance of the pre-trained baseline on the City attribute demonstrates the importance of having a large training corpus, even if the corpus is not directly from the test domain. On the other hand, the complete failure of the pre-trained baseline on the Name attribute illustrates the dangers of using out-of-domain training data. As noted earlier, language models in illicit domains can significantly differ from natural language models; in fact, names in human trafficking websites are often represented in a variety of misleading ways.", + "Recognizing that 30% training data may constitute a sample size too small to make reliable judgments, we also tabulate the results in Table 6 when the training percentage is set at 70. Performance improves for both the re-trained baseline and our system. Performance declines for the pre-trained baseline, but this may be because of the sparseness of positive annotations in the smaller test set.", + "We also note that performance is relatively well-balanced for our system; on all datasets and all metrics, the system achieves scores greater than 50%. This suggests that our approach has a degree of robustness that the CRFs are unable to achieve; we believe that this is a direct consequence of using contextual word representation-based feature vectors.", + "Runtimes We recorded the runtimes for learning word representations using the random indexing algorithm described earlier on the four datasets in Table 2 , and plot the runtimes in Figure 4 as a function of the total number of words in each corpus.", + "In agreement with the expected theoretical time-complexity of random indexing, the empirical run-time is linear in the number of words, for fixed parameter settings. More importantly, the absolute times show that the algorithm is extremely lightweight: on the D-ALL corpus, we are able to learn representations in under an hour.", + "We note that these results do not employ any obvious parallelization or the multi-core capabilities of the machine. The linear scaling properties of the algorithm show that it can be used even for very large Web corpora. In future, we will investigate an implementation of the algorithm in a distributed setting.", + "Robustness to corpus size and quality One issue with using large corpora to derive word representations is concept drift. The D-ALL corpora, for example, contains tens of different Web domains, even though they all pertain to human trafficking. An interesting empirical issue is whether a smaller corpus (e.g. D-10K or D-50K) contains enough data for the derived word representations to converge to reasonable values. Not only would this alleviate initial training times, but it would also partially compensate for concept drift, since it would be expected to contain fewer unique Web domains.", + "Tables 7 and 8 show that such generalization is possible. The best F1-Measure performance, in fact, is achieved for D-10K, although the average F1-Measures vary by a margin of less than 2% on all cases. We cite this as further evidence of the robustness of the overall approach.", + "Effects of feature selection Finally, we evaluate the effects of feature selection in Figure 5 on the GT-Text-Name dataset, with training percentage set at 30. The results show that, although performance is reasonably stable for a wide range of $k$ , some feature selection is necessary for better generalization." + ], + [ + "Table 9 contains some examples (in bold) of cities that got correctly extracted, with the bold term being assigned the highest score by the contextual classifier that was trained for cities. The examples provide good evidence for the kinds of variation (i.e. concept drift) that are often observed in real-world human trafficking data over multiple Web domains. Some domains, for example, were found to have the same kind of structured format as the second row of Table 9 (i.e. Location: followed by the actual locations), but many other domains were far more heterogeneous.", + "The results in this section also illustrate the merits of unsupervised feature engineering and contextual supervision. In principle, there is no reason why the word representation learning module in Figure 1 cannot be replaced by a more adaptive algorithm like Word2vec BIBREF25 . We note again that, before applying such algorithms, it is important to deal with the heterogeneity problem that arises from having many different Web domains present in the corpus. While earlier results in this section (Tables 7 and 8 ) showed that random indexing is reasonably stable as more websites are added to the corpus, we also verify this robustness qualitatively using a few domain-specific examples in Table 10 . We ran the qualitative experiment as follows: for each seed token (e.g. `tall'), we searched for the two nearest neighbors in the semantic space induced by random indexing by applying cosine similarity, using two different word representation datasets (D-10K and D-ALL). As the results in Table 10 show, the induced distributional semantics are stable; even when the nearest neighbors are different (e.g. for `tall'), their semantics still tend to be similar.", + "Another important point implied by both the qualitative and quantitative results on D-10K is that random indexing is able to generalize quickly even on small amounts of data. To the best of our knowledge, it is currently an open question (theoretically and empirically), at the time of writing, whether state-of-the-art neural embedding-based word representation learners can (1) generalize on small quantities of data, especially in a single epoch (`streaming data') (2) adequately compensate for concept drift with the same degree of robustness, and in the same lightweight manner, as the random indexing method that we adapted and evaluated in this paper. A broader empirical study on this issue is warranted.", + "Concerning contextual supervision, we qualitatively visualize the inputs to the contextual city classifier using the t-SNE tool BIBREF32 . We use the ground-truth labels to determine the color of each point in the projected 2d space. The plot in Figure 6 shows that there is a reasonable separation of labels; interestingly there are also `sub-clusters' among the positively labeled points. Each sub-cluster provides evidence for a similar context; the number of sub-clusters even in this small sample of points again illustrates the heterogeneity in the underlying data.", + "A last issue that we mention is the generalization of the method to more unconventional attributes than the ones evaluated herein. In ongoing work, we have experimented with more domain-specific attributes such as ethnicity (of escorts), and have achieved similar performance. In general, the presented method is applicable whenever the context around the extraction is a suitable clue for disambiguation." + ], + [ + "In this paper, we presented a lightweight, feature-agnostic Information Extraction approach that is suitable for illicit Web domains. Our approach relies on unsupervised derivation of word representations from an initial corpus, and the training of a supervised contextual classifier using external high-recall recognizers and a handful of manually verified annotations. Experimental evaluations show that our approach can outperform feature-centric CRF-based approaches for a range of generic attributes. Key modules of our prototype are publicly available (see footnote 15) and can be efficiently bootstrapped in a serial computing environment. Some of these modules are already being used in real-world settings. For example, they were recently released as tools for graduate-level participants in the End Human Trafficking hackathon organized by the office of the District Attorney of New York. At the time of writing, the system is being actively maintained and updated.", + "Acknowledgements The authors gratefully acknowledge the efforts of Lingzhe Teng, Rahul Kapoor and Vinay Rao Dandin, for sampling and producing the ground-truths in Table 3 . This research is supported by the Defense Advanced Research Projects Agency (DARPA) and the Air Force Research Laboratory (AFRL) under contract number FA8750- 14-C-0240. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of DARPA, AFRL, or the U.S. Government." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0689/instruction.md b/qasper-0689/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..111b0469221c891dba734ff65b7f9fada2a81a94 --- /dev/null +++ b/qasper-0689/instruction.md @@ -0,0 +1,104 @@ +Name of Paper: Semantic Role Labeling for Learner Chinese: the Importance of Syntactic Parsing and L2-L1 Parallel Data + +Question: What is the baseline model for the agreement-based mode? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "An L2-L1 Parallel Corpus", + "The Annotation Process", + "Inter-annotator Agreement", + "Three SRL Systems", + "Main Results", + "Analysis", + "Enhancing SRL with L2-L1 Parallel Data", + "The Method", + "Experimental Setup", + "Conclusion", + "Acknowledgement" + ], + "paragraphs": [ + [ + "A learner language (interlanguage) is an idiolect developed by a learner of a second or foreign language which may preserve some features of his/her first language. Previously, encouraging results of automatically building the syntactic analysis of learner languages were reported BIBREF0 , but it is still unknown how semantic processing performs, while parsing a learner language (L2) into semantic representations is the foundation of a variety of deeper analysis of learner languages, e.g., automatic essay scoring. In this paper, we study semantic parsing for interlanguage, taking semantic role labeling (SRL) as a case task and learner Chinese as a case language.", + "Before discussing a computation system, we first consider the linguistic competence and performance. Can human robustly understand learner texts? Or to be more precise, to what extent, a native speaker can understand the meaning of a sentence written by a language learner? Intuitively, the answer is towards the positive side. To validate this, we ask two senior students majoring in Applied Linguistics to carefully annotate some L2-L1 parallel sentences with predicate\u2013argument structures according to the specification of Chinese PropBank BIBREF1 , which is developed for L1. A high inter-annotator agreement is achieved, suggesting the robustness of language comprehension for L2. During the course of semantic annotation, we find a non-obvious fact that we can re-use the semantic annotation specification, Chinese PropBank in our case, which is developed for L1. Only modest rules are needed to handle some tricky phenomena. This is quite different from syntactic treebanking for learner sentences, where defining a rich set of new annotation heuristics seems necessary BIBREF2 , BIBREF0 , BIBREF3 .", + "Our second concern is to mimic the human's robust semantic processing ability by computer programs. The feasibility of reusing the annotation specification for L1 implies that we can reuse standard CPB data to train an SRL system to process learner texts. To test the robustness of the state-of-the-art SRL algorithms, we evaluate two types of SRL frameworks. The first one is a traditional SRL system that leverages a syntactic parser and heavy feature engineering to obtain explicit information of semantic roles BIBREF4 . Furthermore, we employ two different parsers for comparison: 1) the PCFGLA-based parser, viz. Berkeley parser BIBREF5 , and 2) a minimal span-based neural parser BIBREF6 . The other SRL system uses a stacked BiLSTM to implicitly capture local and non-local information BIBREF7 . and we call it the neural syntax-agnostic system. All systems can achieve state-of-the-art performance on L1 texts but show a significant degradation on L2 texts. This highlights the weakness of applying an L1-sentence-trained system to process learner texts.", + "While the neural syntax-agnostic system obtains superior performance on the L1 data, the two syntax-based systems both produce better analyses on the L2 data. Furthermore, as illustrated in the comparison between different parsers, the better the parsing results we get, the better the performance on L2 we achieve. This shows that syntactic parsing is important in semantic construction for learner Chinese. The main reason, according to our analysis, is that the syntax-based system may generate correct syntactic analyses for partial grammatical fragments in L2 texts, which provides crucial information for SRL. Therefore, syntactic parsing helps build more generalizable SRL models that transfer better to new languages, and enhancing syntactic parsing can improve SRL to some extent.", + "Our last concern is to explore the potential of a large-scale set of L2-L1 parallel sentences to enhance SRL systems. We find that semantic structures of the L2-L1 parallel sentences are highly consistent. This inspires us to design a novel agreement-based model to explore such semantic coherency information. In particular, we define a metric for comparing predicate\u2013argument structures and searching for relatively good automatic syntactic and semantic annotations to extend the training data for SRL systems. Experiments demonstrate the value of the L2-L1 parallel sentences as well as the effectiveness of our method. We achieve an F-score of 72.06, which is a 2.02 percentage point improvement over the best neural-parser-based baseline.", + "To the best of our knowledge, this is the first time that the L2-L1 parallel data is utilized to enhance NLP systems for learner texts.", + "For research purpose, we have released our SRL annotations on 600 sentence pairs and the L2-L1 parallel dataset ." + ], + [ + "An L2-L1 parallel corpus can greatly facilitate the analysis of a learner language BIBREF9 . Following mizumoto:2011, we collected a large dataset of L2-L1 parallel texts of Mandarin Chinese by exploring \u201clanguage exchange\" social networking services (SNS), i.e., Lang-8, a language-learning website where native speakers can freely correct the sentences written by foreign learners. The proficiency levels of the learners are diverse, but most of the learners, according to our judgment, is of intermediate or lower level.", + "Our initial collection consists of 1,108,907 sentence pairs from 135,754 essays. As there is lots of noise in raw sentences, we clean up the data by (1) ruling out redundant content, (2) excluding sentences containing foreign words or Chinese phonetic alphabet by checking the Unicode values, (3) dropping overly simple sentences which may not be informative, and (4) utilizing a rule-based classifier to determine whether to include the sentence into the corpus.", + "The final corpus consists of 717,241 learner sentences from writers of 61 different native languages, in which English and Japanese constitute the majority. As for completeness, 82.78% of the Chinese Second Language sentences on Lang-8 are corrected by native human annotators. One sentence gets corrected approximately 1.53 times on average.", + "In this paper, we manually annotate the predicate\u2013argument structures for the 600 L2-L1 pairs as the basis for the semantic analysis of learner Chinese. It is from the above corpus that we carefully select 600 pairs of L2-L1 parallel sentences. We would choose the most appropriate one among multiple versions of corrections and recorrect the L1s if necessary. Because word structure is very fundamental for various NLP tasks, our annotation also contains gold word segmentation for both L2 and L1 sentences. Note that there are no natural word boundaries in Chinese text. We first employ a state-of-the-art word segmentation system to produce initial segmentation results and then manually fix segmentation errors.", + "The dataset includes four typologically different mother tongues, i.e., English (ENG), Japanese (JPN), Russian (RUS) and Arabic (ARA). Sub-corpus of each language consists of 150 sentence pairs. We take the mother languages of the learners into consideration, which have a great impact on grammatical errors and hence automatic semantic analysis. We hope that four selected mother tongues guarantee a good coverage of typologies. The annotated corpus can be used both for linguistic investigation and as test data for NLP systems." + ], + [ + "Semantic role labeling (SRL) is the process of assigning semantic roles to constituents or their head words in a sentence according to their relationship to the predicates expressed in the sentence. Typical semantic roles can be divided into core arguments and adjuncts. The core arguments include Agent, Patient, Source, Goal, etc, while the adjuncts include Location, Time, Manner, Cause, etc.", + "To create a standard semantic-role-labeled corpus for learner Chinese, we first annotate a 50-sentence trial set for each native language. Two senior students majoring in Applied Linguistics conducted the annotation. Based on a total of 400 sentences, we adjudicate an initial gold standard, adapting and refining CPB specification as our annotation heuristics. Then the two annotators proceed to annotate a 100-sentence set for each language independently. It is on these larger sets that we report the inter-annotator agreement.", + "In the final stage, we also produce an adjudicated gold standard for all 600 annotated sentences. This was achieved by comparing the annotations selected by each annotator, discussing the differences, and either selecting one as fully correct or creating a hybrid representing the consensus decision for each choice point. When we felt that the decisions were not already fully guided by the existing annotation guidelines, we worked to articulate an extension to the guidelines that would support the decision.", + "During the annotation, the annotators apply both position labels and semantic role labels. Position labels include S, B, I and E, which are used to mark whether the word is an argument by itself, or at the beginning or in the middle or at the end of a argument. As for role labels, we mainly apply representations defined by CPB BIBREF1 . The predicate in a sentence was labeled as rel, the core semantic roles were labeled as AN and the adjuncts were labeled as AM." + ], + [ + "For inter-annotator agreement, we evaluate the precision (P), recall (R), and F1-score (F) of the semantic labels given by the two annotators. Table TABREF5 shows that our inter-annotator agreement is promising. All L1 texts have F-score above 95, and we take this as a reflection that our annotators are qualified. F-scores on L2 sentences are all above 90, just a little bit lower than those of L1, indicating that L2 sentences can be greatly understood by native speakers. Only modest rules are needed to handle some tricky phenomena:", + "The labeled argument should be strictly limited to the core roles defined in the frameset of CPB, though the number of arguments in L2 sentences may be more or less than the number defined.", + "For the roles in L2 that cannot be labeled as arguments under the specification of CPB, if they provide semantic information such as time, location and reason, we would labeled them as adjuncts though they may not be well-formed adjuncts due to the absence of function words.", + "For unnecessary roles in L2 caused by mistakes of verb subcategorization (see examples in Figure FIGREF30 ), we would leave those roles unlabeled.", + "Table TABREF10 further reports agreements on each argument (AN) and adjunct (AM) in detail, according to which the high scores are attributed to the high agreement on arguments (AN). The labels of A3 and A4 have no disagreement since they are sparse in CPB and are usually used to label specific semantic roles that have little ambiguity.", + "We also conducted in-depth analysis on inter-annotator disagreement. For further details, please refer to duan2018argument." + ], + [ + "The work on SRL has included a broad spectrum of machine learning and deep learning approaches to the task. Early work showed that syntactic information is crucial for learning long-range dependencies, syntactic constituency structure and global constraints BIBREF10 , BIBREF11 , while initial studies on neural methods achieved state-of-the-art results with little to no syntactic input BIBREF12 , BIBREF13 , BIBREF14 , BIBREF7 . However, the question whether fully labeled syntactic structures provide an improvement for neural SRL is still unsettled pending further investigation.", + "To evaluate the robustness of state-of-the-art SRL algorithms, we evaluate two representative SRL frameworks. One is a traditional syntax-based SRL system that leverages a syntactic parser and manually crafted features to obtain explicit information to find semantic roles BIBREF15 , BIBREF16 In particular, we employ the system introduced in BIBREF4 . This system first collects all c-commanders of a predicate in question from the output of a parser and puts them in order. It then employs a first order linear-chain global linear model to perform semantic tagging. For constituent parsing, we use two parsers for comparison, one is Berkeley parser BIBREF5 , a well-known implementation of the unlexicalized latent variable PCFG model, the other is a minimal span-based neural parser based on independent scoring of labels and spans BIBREF6 . As proposed in BIBREF6 , the second parser is capable of achieving state-of-the-art single-model performance on the Penn Treebank. On the Chinese TreeBank BIBREF17 , it also outperforms the Berkeley parser for the in-domain test. We call the corresponding SRL systems as the PCFGLA-parser-based and neural-parser-based systems.", + "The second SRL framework leverages an end-to-end neural model to implicitly capture local and non-local information BIBREF12 , BIBREF7 . In particular, this framework treats SRL as a BIO tagging problem and uses a stacked BiLSTM to find informative embeddings. We apply the system introduced in BIBREF7 for experiments. Because all syntactic information (including POS tags) is excluded, we call this system the neural syntax-agnostic system.", + "To train the three SRL systems as well as the supporting parsers, we use the CTB and CPB data . In particular, the sentences selected for the CoNLL 2009 shared task are used here for parameter estimation. Note that, since the Berkeley parser is based on PCFGLA grammar, it may fail to get the syntactic outputs for some sentences, while the other parser does not have that problem. In this case, we have made sure that both parsers can parse all 1,200 sentences successfully." + ], + [ + "The overall performances of the three SRL systems on both L1 and L2 data (150 parallel sentences for each mother tongue) are shown in Table TABREF11 . For all systems, significant decreases on different mother languages can be consistently observed, highlighting the weakness of applying L1-sentence-trained systems to process learner texts. Comparing the two syntax-based systems with the neural syntax-agnostic system, we find that the overall INLINEFORM0 F, which denotes the F-score drop from L1 to L2, is smaller in the syntax-based framework than in the syntax-agnostic system. On English, Japanese and Russian L2 sentences, the syntax-based system has better performances though it sometimes works worse on the corresponding L1 sentences, indicating the syntax-based systems are more robust when handling learner texts.", + "Furthermore, the neural-parser-based system achieves the best overall performance on the L2 data. Though performing slightly worse than the neural syntax-agnostic one on the L1 data, it has much smaller INLINEFORM0 F, showing that as the syntactic analysis improves, the performances on both the L1 and L2 data grow, while the gap can be maintained. This demonstrates again the importance of syntax in semantic constructions, especially for learner texts.", + "Table TABREF45 summarizes the SRL results of the baseline PCFGLA-parser-based model as well as its corresponding retrained models. Since both the syntactic parser and the SRL classifier can be retrained and thus enhanced, we report the individual impact as well as the combined one. We can clearly see that when the PCFGLA parser is retrained with the SRL-consistent sentence pairs, it is able to provide better SRL-oriented syntactic analysis for the L2 sentences as well as their corrections, which are essentially L1 sentences. The outputs of the L1 sentences that are generated by the deep SRL system are also useful for improving the linear SRL classifier. A non-obvious fact is that such a retrained model yields better analysis for not only L1 but also L2 sentences. Fortunately, combining both results in further improvement.", + "Table TABREF46 shows the results of the parallel experiments based on the neural parser. Different from the PCFGLA model, the SRL-consistent trees only yield a slight improvement on the L2 data. On the contrary, retraining the SRL classifier is much more effective. This experiment highlights the different strengths of different frameworks for parsing. Though for standard in-domain test, the neural parser performs better and thus is more and more popular, for some other scenarios, the PCFGLA model is stronger.", + "Table TABREF47 further shows F-scores for the baseline and the both-retrained model relative to each role type in detail. Given that the F-scores for both models are equal to 0 on A3 and A4, we just omit this part. From the figure we can observe that, all the semantic roles achieve significant improvements in performances." + ], + [ + "To better understand the overall results, we further look deep into the output by addressing the questions:", + "What types of error negatively impact both systems over learner texts?", + "What types of error are more problematic for the neural syntax-agnostic one over the L2 data but can be solved by the syntax-based one to some extent?", + "We first carry out a suite of empirical investigations by breaking down error types for more detailed evaluation. To compare two systems, we analyze results on ENG-L2 and JPN-L2 given that they reflect significant advantages of the syntax-based systems over the neural syntax-agnostic system. Note that the syntax-based system here refers to the neural-parser-based one. Finally, a concrete study on the instances in the output is conducted, as to validate conclusions in the previous step.", + "We employ 6 oracle transformations designed by he2017deep to fix various prediction errors sequentially (see details in Table TABREF19 ), and observe the relative improvements after each operation, as to obtain fine-grained error types. Figure FIGREF21 compares two systems in terms of different mistakes on ENG-L2 and JPN-L2 respectively. After fixing the boundaries of spans, the neural syntax-agnostic system catches up with the other, illustrating that though both systems handle boundary detection poorly on the L2 sentences, the neural syntax-agnostic one suffers more from this type of errors.", + "Excluding boundary errors (after moving, merging, splitting spans and fixing boundaries), we also compare two systems on L2 in terms of detailed label identification, so as to observe which semantic role is more likely to be incorrectly labeled. Figure FIGREF24 shows the confusion matrices. Comparing (a) with (c) and (b) with (d), we can see that the syntax-based and the neural system often overly label A1 when processing learner texts. Besides, the neural syntax-agnostic system predicts the adjunct AM more than necessary on L2 sentences by 54.24% compared with the syntax-based one.", + "On the basis of typical error types found in the previous stage, specifically, boundary detection and incorrect labels, we further conduct an on-the-spot investigation on the output sentences.", + "Previous work has proposed that the drop in performance of SRL systems mainly occurs in identifying argument boundaries BIBREF18 . According to our results, this problem will be exacerbated when it comes to L2 sentences, while syntactic structure sometimes helps to address this problem.", + "Figure FIGREF30 is an example of an output sentence. The Chinese word \u201c\u4e5f\u201d (also) usually serves as an adjunct but is now used for linking the parallel structure \u201c\u7528 \u6c49\u8bed \u4e5f \u8bf4\u8bdd \u5feb\u201d (using Chinese also speaking quickly) in this sentence, which is ill-formed to native speakers and negatively affects the boundary detection of A0 for both systems.", + "On the other hand, the neural system incorrectly takes the whole part before \u201c\u5f88 \u96be\u201d (very hard) as A0, regardless of the adjunct \u201c\u5bf9 \u6211 \u6765\u8bf4\u201d (for me), while this can be figured out by exploiting syntactic analysis, as illustrated in Figure FIGREF30 . The constituent \u201c\u5bf9 \u6211 \u6765\u8bf4\u201d (for me) has been recognized as a prepositional phrase (PP) attached to the VP, thus labeled as AM. This shows that by providing information of some well-formed sub-trees associated with correct semantic roles, the syntactic system can perform better than the neural one on SRL for learner texts.", + "A second common source of errors is wrong labels, especially for A1. Based on our quantitative analysis, as reported in Table TABREF37 , these phenomena are mainly caused by mistakes of verb subcategorization, where the systems label more arguments than allowed by the predicates. Besides, the deep end-to-end system is also likely to incorrectly attach adjuncts AM to the predicates.", + "Figure FIGREF30 is another example. The Chinese verb \u201c\u505a\u996d\u201d (cook-meal) is intransitive while this sentence takes it as a transitive verb, which is very common in L2. Lacking in proper verb subcategorization, both two systems fail to recognize those verbs allowing only one argument and label the A1 incorrectly.", + "As for AM, the neural system mistakenly adds the adjunct to the predicate, which can be avoided by syntactic information of the sentence shown in Figure FIGREF30 . The constituent \u201c\u5e38\u5e38\u201d (often) are adjuncts attached to VP structure governed by the verb \u201c\u7ec3\u4e60\u201d(practice), which will not be labeled as AM in terms of the verb \u201c\u505a\u996d\u201d(cook-meal). In other words, the hierarchical structure can help in argument identification and assignment by exploiting local information." + ], + [ + "We explore the valuable information about the semantic coherency encoded in the L2-L1 parallel data to improve SRL for learner Chinese. In particular, we introduce an agreement-based model to search for high-quality automatic syntactic and semantic role annotations, and then use these annotations to retrain the two parser-based SRL systems." + ], + [ + "For the purpose of harvesting the good automatic syntactic and semantic analysis, we consider the consistency between the automatically produced analysis of a learner sentence and its corresponding well-formed sentence. Determining the measurement metric for comparing predicate\u2013argument structures, however, presents another challenge, because the words of the L2 sentence and its L1 counterpart do not necessarily match. To solve the problem, we use an automatic word aligner. BerkeleyAligner BIBREF19 , a state-of-the-art tool for obtaining a word alignment, is utilized.", + "The metric for comparing SRL results of two sentences is based on recall of INLINEFORM0 tuples, where INLINEFORM1 is a predicate, INLINEFORM2 is a word that is in the argument or adjunct of INLINEFORM3 and INLINEFORM4 is the corresponding role. Based on a word alignment, we define the shared tuple as a mutual tuple between two SRL results of an L2-L1 sentence pair, meaning that both the predicate and argument words are aligned respectively, and their role relations are the same. We then have two recall values:", + "L2-recall is (# of shared tuples) / (# of tuples of the result in L2)", + "L1-recall is (# of shared tuples) / (# of tuples of the result in L1)", + "In accordance with the above evaluation method, we select the automatic analysis of highest scoring sentences and use them to expand the training data. Sentences whose L1 and L2 recall are both greater than a threshold INLINEFORM0 are taken as good ones. A parser-based SRL system consists of two essential modules: a syntactic parser and a semantic classifier. To enhance the syntactic parser, the automatically generated syntactic trees of the sentence pairs that exhibit high semantic consistency are directly used to extend training data. To improve a semantic classifier, besides the consistent semantic analysis, we also use the outputs of the L1 but not L2 data which are generated by the neural syntax-agnostic SRL system." + ], + [ + "Our SRL corpus contains 1200 sentences in total that can be used as an evaluation for SRL systems. We separate them into three data sets. The first data set is used as development data, which contains 50 L2-L1 sentence pairs for each language and 200 pairs in total. Hyperparameters are tuned using the development set. The second data set contains all other 400 L2 sentences, which is used as test data for L2. Similarly, all other 400 L1 sentences are used as test data for L1.", + "The sentence pool for extracting retraining annotations includes all English- and Japanese-native speakers' data along with its corrections. Table TABREF43 presents the basic statistics. Around 8.5 \u2013 11.9% of the sentence can be taken as high L1/L2 recall sentences, which serves as a reflection that argument structure is vital for language acquisition and difficult for learners to master, as proposed in vazquez2004learning and shin2010contribution. The threshold ( INLINEFORM0 ) for selecting sentences is set upon the development data. For example, we use additional 156,520 sentences to enhance the Berkeley parser." + ], + [ + "Statistical models of annotating learner texts are making rapid progress. Although there have been some initial studies on defining annotation specification as well as corpora for syntactic analysis, there is almost no work on semantic parsing for interlanguages. This paper discusses this topic, taking Semantic Role Labeling as a case task and learner Chinese as a case language. We reveal three unknown facts that are important towards a deeper analysis of learner languages: (1) the robustness of language comprehension for interlanguage, (2) the weakness of applying L1-sentence-trained systems to process learner texts, and (3) the significance of syntactic parsing and L2-L1 parallel data in building more generalizable SRL models that transfer better to L2. We have successfully provided a better SRL-oriented syntactic parser as well as a semantic classifier for processing the L2 data by exploring L2-L1 parallel data, supported by a significant numeric improvement over a number of state-of-the-art systems. To the best of our knowledge, this is the first work that demonstrates the effectiveness of large-scale L2-L1 parallel data to enhance the NLP system for learner texts." + ], + [ + "This work was supported by the National Natural Science Foundation of China (61772036, 61331011) and the Key Laboratory of Science, Technology and Standard in Press Industry (Key Laboratory of Intelligent Press Media Technology). We thank the anonymous reviewers and for their helpful comments. We also thank Nianwen Xue for useful comments on the final version. Weiwei Sun is the corresponding author." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0701/instruction.md b/qasper-0701/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..b6200efb7e419801bb6ddade0809669f73031f59 --- /dev/null +++ b/qasper-0701/instruction.md @@ -0,0 +1,73 @@ +Name of Paper: VAIS Hate Speech Detection System: A Deep Learning based Approach for System Combination + +Question: What classifier do they use? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "System description", + "System description ::: System overview", + "System description ::: Data pre-processing", + "System description ::: Models architecture", + "System description ::: Ensemble method", + "Experiment", + "Conclusion" + ], + "paragraphs": [ + [ + "Currently, social networks are so popular. Some of the biggest ones include Facebook, Twitter, Youtube,... with extremely number of users. Thus, controlling content of those platforms is essential. For years, social media companies such as Twitter, Facebook, and YouTube have been investing hundreds of millions euros on this task BIBREF0, BIBREF1. However, their effort is not enough since such efforts are primarily based on manual moderation to identify and delete offensive materials. The process is labour intensive, time consuming, and not sustainable or scalable in reality BIBREF2, BIBREF0, BIBREF3.", + "In the sixth international workshop on Vietnamese Language and Speech Processing (VLSP 2019), the Hate Speech Detection (HSD) task is proposed as one of the shared-tasks to handle the problem related to controlling content in SNSs. HSD is required to build a multi-class classification model that is capable of classifying an item to one of 3 classes (hate, offensive, clean). Hate speech (hate): an item is identified as hate speech if it (1) targets individuals or groups on the basis of their characteristics; (2) demonstrates a clear intention to incite harm, or to promote hatred; (3) may or may not use offensive or profane words. Offensive but not hate speech (offensive): an item (posts/comments) may contain offensive words but it does not target individuals or groups on the basis of their characteristics. Neither offensive nor hate speech (clean): normal item, it does not contain offensive language or hate speech.", + "The term `hate speech' was formally defined as `any communication that disparages a person or a group on the basis of some characteristics (to be referred to as types of hate or hate classes) such as race, colour, ethnicity, gender, sexual orientation, nationality, religion, or other characteristics' BIBREF4. Many researches have been conducted in recent years to develop automatic methods for hate speech detection in the social media domain. These typically employ semantic content analysis techniques built on Natural Language Processing (NLP) and Machine Learning (ML) methods. The task typically involves classifying textual content into non-hate or hateful. This HSD task is much more difficult when it requires classify text in three classes, with hate and offensive class quite hard to classify even with humans.", + "In this paper, we propose a method to handle this HSD problem. Our system combines multiple text representations and models architecture in order to make diverse predictions. The system is heavily based on the ensemble method. The next section will present detail of our system including data preparation (how we clean text and build text representation), architecture of the model using in the system, and how we combine them together. The third section is our experiment and result report in HSD shared-task VLSP 2019. The final section is our conclusion with advantages and disadvantages of the system following by our perspective." + ], + [ + "In this section, we present the system architecture. It includes how we pre-process text, what types of text representation we use and models used in our system. In the end, we combine model results by using an ensemble technique." + ], + [ + "The fundamental idea of this system is how to make a system that has the diversity of viewing an input. That because of the variety of the meaning in Vietnamese language especially with the acronym, teen code type. To make this diversity, after cleaning raw text input, we use multiple types of word tokenizers. Each one of these tokenizers, we combine with some types of representation methods, including word to vector methods such as continuous bag of words BIBREF5, pre-trained embedding as fasttext (trained on Wiki Vietnamese language) BIBREF6 and sonvx (trained on Vietnamese newspaper) BIBREF7. Each sentence has a set of words corresponding to a set of word vectors, and that set of word vectors is a representation of a sentence. We also make a sentence embedding by using RoBERTa architecture BIBREF8. CBOW and RoBERTa models trained on text from some resources including VLSP 2016 Sentiment Analysis, VLSP 2018 Sentiment Analysis, VLSP 2019 HSD and text crawled from Facebook. After having sentence representation, we use some classification models to classify input sentences. Those models will be described in detail in the section SECREF13. With the multiply output results, we will use an ensemble method to combine them and output the final result. Ensemble method we use here is Stacking method will be introduced in the section SECREF16." + ], + [ + "Content in the dataset that provided in this HSD task is very diverse. Words having the same meaning were written in various types (teen code, non tone, emojis,..) depending on the style of users. Dataset was crawled from various sources with multiple text encodes. In order to make it easy for training, all types of encoding need to be unified. This cleaning module will be used in two processes: cleaning data before training and cleaning input in inferring phase. Following is the data processing steps that we use:", + "Step 1: Format encoding. Vietnamese has many accents, intonations with different Unicode typing programs which may have different outputs with the same typing type. To make it unified, we build a library named visen. For example, the input \"th\u00ed\u00eat k\u00ea will be normalized to \"thi\u1ebft k\u1ebf\" as the output.", + "Step 2: In social networks, people show their feelings a lot by emojis. Emoticon is often a special Unicode character, but sometimes, it is combined by multiple normal characters like `: ( = ]'. We make a dictionary mapping this emoji (combined by some characters) to a single Unicode character like other emojis to make it unified.", + "Step 3: Remove unseen characters. For human, unseen character is invisible but for a computer, it makes the model harder to process and inserts space between words, punctuation and emoji. This step aims at reducing the number of words in the dictionary which is important task, especially with low dataset resources like this HSD task.", + "Step 4: With model requiring Vietnamese word segmentation as the input, we use BIBREF9, BIBREF10 to tokenize the input text.", + "Step 5: Make all string lower. We experimented and found that lower-case or upper-case are not a significant impact on the result, but with lower characters, the number of words in the dictionary is reduced.", + "RoBERTa proposed in BIBREF8 an optimized method for pretraining self-supervised NLP systems. In our system, we use RoBERTa not only to make sentence representation but also to augment data. With mask mechanism, we replace a word in the input sentence with another word that RoBERTa model proposes. To reduce the impact of replacement word, the chosen words are all common words that appear in almost three classes of the dataset. For example, with input `nh\u1ed5n l\u00e0m g\u1eaft vl', we can augment to other outputs: `vl l\u00e0m g\u1eaft q\u00e1', `c\u00f2n l\u00e0m vl v\u1eady', `vl l\u00e0m \u0111\u1ec9nh vl' or `thanh ch\u00fat g\u1eaft vl'.", + "british" + ], + [ + "Social comment dataset has high variety, the core idea is using multiple model architectures to handle data in many viewpoints. In our system, we use five different model architectures combining many types of CNN, and RNN. Each model will use some types of word embedding or handle directly sentence embedding to achieve the best general result. Source code of five models is extended from the GitHub repository", + "The first model is TextCNN (figure FIGREF2) proposed in BIBREF11. It only contains CNN blocks following by some Dense layers. The output of multiple CNN blocks with different kernel sizes is connected to each other.", + "The second model is VDCNN (figure FIGREF5) inspired by the research in BIBREF12. Like the TextCNN model, it contains multiple CNN blocks. The addition in this model is its residual connection.", + "The third model is a simple LSTM bidirectional model (figure FIGREF15). It contains multiple LSTM bidirectional blocks stacked to each other.", + "The fourth model is LSTMCNN (figure FIGREF24). Before going through CNN blocks, series of word embedding will be transformed by LSTM bidirectional block.", + "The final model is the system named SARNN (figure FIGREF25). It adds an attention block between LTSM blocks." + ], + [ + "Ensemble methods is a machine learning technique that combines several base models in order to produce one optimal predictive model. Have the main three types of ensemble methods including Bagging, Boosting and Stacking. In this system, we use the Stacking method. In this method, the output of each model is not only class id but also the probability of each class in the set of three classes. This probability will become a feature for the ensemble model. The stacking ensemble model here is a simple full-connection model with input is all of probability that output from sub-model. The output is the probability of each class." + ], + [ + "The dataset in this HSD task is really imbalance. Clean class dominates with 91.5%, offensive class takes 5% and the rest belongs to hate class with 3.5%. To make model being able to learn with this imbalance data, we inject class weight to the loss function with the corresponding ratio (clean, offensive, hate) is $(0.09, 0.95, 0.96)$. Formular DISPLAY_FORM17 is the loss function apply for all models in our system. $w_i$ is the class weight, $y_i$ is the ground truth and $\\hat{y}_i$ is the output of the model. If the class weight is not set, we find that model cannot adjust parameters. The model tends to output all clean classes.", + "We experiment 8 types of embedding in total:", + "comment: CBOW embedding training in all dataset comment, each word is splited by space. Embedding size is 200.", + "comment_bpe: CBOW embedding training in all dataset comment, each word is splited by subword bpe. Embedding size is 200.", + "comment_tokenize: CBOW embedding training in all dataset comment, each word is splited by space. Before split by space, word is concatenated by using BIBREF9, BIBREF13, BIBREF10. Embedding size is 200.", + "roberta: sentence embedding training in all dataset comment, training by using RoBERTa architecture. Embedding size is 256.", + "fasttext, sonvx* is all pre-trained word embedding in general domain. Before mapping word to vector, word is concatenated by using BIBREF9, BIBREF13, BIBREF10. Embedding size of fasttext is 300. (sonvx_wiki, sonvx_baomoi_w2, sonvx_baomoi_w5) have embedding size corresponding is (400, 300, 400).", + "In our experiment, the dataset is split into two-part: train set and dev set with the corresponding ratio $(0.9, 0.1)$. Two subsets have the same imbalance ratio like the root set. For each combination of model and word embedding, we train model in train set until it achieve the best result of loss score in the dev set. The table TABREF12 shows the best result of each combination on the f1_macro score.", + "For each model having the best fit on the dev set, we export the probability distribution of classes for each sample in the dev set. In this case, we only use the result of model that has f1_macro score that larger than 0.67. The probability distribution of classes is then used as feature to input into a dense model with only one hidden layer (size 128). The training process of the ensemble model is done on samples of the dev set. The best fit result is 0.7356. The final result submitted in public leaderboard is 0.73019 and in private leaderboard is 0.58455. It is quite different in bad way. That maybe is the result of the model too overfit on train set tuning on public test set.", + "Statistics of the final result on the dev set shows that almost cases have wrong prediction from offensive and hate class to clean class belong to samples containing the word `vl'. (62% in the offensive class and 48% in the hate class). It means that model overfit the word `vl' to the clean class. This makes sense because `vl' appears too much in the clean class dataset.", + "In case the model predicts wrong from the clean class to the offensive class and the hate class, the model tends to decide case having sensitive words to be wrong class. The class offensive and the hate are quite difficult to distinguish even with human." + ], + [ + "In this study, we experiment the combination of multiple embedding types and multiple model architecture to solve a part of the problem Hate Speech Detection with a signification good classification results. Our system heavily based on the ensemble technique so the weakness of the system is slow processing speed. But in fact, it is not big trouble with this HSD problem when human usually involve handling directly in the before.", + "HSD is a hard problem even with human. In order to improve classification quality, in the future, we need to collect more data especially social networks content. This will make building text representation more correct and help model easier to classify.", + "british" + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0706/instruction.md b/qasper-0706/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..8b6d2df4e88656a4133b8cc04a83673db16ade4f --- /dev/null +++ b/qasper-0706/instruction.md @@ -0,0 +1,109 @@ +Name of Paper: Yoga-Veganism: Correlation Mining of Twitter Health Data + +Question: What other interesting correlations are observed? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Data Collection", + "Apache Kafka", + "Apache Zookeeper", + "Data Extraction using Tweepy", + "Data Pre-processing", + "Methodology", + "Construct document-term matrix", + "Topic Modeling", + "Optimal number of Topics", + "Topic Inference", + "Manual Annotation", + "Visualization", + "Topic Frequency Distribution", + "Comparison with Ground Truth", + "Observation and Future Work", + "Conclusions" + ], + "paragraphs": [ + [ + "The main motivation of this work has been started with a question \"What do people do to maintain their health?\"\u2013 some people do balanced diet, some do exercise. Among diet plans some people maintain vegetarian diet/vegan diet, among exercises some people do swimming, cycling or yoga. There are people who do both. If we want to know the answers of the following questions\u2013 \"How many people follow diet?\", \"How many people do yoga?\", \"Does yogi follow vegetarian/vegan diet?\", may be we could ask our acquainted person but this will provide very few intuition about the data. Nowadays people usually share their interests, thoughts via discussions, tweets, status in social media (i.e. Facebook, Twitter, Instagram etc.). It's huge amount of data and it's not possible to go through all the data manually. We need to mine the data to get overall statistics and then we will also be able to find some interesting correlation of data.", + "Several works have been done on prediction of social media content BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 . Prieto et al. proposed a method to extract a set of tweets to estimate and track the incidence of health conditions in society BIBREF5 . Discovering public health topics and themes in tweets had been examined by Prier et al. BIBREF6 . Yoon et al. described a practical approach of content mining to analyze tweet contents and illustrate an application of the approach to the topic of physical activity BIBREF7 .", + "Twitter data constitutes a rich source that can be used for capturing information about any topic imaginable. In this work, we use text mining to mine the Twitter health-related data. Text mining is the application of natural language processing techniques to derive relevant information BIBREF8 . Millions of tweets are generated each day on multifarious issues BIBREF9 . Twitter mining in large scale has been getting a lot of attention last few years. Lin and Ryaboy discussed the evolution of Twitter infrastructure and the development of capabilities for data mining on \"big data\" BIBREF10 . Pandarachalil et al. provided a scalable and distributed solution using Parallel python framework for Twitter sentiment analysis BIBREF9 . Large-scale Twitter Mining for drug-related adverse events was developed by Bian et al. BIBREF11 .", + "In this paper, we use parallel and distributed technology Apache Kafka BIBREF12 to handle the large streaming twitter data. The data processing is conducted in parallel with data extraction by integration of Apache Kafka and Spark Streaming. Then we use Topic Modeling to infer semantic structure of the unstructured data (i.e Tweets). Topic Modeling is a text mining technique which automatically discovers the hidden themes from given documents. It is an unsupervised text analytic algorithm that is used for finding the group of words from the given document. We build the model using three different algorithms Latent Semantic Analysis (LSA) BIBREF13 , Non-negative Matrix Factorization (NMF) BIBREF14 , and Latent Dirichlet Allocation (LDA) BIBREF15 and infer the topic of tweets. To observe the model behavior, we test the model to infer new tweets. The implication of our work is to annotate unlabeled data using the model and find interesting correlation." + ], + [ + "Tweet messages are retrieved from the Twitter source by utilizing the Twitter API and stored in Kafka topics. The Producer API is used to connect the source (i.e. Twitter) to any Kafka topic as a stream of records for a specific category. We fetch data from a source (Twitter), push it to a message queue, and consume it for further analysis. Fig. FIGREF2 shows the overview of Twitter data collection using Kafka." + ], + [ + "In order to handle the large streaming twitter data, we use parallel and distributed technology for big data framework. In this case, the output of the twitter crawling is queued in messaging system called Apache Kafka. This is a distributed streaming platform created and open sourced by LinkedIn in 2011 BIBREF12 . We write a Producer Client which fetches latest tweets continuously using Twitter API and push them to single node Kafka Broker. There is a Consumer that reads data from Kafka (Fig. FIGREF2 )." + ], + [ + "Apache Zookeeper is a distributed, open-source configuration, synchronization service along with naming registry for distributed applications. Kafka uses Zookeeper to store metadata about the Kafka cluster, as well as consumer client details." + ], + [ + "The twitter data has been crawled using Tweepy which is a Python library for accessing the Twitter API. We use Twitter streaming API to extract 40k tweets (April 17-19, 2019). For the crawling, we focus on several keywords that are related to health. The keywords are processed in a non-case-sensitive way. We use filter to stream all tweets containing the word `yoga', `healthylife', `healthydiet', `diet',`hiking', `swimming', `cycling', `yogi', `fatburn', `weightloss', `pilates', `zumba', `nutritiousfood', `wellness', `fitness', `workout', `vegetarian', `vegan', `lowcarb', `glutenfree', `calorieburn'.", + "The streaming API returns tweets, as well as several other types of messages (e.g. a tweet deletion notice, user update profile notice, etc), all in JSON format. We use Python libraries json for parsing the data, pandas for data manipulation." + ], + [ + "Data pre-processing is one of the key components in many text mining algorithms BIBREF8 . Data cleaning is crucial for generating a useful topic model. We have some prerequisites i.e. we download the stopwords from NLTK (Natural Language Toolkit) and spacy's en model for text pre-processing.", + "It is noticeable that the parsed full-text tweets have many emails, `RT', newline and extra spaces that is quite distracting. We use Python Regular Expressions (re module) to get rid of them. Then we tokenize each text into a list of words, remove punctuation and unnecessary characters. We use Python Gensim package for further processing. Gensim's simple_preprocess() is used for tokenization and removing punctuation. We use Gensim's Phrases model to build bigrams. Certain parts of English speech, like conjunctions (\"for\", \"or\") or the word \"the\" are meaningless to a topic model. These terms are called stopwords and we remove them from the token list. We use spacy model for lemmatization to keep only noun, adjective, verb, adverb. Stemming words is another common NLP technique to reduce topically similar words to their root. For example, \"connect\", \"connecting\", \"connected\", \"connection\", \"connections\" all have similar meanings; stemming reduces those terms to \"connect\". The Porter stemming algorithm BIBREF16 is the most widely used method." + ], + [ + "We use Twitter health-related data for this analysis. In subsections [subsec:3.1]3.1, [subsec:3.2]3.2, [subsec:3.3]3.3, and [subsec:3.4]3.4 elaborately present how we can infer the meaning of unstructured data. Subsection [subsec:3.5]3.5 shows how we do manual annotation for ground truth comparison. Fig. FIGREF6 shows the overall pipeline of correlation mining." + ], + [ + "The result of the data cleaning stage is texts, a tokenized, stopped, stemmed and lemmatized list of words from a single tweet. To understand how frequently each term occurs within each tweet, we construct a document-term matrix using Gensim's Dictionary() function. Gensim's doc2bow() function converts dictionary into a bag-of-words. In the bag-of-words model, each tweet is represented by a vector in a m-dimensional coordinate space, where m is number of unique terms across all tweets. This set of terms is called the corpus vocabulary." + ], + [ + "Topic modeling is a text mining technique which provides methods for identifying co-occurring keywords to summarize collections of textual information. This is used to analyze collections of documents, each of which is represented as a mixture of topics, where each topic is a probability distribution over words BIBREF17 . Applying these models to a document collection involves estimating the topic distributions and the weight each topic receives in each document. A number of algorithms exist for solving this problem. We use three unsupervised machine learning algorithms to explore the topics of the tweets: Latent Semantic Analysis (LSA) BIBREF13 , Non-negative Matrix Factorization (NMF) BIBREF14 , and Latent Dirichlet Allocation (LDA) BIBREF15 . Fig. FIGREF7 shows the general idea of topic modeling methodology. Each tweet is considered as a document. LSA, NMF, and LDA use Bag of Words (BoW) model, which results in a term-document matrix (occurrence of terms in a document). Rows represent terms (words) and columns represent documents (tweets). After completing topic modeling, we identify the groups of co-occurring words in tweets. These group co-occurring related words makes \"topics\".", + "LSA (Latent Semantic Analysis) BIBREF13 is also known as LSI (Latent Semantic Index). It learns latent topics by performing a matrix decomposition on the document-term matrix using Singular Value Decomposition (SVD) BIBREF18 . After corpus creation in [subsec:3.1]Subsection 3.1, we generate an LSA model using Gensim.", + "Non-negative Matrix Factorization (NMF) BIBREF14 is a widely used tool for the analysis of high-dimensional data as it automatically extracts sparse and meaningful features from a set of non-negative data vectors. It is a matrix factorization method where we constrain the matrices to be non-negative.", + "We apply Term Weighting with term frequency-inverse document frequency (TF-IDF) BIBREF19 to improve the usefulness of the document-term matrix (created in [subsec:3.1]Subsection 3.1) by giving more weight to the more \"important\" terms. In Scikit-learn, we can generate at TF-IDF weighted document-term matrix by using TfidfVectorizer. We import the NMF model class from sklearn.decomposition and fit the topic model to tweets.", + "Latent Dirichlet Allocation (LDA) BIBREF15 is widely used for identifying the topics in a set of documents, building on Probabilistic Latent Semantic Analysis (PLSI) BIBREF20 . LDA considers each document as a collection of topics in a certain proportion and each topic as a collection of keywords in a certain proportion. We provide LDA the optimal number of topics, it rearranges the topics' distribution within the documents and keywords' distribution within the topics to obtain a good composition of topic-keywords distribution.", + "We have corpus generated in [subsec:3.1]Subsection 3.1 to train the LDA model. In addition to the corpus and dictionary, we provide the number of topics as well." + ], + [ + "Topic modeling is an unsupervised learning, so the set of possible topics are unknown. To find out the optimal number of topic, we build many LSA, NMF, LDA models with different values of number of topics (k) and pick the one that gives the highest coherence score. Choosing a `k' that marks the end of a rapid growth of topic coherence usually offers meaningful and interpretable topics.", + "We use Gensim's coherencemodel to calculate topic coherence for topic models (LSA and LDA). For NMF, we use a topic coherence measure called TC-W2V. This measure relies on the use of a word embedding model constructed from the corpus. So in this step, we use the Gensim implementation of Word2Vec BIBREF21 to build a Word2Vec model based on the collection of tweets.", + "We achieve the highest coherence score = 0.4495 when the number of topics is 2 for LSA, for NMF the highest coherence value is 0.6433 for K = 4, and for LDA we also get number of topics is 4 with the highest coherence score which is 0.3871 (see Fig. FIGREF8 ).", + "For our dataset, we picked k = 2, 4, and 4 with the highest coherence value for LSA, NMF, and LDA correspondingly (Fig. FIGREF8 ). Table TABREF13 shows the topics and top-10 keywords of the corresponding topic. We get more informative and understandable topics using LDA model than LSA. LSA decomposed matrix is a highly dense matrix, so it is difficult to index individual dimension. LSA is unable to capture the multiple meanings of words. It offers lower accuracy than LDA.", + "In case of NMF, we observe same keywords are repeated in multiple topics. Keywords \"go\", \"day\" both are repeated in Topic 2, Topic 3, and Topic 4 (Table TABREF13 ). In Table TABREF13 keyword \"yoga\" has been found both in Topic 1 and Topic 4. We also notice that keyword \"eat\" is in Topic 2 and Topic 3 (Table TABREF13 ). If the same keywords being repeated in multiple topics, it is probably a sign that the `k' is large though we achieve the highest coherence score in NMF for k=4.", + "We use LDA model for our further analysis. Because LDA is good in identifying coherent topics where as NMF usually gives incoherent topics. However, in the average case NMF and LDA are similar but LDA is more consistent." + ], + [ + "After doing topic modeling using three different method LSA, NMF, and LDA, we use LDA for further analysis i.e. to observe the dominant topic, 2nd dominant topic and percentage of contribution of the topics in each tweet of training data. To observe the model behavior on new tweets those are not included in training set, we follow the same procedure to observe the dominant topic, 2nd dominant topic and percentage of contribution of the topics in each tweet on testing data. Table TABREF30 shows some tweets and corresponding dominant topic, 2nd dominant topic and percentage of contribution of the topics in each tweet." + ], + [ + "To calculate the accuracy of model in comparison with ground truth label, we selected top 500 tweets from train dataset (40k tweets). We extracted 500 new tweets (22 April, 2019) as a test dataset. We did manual annotation both for train and test data by choosing one topic among the 4 topics generated from LDA model (7th, 8th, 9th, and 10th columns of Table TABREF13 ) for each tweet based on the intent of the tweet. Consider the following two tweets:", + "Tweet 1: Learning some traditional yoga with my good friend.", + "Tweet 2: Why You Should #LiftWeights to Lose #BellyFat #Fitness #core #abs #diet #gym #bodybuilding #workout #yoga", + "The intention of Tweet 1 is yoga activity (i.e. learning yoga). Tweet 2 is more about weight lifting to reduce belly fat. This tweet is related to workout. When we do manual annotation, we assign Topic 2 in Tweet 1, and Topic 1 in Tweet 2. It's not wise to assign Topic 2 for both tweets based on the keyword \"yoga\". During annotation, we focus on functionality of tweets." + ], + [ + "We use LDAvis BIBREF22 , a web-based interactive visualization of topics estimated using LDA. Gensim's pyLDAVis is the most commonly used visualization tool to visualize the information contained in a topic model. In Fig. FIGREF21 , each bubble on the left-hand side plot represents a topic. The larger the bubble, the more prevalent is that topic. A good topic model has fairly big, non-overlapping bubbles scattered throughout the chart instead of being clustered in one quadrant. A model with too many topics, is typically have many overlaps, small sized bubbles clustered in one region of the chart. In right hand side, the words represent the salient keywords.", + "If we move the cursor over one of the bubbles (Fig. FIGREF21 ), the words and bars on the right-hand side have been updated and top-30 salient keywords that form the selected topic and their estimated term frequencies are shown.", + "We observe interesting hidden correlation in data. Fig. FIGREF24 has Topic 2 as selected topic. Topic 2 contains top-4 co-occurring keywords \"vegan\", \"yoga\", \"job\", \"every_woman\" having the highest term frequency. We can infer different things from the topic that \"women usually practice yoga more than men\", \"women teach yoga and take it as a job\", \"Yogi follow vegan diet\". We would say there are noticeable correlation in data i.e. `Yoga-Veganism', `Women-Yoga'." + ], + [ + "Each tweet is composed of multiple topics. But, typically only one of the topics is dominant. We extract the dominant and 2nd dominant topic for each tweet and show the weight of the topic (percentage of contribution in each tweet) and the corresponding keywords.", + "We plot the frequency of each topic's distribution on tweets in histogram. Fig. FIGREF25 shows the dominant topics' frequency and Fig. FIGREF25 shows the 2nd dominant topics' frequency on tweets. From Fig. FIGREF25 we observe that Topic 1 became either the dominant topic or the 2nd dominant topic for most of the tweets. 7th column of Table TABREF13 shows the corresponding top-10 keywords of Topic 1." + ], + [ + "To compare with ground truth, we gradually increased the size of dataset 100, 200, 300, 400, and 500 tweets from train data and test data (new tweets) and did manual annotation both for train/test data based on functionality of tweets (described in [subsec:3.5]Subsection 3.5).", + "For accuracy calculation, we consider the dominant topic only. We achieved 66% train accuracy and 51% test accuracy when the size of dataset is 500 (Fig. FIGREF28 ). We did baseline implementation with random inference by running multiple times with different seeds and took the average accuracy. For dataset 500, the accuracy converged towards 25% which is reasonable as we have 4 topics." + ], + [ + "In Table TABREF30 , we show some observations. For the tweets in 1st and 2nd row (Table TABREF30 ), we observed understandable topic. We also noticed misleading topic and unrelated topic for few tweets (3rd and 4th row of Table TABREF30 ).", + "In the 1st row of Table TABREF30 , we show a tweet from train data and we got Topic 2 as a dominant topic which has 61% of contribution in this tweet. Topic 1 is 2nd dominant topic and 18% contribution here.", + "2nd row of Table TABREF30 shows a tweet from test set. We found Topic 2 as a dominant topic with 33% of contribution and Topic 4 as 2nd dominant topic with 32% contribution in this tweet.", + "In the 3rd (Table TABREF30 ), we have a tweet from test data and we got Topic 2 as a dominant topic which has 43% of contribution in this tweet. Topic 3 is 2nd dominant with 23% contribution which is misleading topic. The model misinterprets the words `water in hand' and infers topic which has keywords \"swimming, swim, pool\". But the model should infer more reasonable topic (Topic 1 which has keywords \"diet, workout\") here.", + "We got Topic 2 as dominant topic for the tweet in 4th row (Table TABREF30 ) which is unrelated topic for this tweet and most relevant topic of this tweet (Topic 2) as 2nd dominant topic. We think during accuracy comparison with ground truth 2nd dominant topic might be considered.", + "In future, we will extract more tweets and train the model and observe the model behavior on test data. As we found misleading and unrelated topic in test cases, it is important to understand the reasons behind the predictions. We will incorporate Local Interpretable model-agnostic Explanation (LIME) BIBREF23 method for the explanation of model predictions. We will also do predictive causality analysis on tweets." + ], + [ + "It is challenging to analyze social media data for different application purpose. In this work, we explored Twitter health-related data, inferred topic using topic modeling (i.e. LSA, NMF, LDA), observed model behavior on new tweets, compared train/test accuracy with ground truth, employed different visualizations after information integration and discovered interesting correlation (Yoga-Veganism) in data. In future, we will incorporate Local Interpretable model-agnostic Explanation (LIME) method to understand model interpretability." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0707/instruction.md b/qasper-0707/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..99140e90a9d257b825abb162c513b5ee5de91315 --- /dev/null +++ b/qasper-0707/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Joint Learning of Sentence Embeddings for Relevance and Entailment + +Question: what were the baselines? \ No newline at end of file diff --git a/qasper-0708/instruction.md b/qasper-0708/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..186003f579761b7f50cce54d71f7e3eeb4800d23 --- /dev/null +++ b/qasper-0708/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Joint Learning of Sentence Embeddings for Relevance and Entailment + +Question: what is the state of the art for ranking mc test answers? \ No newline at end of file diff --git a/qasper-0721/instruction.md b/qasper-0721/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..8152bf62518a3653ab05824fa7504838d98adace --- /dev/null +++ b/qasper-0721/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset + +Question: Which six domains are covered in the dataset? \ No newline at end of file diff --git a/qasper-0730/instruction.md b/qasper-0730/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..098de6c149e55889b70c212ea75a7e95c758a08f --- /dev/null +++ b/qasper-0730/instruction.md @@ -0,0 +1,155 @@ +Name of Paper: e-SNLI-VE-2.0: Corrected Visual-Textual Entailment with Natural Language Explanations + +Question: What is the class with highest error rate in SNLI-VE? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "SNLI-VE-2.0", + "SNLI-VE-2.0 ::: Re-annotation details", + "SNLI-VE-2.0 ::: Re-evaluation of Visual-Textual Entailment", + "SNLI-VE-2.0 ::: Re-evaluation of Visual-Textual Entailment ::: Model.", + "SNLI-VE-2.0 ::: Re-evaluation of Visual-Textual Entailment ::: Results.", + "Visual-Textual Entailment with Natural Language Explanations", + "Visual-Textual Entailment with Natural Language Explanations ::: e-SNLI-VE-2.0", + "Visual-Textual Entailment with Natural Language Explanations ::: Collecting Explanations", + "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations", + "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Predict and Explain", + "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Predict and Explain ::: Model.", + "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Predict and Explain ::: Loss.", + "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Predict and Explain ::: Model selection.", + "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Predict and Explain ::: Results.", + "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Explain Then Predict", + "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Explain Then Predict ::: Model.", + "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Explain Then Predict ::: Model selection.", + "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Explain Then Predict ::: Results.", + "Visual-Textual Entailment with Natural Language Explanations ::: VTE Models with Natural Language Explanations ::: Qualitative Analysis of Generated Explanations", + "Conclusion", + "Conclusion ::: Acknowledgements.", + "Appendix ::: Statistics of e-SNLI-VE-2.0", + "Appendix ::: Details of the Mechanical Turk Task", + "Appendix ::: Ambiguous Examples from SNLI-VE" + ], + "paragraphs": [ + [ + "Inspired by textual entailment BIBREF0, Xie BIBREF1 introduced the visual-textual entailment (VTE) task, which considers semantic entailment between a premise image and a textual hypothesis. Semantic entailment consists in determining if the hypothesis can be concluded from the premise, and assigning to each pair of (premise image, textual hypothesis) a label among entailment, neutral, and contradiction. In Figure FIGREF3, the label for the first image-sentence pair is entailment, because the hypothesis states that \u201ca bunch of people display different flags\u201d, which can be clearly derived from the image. On the contrary, the second image-sentence pair is labelled as contradiction, because the hypothesis stating that \u201cpeople [are] running a marathon\u201d contradicts the image with static people.", + "Xie also propose the SNLI-VE dataset as the first dataset for VTE. SNLI-VE is built from the textual entailment SNLI dataset BIBREF0 by replacing textual premises with the Flickr30k images that they originally described BIBREF2. However, images contain more information than their descriptions, which may entail or contradict the textual hypotheses (see Figure FIGREF3). As a result, the neutral class in SNLI-VE has substantial labelling errors. Vu BIBREF3 estimated ${\\sim }31\\%$ errors in this class, and ${\\sim }1\\%$ for the contradiction and entailment classes.", + "Xie BIBREF1 introduced the VTE task under the name of \u201cvisual entailment\u201d, which could imply recognizing entailment between images only. This paper prefers to follow Suzuki BIBREF4 and call it \u201cvisual-textual entailment\u201d instead, as it involves reasoning on image-sentence pairs.", + "In this work, we first focus on decreasing the error in the neutral class by collecting new labels for the neutral pairs in the validation and test sets of SNLI-VE, using Amazon Mechanical Turk (MTurk). To ensure high quality annotations, we used a series of quality control measures, such as in-browser checks, inserting trusted examples, and collecting three annotations per instance. Secondly, we re-evaluate current image-text understanding systems, such as the bottom-up top-down attention network (BUTD) BIBREF5 on VTE using our corrected dataset, which we call SNLI-VE-2.0.", + "Thirdly, we introduce the e-SNLI-VE-2.0 corpus, which we form by appending human-written natural language explanations to SNLI-VE-2.0. These explanations were collected in e-SNLI BIBREF6 to support textual entailment for SNLI. For the same reasons as above, we re-annotate the explanations for the neutral pairs in the validation and test sets, while keeping the explanations from e-SNLI for all the rest. Finally, we extend a current VTE model with the capacity of learning from these explanations at training time and outputting an explanation for each predicted label at testing time." + ], + [ + "The goal of VTE is to determine if a textual hypothesis $H_{text}$ can be concluded, given the information in a premise image $P_{image}$ BIBREF1. There are three possible labels:", + "Entailment: if there is enough evidence in $P_{image}$ to conclude that $H_{text}$ is true.", + "Contradiction: if there is enough evidence in $P_{image}$ to conclude that $H_{text}$ is false.", + "Neutral: if neither of the earlier two are true.", + "The SNLI-VE dataset proposed by Xie BIBREF1 is the combination of Flickr30k, a popular image dataset for image captioning BIBREF2 and SNLI, an influential dataset for natural language inference BIBREF0. Textual premises from SNLI are replaced with images from Flickr30k, which is possible, as these premises were originally collected as captions of these images (see Figure FIGREF3).", + "However, in practice, a sensible proportion of labels are wrong due to the additional information contained in images. This mostly affects neutral pairs, since images may contain the necessary information to ground a hypothesis for which a simple premise caption was not sufficient. An example is shown in Figure FIGREF3. Vu BIBREF3 report that the label is wrong for ${\\sim }31\\%$ of neutral examples, based on a random subset of 171 neutral points from the test set. We also annotated 150 random neutral examples from the test set and found a similar percentage of 30.6% errors.", + "Our annotations are available at https://github.com/virginie-do/e-SNLI-VE/tree/master/annotations/gt_labels.csv" + ], + [ + "In this work, we only collect new labels for the neutral pairs in the validation and test sets of SNLI-VE. While the procedure of re-annotation is generic, we limit our re-annotation to these splits as a first step to verify the difference in performance that current models have when evaluated on the corrected test set as well as the effect of model selection on the corrected validation set. We leave for future work re-annotation of the training set, which would likely lead to training better VTE models. We also chose not to re-annotate entailment and contradiction classes, as their error rates are much lower ($<$1% as reported by Vu BIBREF3).", + "The main question that we want our dataset to answer is: \u201cWhat is the relationship between the image premise and the sentence hypothesis?\u201d. We provide workers with the definitions of entailment, neutral, and contradiction for image-sentence pairs and one example for each label. As shown in Figure FIGREF8, for each image-sentence pair, workers are required to (a) choose a label, (b) highlight words in the sentence that led to their decision, and (c) explain their decision in a comprehensive and concise manner, using at least half of the words that they highlighted. The collected explanations will be presented in more detail in Section SECREF20, as we focus here on the label correction. We point out that it is likely that requiring an explanation at the same time as requiring a label has a positive effect on the correctness of the label, since having to justify in writing the picked label may make workers pay an increased attention. Moreover, we implemented additional quality control measures for crowdsourced annotations, such as (a) collecting three annotations for every input, (b) injecting trusted annotations into the task for verification BIBREF7, and (c) restricting to workers with at least 90% previous approval rate.", + "First, we noticed that some instances in SNLI-VE are ambiguous. We show some examples in Figure FIGREF3 and in Appendix SECREF43. In order to have a better sense of this ambiguity, three authors of this paper independently annotated 100 random examples. All three authors agreed on 54% of the examples, exactly two authors agreed on 45%, and there was only one example on which all three authors disagreed. We identified the following three major sources of ambiguity:", + "mapping an emotion in the hypothesis to a facial expression in the image premise, e.g., \u201cpeople enjoy talking\u201d, \u201cangry people\u201d, \u201csad woman\u201d. Even when the face is seen, it may be subjective to infer an emotion from a static image (see Figure FIGREF44 in Appendix SECREF43).", + "personal taste, e.g., \u201cthe sign is ugly\u201d.", + "lack of consensus on terms such as \u201cmany people\u201d or \u201ccrowded\u201d.", + "To account for the ambiguity that the neutral labels seem to present, we considered that an image-sentence pair is too ambiguous and not suitable for a well-defined visual-textual entailment task when three different labels were assigned by the three workers. Hence, we removed these examples from the validation (5.2%) and test (5.5%) sets.", + "To ensure that our workers are correctly performing the task, we randomly inserted trusted pairs, i.e., pairs among the 54% on which all three authors agreed on the label. For each set of 10 pairs presented to a worker, one trusted pair was introduced at a random location, so that the worker, while being told that there is such a test pair, cannot figure out which one it is. Via an in-browser check, we only allow workers to submit their answers for each set of 10 instances only if the trusted pair was correctly labelled. Other in-browser checks were done for the collection of explanations, as we will describe in Section SECREF20. More details about the participants and design of the Mechanical Turk task can be found in Appendix SECREF41.", + "After collecting new labels for the neutral instances in the validation and testing sets, we randomly select and annotate 150 instances from the validation set that were neutral in SNLI-VE. Based on this sample, the error rate went down from 31% to 12% in SNLI-VE-2.0. Looking at the 18 instances where we disagreed with the label assigned by MTurk workers, we noticed that 12 were due to ambiguity in the examples, and 6 were due to workers' errors. Further investigation into potentially eliminating ambiguous instances would likely be beneficial. However, we leave it as future work, and we proceed in this work with using our corrected labels, since our error rate is significantly lower than that of the original SNLI-VE.", + "Finally, we note that only about 62% of the originally neutral pairs remain neutral, while 21% become contradiction and 17% entailment pairs. Therefore, we are now facing an imbalance between the neutral, entailment, and contradiction instances in the validation and testing sets of SNLI-VE-2.0. The neutral class becomes underrepresented and the label distributions in the corrected validation and testing sets both become E / N / C: 39% / 20% / 41%. To account for this, we compute the balanced accuracy, i.e., the average of the three accuracies on each class." + ], + [ + "Since we decreased the error rate of labels in the validation and test set, we are interested in the performance of a VTE model when using the corrected sets." + ], + [ + "To tackle SNLI-VE, Xie BIBREF1 used EVE (for \u201cExplainable Visual Entailment\u201d), a modified version of the BUTD architecture, the winner of the Visual Question Answering (VQA) challenge in 2017 BIBREF5. Since the EVE implementation is not available at the time of this work, we used the original BUTD architecture, with the same hyperparameters as reported in BIBREF1.", + "BUTD contains an image processing module and a text processing module. The image processing module encodes each image region proposed by FasterRCNN BIBREF8 into a feature vector using a bottom-up attention mechanism. In the text processing module, the text hypothesis is encoded into a fixed-length vector, which is the last output of a recurrent neural network with 512-GRU units BIBREF9. To input each token into the recurrent network, we use the pretrained GloVe vectors BIBREF10. Finally, a top-down attention mechanism is used between the hypothesis vector and each of the image region vectors to obtain an attention weight for each region. The weighted sum of these image region vectors is then fused with the text hypothesis vector. The multimodal fusion is fed to a multilayer percetron (MLP) with tanh activations and a final softmax layer to classify the image-sentence relation as entailment, contradiction, or neutral.", + "Using the implementation from https://github.com/claudiogreco/coling18-gte.", + "We use the original training set from SNLI-VE. To see the impact of correcting the validation and test sets, we do the following three experiments:", + "model selection as well as testing are done on the original uncorrected SNLI-VE.", + "model selection is done on the uncorrected SNLI-VE validation set, while testing is done on the corrected SNLI-VE-2.0 test set.", + "model selection as well as testing are done on the corrected SNLI-VE-2.0.", + "Models are trained with cross-entropy loss optimized by the Adam optimizer BIBREF11 with batch size 64. The maximum number of training epochs is set to 100, with early stopping when no improvement is observed on validation accuracy for 3 epochs. The final model checkpoint selected for testing is the one with the highest validation accuracy." + ], + [ + "The results of the three experiments enumerated above are reported in Table TABREF18. Surprisingly, we obtained an accuracy of 73.02% on SNLI-VE using BUTD, which is better than the 71.16% reported by Xie BIBREF1 for the EVE system which meant to be an improvement over BUTD. It is also better than their reproduction of BUTD, which gave 68.90%.", + "The same BUTD model that achieves 73.02% on the uncorrected SNLI-VE test set, achieves 73.18% balanced accuracy when tested on the corrected test set from SNLI-VE-2.0. Hence, for this model, we do not notice a significant difference in performance. This could be due to randomness. Finally, when we run the training loop again, this time doing the model selection on the corrected validation set from SNLI-VE-2.0, we obtain a slightly worse performance of 72.52%, although the difference is not clearly significant.", + "Finally, we recall that the training set has not been re-annotated, and hence approximately 31% image-sentence pairs are wrongly labelled as neutral, which likely affects the performance of the model." + ], + [ + "In this work, we also introduce e-SNLI-VE-2.0, a dataset combining SNLI-VE-2.0 with human-written explanations from e-SNLI BIBREF6, which were originally collected to support textual entailment. We replace the explanations for the neutral pairs in the validation and test sets with new ones collected at the same time as the new labels. We extend a current VTE model with an explanation module able to learn from these explanations at training time and generate an explanation for each predicted label at testing time." + ], + [ + "e-SNLI BIBREF6 is an extension of the SNLI corpus with human-annotated natural language explanations for the ground-truth labels. The authors use the explanations to train models to also generate natural language justifications for their predictions. They collected one explanation for each instance in the training set of SNLI and three explanations for each instance in the validation and testing sets.", + "We randomly selected 100 image-sentence pairs in the validation set of SNLI-VE and their corresponding explanations in e-SNLI and examined how relevant these explanations are for the VTE task. More precisely, we say that an explanation is relevant if it brings information that justifies the relationship between the image and the sentence. We restricted the count to correctly labelled inputs and found that 57% explanations were relevant. For example, the explanation for entailment in Figure FIGREF21 (\u201cCooking in his apartment is cooking\u201d) was counted as irrelevant in our statistics, because it would not be the best explanation for an image-sentence pair, even though it is coherent with the textual pair. We investigate whether these explanations improve a VTE model when enhanced with a component that can process explanations at train time and output them at test time.", + "To form e-SNLI-VE-2.0, we append to SNLI-VE-2.0 the explanations from e-SNLI for all except the neutral pairs in the validation and test sets of SNLI-VE, which we replace with newly crowdsourced explanations collected at the same time as the labels for these splits (see Figure FIGREF21). Statistics of e-SNLI-VE-2.0 are shown in Appendix SECREF39, Table TABREF40." + ], + [ + "As mentioned before, in order to submit the annotation of an image-sentence pair, three steps must be completed: workers must choose a label, highlight words in the hypothesis, and use at least half of the highlighted words to write an explanation for their decision. The last two steps thus follow the quality control of crowd-sourced explanations introduced by Camburu BIBREF6. We also ensured that workers do not simply use a copy of the given hypothesis as explanation. We ensured all the above via in-browser checks before workers' submission. An example of collected explanations is given in Figure FIGREF21.", + "To check the success of our crowdsourcing, we manually assessed the relevance of explanations among a random subset of 100 examples. A marking scale between 0 and 1 was used, assigning a score of $k$/$n$ when $k$ required attributes were given in an explanation out of $n$. We report an 83.5% relevance of explanations from workers. We note that, since our explanations are VTE-specific, they were phrased differently from the ones in e-SNLI, with more specific mentions to the images (e.g., \u201cThere is no labcoat in the picture, just a man wearing a blue shirt.\u201d, \u201cThere are no apples or oranges shown in the picture, only bananas.\u201d). Therefore, it would likely be beneficial to collect new explanations for all SNLI-VE-2.0 (not only for the neutral pairs in the validation and test sets) such that models can learn to output convincing explanations for the task at hand. However, we leave this as future work, and we show in this work the results that one obtains when using the explanations from e-SNLI-VE-2.0." + ], + [ + "This section presents two VTE models that generate natural language explanations for their own decisions. We name them PaE-BUTD-VE and EtP-BUTD-VE, where PaE (resp. EtP) is for PredictAndExplain (resp. ExplainThenPredict), two models with similar principles introduced by Camburu BIBREF6. The first system learns to generate an explanation conditioned on the image premise, textual hypothesis, and predicted label. In contrast, the second system learns to first generate an explanation conditioned on the image premise and textual hypothesis, and subsequently makes a prediction solely based on the explanation." + ], + [ + "PaE-BUTD-VE is a system for solving VTE and generating natural language explanations for the predicted labels. The explanations are conditioned on the image premise, the text hypothesis, and the predicted label (ground-truth label at train time), as shown in Figure FIGREF24." + ], + [ + "As described in Section SECREF12, in the BUTD model, the hypothesis vector and the image vector were fused in a fixed-size feature vector f. The vector f was then given as input to an MLP which outputs a probability distribution over the three labels. In PaE-BUTD-VE, in addition to the classification layer, we add a 512-LSTM BIBREF12 decoder to generate an explanation. The decoder takes the feature vector f as initial state. Following Camburu BIBREF6, we prepend the label as a token at the beginning of the explanation to condition the explanation on the label. The ground truth label is provided at training time, whereas the predicted label is given at test time.", + "At test time, we use beam search with a beam width of 3 to decode explanations. For memory and time reduction, we replaced words that appeared less than 15 times among explanations with \u201c#UNK#\u201d. This strategy reduces the output vocabulary size to approximately 8.6k words." + ], + [ + "The training loss is a weighted combination of the classification loss and the explanation loss, both computed using softmax cross entropy: $\\mathcal {L} = \\alpha \\mathcal {L}_{label} + (1-\\alpha ) \\mathcal {L}_{explanation} \\; \\textrm {;} \\; \\alpha \\in [0,1]$." + ], + [ + "In this experiment, we are first interested in examining if a neural network can generate explanations at no cost for label accuracy. Therefore, only balanced accuracy on label is used for the model selection criterion. However, future work can investigate other selection criteria involving a combination between the label and explanation performances. We performed hyperparameter search on $\\alpha $, considering values between 0.2 and 0.8 with a step of 0.2. We found $\\alpha =0.4$ to produce the best validation balanced accuracy of 72.81%, while BUTD trained without explanations yielded a similar 72.58% validation balanced accuracy." + ], + [ + "As summarised in Table TABREF30, we obtain a test balanced accuracy for PaE-BUTD-VE of 73%, while the same model trained without explanations obtains 72.52%. This is encouraging, since it shows that one can obtain additional natural language explanations without sacrificing performance (and eventually even improving the label performance, however, future work is needed to conclude whether the difference $0.48\\%$ improvement in performance is statistically significant).", + "Camburu BIBREF6 mentioned that the BLEU score was not an appropriate measure for the quality of explanations and suggested human evaluation instead. We therefore manually scored the relevance of 100 explanations that were generated when the model predicted correct labels. We found that only 20% of explanations were relevant. We highlight that the relevance of explanations is in terms of whether the explanation reflects ground-truth reasons supporting the correct label. This is not to be confused with whether an explanation is correctly illustrating the inner working of the model, which is left as future work. It is also important to note that on a similar experimental setting, Camburu report as low as 34.68% correct explanations, training with explanations that were actually collected for their task. Lastly, the model selection criterion at validation time was the prediction balanced accuracy, which may contribute to the low quality of explanations. While we show that adding an explanation module does not harm prediction performance, more work is necessary to get models that output trustable explanations." + ], + [ + "When assigning a label, an explanation is naturally part of the decision-making process. This motivates the design of a system that explains itself before deciding on a label, called EtP-BUTD-VE. For this system, a first neural network is trained to generate an explanation given an image-sentence input. Separately, a second neural network, called ExplToLabel-VE, is trained to predict a label from an explanation (see Figure FIGREF32)." + ], + [ + "For the first network, we set $\\alpha =0$ in the training loss of the PaE-BUTD-VE model to obtain a system that only learns to generate an explanation from the image-sentence input, without label prediction. Hence, in this setting, no label is prepended before the explanation.", + "For the ExplToLabel-VE model, we use a 512-LSTM followed by an MLP with three 512-layers and ReLU activation, and softmax activation to classify the explanation between entailment, contradiction, and neutral." + ], + [ + "For ExplToLabel-VE, the best model is selected on balanced accuracy at validation time. For EtP-BUTD-VE, perplexity is used to select the best model parameters at validation time. It is computed between the explanations produced by the LSTM and ground truth explanations from the validation set." + ], + [ + "When we train ExplToLabel-VE on e-SNLI-VE-2.0, we obtain a balanced accuracy of 90.55% on the test set.", + "As reported in Table TABREF30, the overall PaE-BUTD-VE system achieves 69.40% balanced accuracy on the test set of e-SNLI-VE-2.0, which is a 3% decrease from the non-explanatory BUTD counterpart (72.52%). However, by setting $\\alpha $ to zero and selecting the model that gives the best perplexity per word at validation, the quality of explanation significantly increased, with 35% relevance, based on manual evaluation. Thus, in our model, generating better explanations involves a small sacrifice in label prediction accuracy, implying a trade-off between explanation generation and accuracy.", + "We note that there is room for improvement in our explanation generation method. For example, one can implement an attention mechanism similar to Xu BIBREF13, so that each generated word relates to a relevant part of the multimodal feature representation." + ], + [ + "We complement our quantitative results with a qualitative analysis of the explanations generated by our enhanced VTE systems. In Figures FIGREF36 and FIGREF37, we present examples of the predicted labels and generated explanations.", + "Figure FIGREF36 shows an example where the EtP-BUTD-VE model produces both a correct label and a relevant explanation. The label is contradiction, because in the image, the students are playing with a soccer ball and not a basketball, thus contradicting the text hypothesis. Given the composition of the generated sentence (\u201cStudents cannot be playing soccer and baseball at the same time.\u201d), ExplToLabel-VE was able to detect a contradiction in the image-sentence input. In comparison, the explanation from e-SNLI-VE-2.0 is not correct, even if it was valid for e-SNLI when the text premise was given. This emphasizes the difficulty that we are facing with generating proper explanations when training on a noisy dataset.", + "Even when the generated explanations are irrelevant, we noticed that they are on-topic and that most of the time the mistakes come from repetitions of certain sub-phrases. For example, in Figure FIGREF37, PaE-BUTD-VE predicts the label neutral, which is correct, but the explanation contains an erroneous repetition of the n-gram \u201care in a car\u201d. However, it appears that the system learns to generate a sentence in the form \u201cJust because ...doesn't mean ...\u201d, which is frequently found for the justification of neutral pairs in the training set. The explanation generated by EtP-BUTD-VE adopts the same structure, and the ExplToLabel-VE component correctly classifies the instance as neutral. However, even if the explanation is semantically correct, it is not relevant for the input and fails to explain the classification." + ], + [ + "In this paper, we first presented SNLI-VE-2.0, which corrects the neutral instances in the validation and test sets of SNLI-VE. Secondly, we re-evaluated an existing model on the corrected sets in order to update the estimate of its performance on this task. Thirdly, we introduced e-SNLI-VE-2.0, a dataset which extends SNLI-VE-2.0 with natural language explanations. Finally, we trained two types of models that learn from these explanations at training time, and output such explanations at test time, as a stepping stone in explainable artificial intelligence. Our work is a jumping-off point for both the identification and correction of SNLI-VE, as well as in the extension to explainable VTE. We hope that the community will build on our findings to create more robust as well as explainable multimodal systems." + ], + [ + "This work was supported by the Oxford Internet Institute, a JP Morgan PhD Fellowship 2019-2020, an Oxford-DeepMind Graduate Scholarship, the Alan Turing Institute under the EPSRC grant EP/N510129/1, and the AXA Research Fund, as well as DFG-EXC-Nummer 2064/1-Projektnummer 390727645 and the ERC under the Horizon 2020 program (grant agreement No. 853489)." + ], + [ + "e-SNLI-VE-2.0 is the combination of SNLI-VE-2.0 with explanations from either e-SNLI or our crowdsourced annotations where applicable. The statistics of e-SNLI-VE-2.0 are shown in Table TABREF40.", + "Including text hypotheses and explanations." + ], + [ + "We used Amazon Mechanical Turk (MTurk) to collect new labels and explanations for SNLI-VE. 2,060 workers participated in the annotation effort, with an average of 1.98 assignments per worker and a standard deviation of 5.54. We required the workers to have a previous approval rate above 90%. No restriction was put on the workers' location.", + "Each assignment consisted of a set of 10 image-sentence pairs. For each pair, the participant was asked to (a) choose a label, (b) highlight words in the sentence that led to their decision, and (c) explain their decision in a comprehensive and concise manner, using a subset of the words that they highlighted. The instructions are shown in Figure FIGREF42. Workers were also guided with three annotated examples, one for each label.", + "For each assignment of 10 questions, one trusted annotation with gold standard label was inserted at a random position, as a measure to control the quality of label annotation. Each assignment was completed by three different workers. An example of question is shown in Figure FIGREF8 in the core paper." + ], + [ + "Some examples in SNLI-VE were ambiguous and could find correct justifications for incompatible labels, as shown in Figures FIGREF44, FIGREF45, and FIGREF46." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0737/instruction.md b/qasper-0737/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..3cf675bc77916d82b350b15dba211125497543fc --- /dev/null +++ b/qasper-0737/instruction.md @@ -0,0 +1,72 @@ +Name of Paper: An Analysis of Word2Vec for the Italian Language + +Question: What dataset is used for training Word2Vec in Italian language? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Word2Vec", + "Word2Vec ::: Sampling rate", + "Word2Vec ::: Negative sampling", + "Implementation details", + "Results", + "Results ::: Analysis of the various models", + "Results ::: Comparison with other models", + "Conclusion" + ], + "paragraphs": [ + [ + "In order to make human language comprehensible to a computer, it is obviously essential to provide some word encoding. The simplest approach is the one-hot encoding, where each word is represented by a sparse vector with dimension equal to the vocabulary size. In addition to the storage need, the main problem of this representation is that any concept of word similarity is completely ignored (each vector is orthogonal and equidistant from each other). On the contrary, the understanding of natural language cannot be separated from the semantic knowledge of words, which conditions a different closeness between them. Indeed, the semantic representation of words is the basic problem of Natural Language Processing (NLP). Therefore, there is a necessary need to code words in a space that is linked to their meaning, in order to facilitate a machine in potential task of \u201cunderstanding\" it. In particular, starting from the seminal work BIBREF0, words are usually represented as dense distributed vectors that preserve their uniqueness but, at the same time, are able to encode the similarities.", + "These word representations are called Word Embeddings since the words (points in a space of vocabulary size) are mapped in an embedding space of lower dimension. Supported by the distributional hypothesis BIBREF1 BIBREF2, which states that a word can be semantically characterized based on its context (i.e. the words that surround it in the sentence), in recent years many word embedding representations have been proposed (a fairly complete and updated review can be found in BIBREF3 and BIBREF4). These methods can be roughly categorized into two main classes: prediction-based models and count-based models. The former is generally linked to work on Neural Network Language Models (NNLM) and use a training algorithm that predicts the word given its local context, the latter leverage word-context statistics and co-occurrence counts in an entire corpus. The main prediction-based and count-based models are respectively Word2Vec BIBREF5 (W2V) and GloVe BIBREF6.", + "Despite the widespread use of these concepts BIBREF7 BIBREF8, few contributions exist regarding the development of a W2V that is not in English. In particular, no detailed analysis on an Italian W2V seems to be present in the literature, except for BIBREF9 and BIBREF10. However, both seem to leave out some elements of fundamental interest in the learning of the neural network, in particular relating to the number of epochs performed during learning, reducing the importance that it may have on the final result. In BIBREF9, this for example leads to the simplistic conclusion that (being able to organize with more freedom in space) the more space is given to the vectors, the better the results may be. However, the problem in complex structures is that large embedding spaces can make training too difficult.", + "In this work, by setting the size of the embedding to a commonly used average value, various parameters are analysed as the number of learning epochs changes, depending on the window sizes and the negatively backpropagated samples." + ], + [ + "The W2V structure consists of a simple two-level neural network (Figure FIGREF1) with one-hot vectors representing words at the input. It can be trained in two different modes, algorithmically similar, but different in concept: Continuous Bag-of-Words (CBOW) model and Skip-Gram model. While CBOW tries to predict the target words from the context, Skip-Gram instead aims to determine the context for a given target word. The two different approaches therefore modify only the way in which the inputs and outputs are to be managed, but in any case, the network does not change, and the training always takes place between single pairs of words (placed as one-hot in input and output).", + "The text is in fact divided into sentences, and for each word of a given sentence a window of words is taken from the right and from the left to define the context. The central word is coupled with each of the words forming the set of pairs for training. Depending on the fact that the central word represents the output or the input in training pairs, the CBOW and Skip-gram models are obtained respectively.", + "Regardless of whether W2V is trained to predict the context or the target word, it is used as a word embedding in a substantially different manner from the one for which it has been trained. In particular, the second matrix is totally discarded during use, since the only thing relevant to the representation is the space of the vectors generated in the intermediate level (embedding space)." + ], + [ + "The common words (such as \u201cthe\", \u201cof\", etc.) carry very little information on the target word with which they are coupled, and through backpropagation they tend to have extremely small representative vectors in the embedding space. To solve both these problems the W2V algorithm implements a particular \u201csubsampling\" BIBREF11, which acts by eliminating some words from certain sentences. Note that the elimination of a word directly from the text means that it no longer appears in the context of any of the words of the sentence and, at the same time, a number of pairs equal to (at most) twice the size of the window relating to the deleted word will also disappear from the training set.", + "In practice, each word is associated with a sort of \u201ckeeping probability\" and, when you meet that word, if this value is greater than a randomly generated value then the word will not be discarded from the text. The W2V implementation assigns this \u201cprobability\" to the generic word $w_i$ through the formula:", + "where $f(w_i)$ is the relative frequency of the word $w_i$ (namely $count(w_i)/total$), while $s$ is a sample value, typically set between $10^{-3}$ and $10^{-5}$." + ], + [ + "Working with one-hot pairs of words means that the size of the network must be the same at input and output, and must be equal to the size of the vocabulary. So, although very simple, the network has a considerable number of parameters to train, which lead to an excessive computational cost if we are supposed to backpropagate all the elements of the one-hot vector in output.", + "The \u201cnegative sampling\" technique BIBREF11 tries to solve this problem by modifying only a small percentage of the net weights every time. In practice, for each pair of words in the training set, the loss function is calculated only for the value 1 and for a few values 0 of the one-hot vector of the desired output. The computational cost is therefore reduced by choosing to backpropagate only $K$ words \u201cnegative\" and one positive, instead of the entire vocabulary. Typical values for negative sampling (the number of negative samples that will be backpropagated and to which therefore the only positive value will always be added), range from 2 to 20, depending on the size of the dataset.", + "The probability of selecting a negative word to backpropagate depends on its frequency, in particular through the formula:", + "Negative samples are then selected by choosing a sort of \u201cunigram distribution\", so that the most frequent words are also the most often backpropated ones." + ], + [ + "The dataset needed to train the W2V was obtained using the information extracted from a dump of the Italian Wikipedia (dated 2019.04.01), from the main categories of Italian Google News (WORLD, NATION, BUSINESS, TECHNOLOGY, ENTERTAINMENT, SPORTS, SCIENCE, HEALTH) and from some anonymized chats between users and a customer care chatbot (Laila). The dataset (composed of 2.6 GB of raw text) includes $421\\,829\\,960$ words divided into $17\\,305\\,401$ sentences.", + "The text was previously preprocessed by removing the words whose absolute frequency was less than 5 and eliminating all special characters. Since it is impossible to represent every imaginable numerical value, but not wanting to eliminate the concept of \u201cnumerical representation\" linked to certain words, it was also decided to replace every number present in the text with the particular $\\langle NUM \\rangle $ token; which probably also assumes a better representation in the embedding space (not separating into the various possible values). All the words were then transformed to lowercase (to avoid a double presence) finally producing a vocabulary of $618\\,224$ words.", + "Note that among the special characters are also included punctuation marks, which therefore do not appear within the vocabulary. However, some of them (`.', `?' and `!') are later removed, as they are used to separate the sentences.", + "The Python implementation provided by Gensim was used for training the various embeddings all with size 300 and sampling parameter ($s$ in Equation DISPLAY_FORM3) set at $0.001$." + ], + [ + "To analyse the results we chose to use the test provided by BIBREF10, which consists of $19\\,791$ analogies divided into 19 different categories: 6 related to the \u201csemantic\" macro-area (8915 analogies) and 13 to the \u201csyntactic\" one (10876 analogies). All the analogies are composed by two pairs of words that share a relation, schematized with the equation: $a:a^{*}=b:b^{*}$ (e.g. \u201cman : woman = king : queen\"); where $b^{*}$ is the word to be guessed (\u201cqueen\"), $b$ is the word coupled to it (\u201cking\"), $a$ is the word for the components to be eliminated (\u201cman\"), and $a^{*}$ is the word for the components to be added (\u201cwoman\").", + "The determination of the correct response was obtained both through the classical additive cosine distance (3COSADD) BIBREF5:", + "and through the multiplicative cosine distance (3COSMUL) BIBREF12:", + "where $\\epsilon =10^{-6}$ and $\\cos (x, y) = \\frac{x \\cdot y}{\\left\\Vert x\\right\\Vert \\left\\Vert y\\right\\Vert }$. The extremely low value chosen for the $\\epsilon $ is due to the desire to minimize as much as possible its impact on performance, as during the various testing phases we noticed a strange bound that is still being investigated. As usual, moreover, the representative vectors of the embedding space are previously normalized for the execution of the various tests." + ], + [ + "We first analysed 6 different implementations of the Skip-gram model each one trained for 20 epochs. Table TABREF10 shows the accuracy values (only on possible analogies) at the 20th epoch for the six models both using 3COSADD and 3COSMUL. It is interesting to note that the 3COSADD total metric, respect to 3COSMUL, seems to have slightly better results in the two extreme cases of limited learning (W5N5 and W10N20) and under the semantic profile. However, we should keep in mind that the semantic profile is the one best captured by the network in both cases, which is probably due to the nature of the database (mainly composed of articles and news that principally use an impersonal language). In any case, the improvements that are obtained under the syntactic profile lead to the 3COSMUL metric obtaining better overall results.", + "Figure FIGREF11 shows the trends of the total accuracy at different epochs for the various models using 3COSMUL (the trend obtained with 3COSADD is very similar). Here we can see how the use of high negative sampling can worsen performance, even causing the network to oscillate (W5N20) in order to better adapt to all the data. The choice of the negative sampling to be used should therefore be strongly linked to the choice of the window size as well as to the number of training epochs.", + "Continuing the training of the two worst models up to the 50th epoch, it is observed (Table TABREF12) that they are still able to reach the performances of the other models. The W10N20 model at the 50th epoch even proves to be better than all the other previous models, becoming the reference model for subsequent comparisons. As the various epochs change (Figure FIGREF13.a) it appears to have the same oscillatory pattern observed previously, albeit with only one oscillation given the greater window size. This model is available at: https://mlunicampania.gitlab.io/italian-word2vec/.", + "Various tests were also conducted on CBOW models, which however proved to be in general significantly lower than Skip-gram models. Figure FIGREF13.b shows, for example, the accuracy trend for a CBOW model with a window equal to 10 and negative sampling equal to 20, which on 50 epochs reaches only $37.20\\%$ of total accuracy (with 3COSMUL metric)." + ], + [ + "Finally, a comparison was made between the Skip-gram model W10N20 obtained at the 50th epoch and the other two W2V in Italian present in the literature (BIBREF9 and BIBREF10). The first test (Table TABREF15) was performed considering all the analogies present, and therefore evaluating as an error any analogy that was not executable (as it related to one or more words absent from the vocabulary).", + "As it can be seen, regardless of the metric used, our model has significantly better results than the other two models, both overall and within the two macro-areas. Furthermore, the other two models seem to be more subject to the metric used, perhaps due to a stabilization not yet reached for the few training epochs.", + "For a complete comparison, both models were also tested considering only the subset of the analogies in common with our model (i.e. eliminating from the test all those analogies that were not executable by one or the other model). Tables TABREF16 and TABREF17 again highlight the marked increase in performance of our model compared to both." + ], + [ + "In this work we have analysed the Word2Vec model for Italian Language obtaining a substantial increase in performance respect to other two models in the literature (and despite the fixed size of the embedding). These results, in addition to the number of learning epochs, are probably also due to the different phase of data pre-processing, very carefully excuted in performing a complete cleaning of the text and above all in substituting the numerical values with a single particular token. We have observed that the number of epochs is an important parameter and its increase leads to results that rank our two worst models almost equal, or even better than others.", + "Changing the number of epochs, in some configurations, creates an oscillatory trend, which seems to be linked to a particular interaction between the window size and the negative sampling value. In the future, thanks to the collaboration in the Laila project, we intend to expand the dataset by adding more user chats. The objective will be to verify if the use of a less formal language can improves accuracy in the syntactic macro-area." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0739/instruction.md b/qasper-0739/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..88db23180cfcc651281724d06f51d63b103823d7 --- /dev/null +++ b/qasper-0739/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Improving Character-based Decoding Using Target-Side Morphological Information for Neural Machine Translation + +Question: What type of morphological information is contained in the "morphology table"? \ No newline at end of file diff --git a/qasper-0753/instruction.md b/qasper-0753/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..4eedf599c09048d2c1747e5b1c8ef7a94f7675f9 --- /dev/null +++ b/qasper-0753/instruction.md @@ -0,0 +1,68 @@ +Name of Paper: Domain Adaptation of Recurrent Neural Networks for Natural Language Understanding + +Question: What tasks are they experimenting with in this paper? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Model", + "Data", + "Experiments", + "Training and Model Configuration Details", + "Multi-task Model Experiments", + "Open Vocabulary Model Experiments", + "Conclusions" + ], + "paragraphs": [ + [ + "Slot filling models are a useful method for simple natural language understanding tasks, where information can be extracted from a sentence and used to perform some structured action. For example, dates, departure cities and destinations represent slots to fill in a flight booking task. This information is extracted from natural language queries leveraging typical context associated with each slot type. Researchers have been exploring data-driven approaches to learning models for automatic identification of slot information since the 90's, and significant advances have been made BIBREF0 . Our paper builds on recent work on slot-filling using recurrent neural networks (RNNs) with a focus on the problem of training from minimal annotated data, taking an approach of sharing data from multiple tasks to reduce the amount of data for developing a new task.", + "As candidate tasks, we consider the actions that a user might perform via apps on their phone. Typically, a separate slot-filling model would be trained for each app. For example, one model understands queries about classified ads for cars BIBREF1 and another model handles queries about the weather BIBREF2 . As the number of apps increases, this approach becomes impractical due to the burden of collecting and labeling the training data for each model. In addition, using independent models for each task has high storage costs for mobile devices.", + "Alternatively, a single model can be learned to handle all of the apps. This type of approach is known as multi-task learning and can lead to improved performance on all of the tasks due to information sharing between the different apps BIBREF3 . Multi-task learning in combination with neural networks has been shown to be effective for natural language processing tasks BIBREF4 . When using RNNs for slot filling, almost all of the model parameters can be shared between tasks. In our study, only the relatively small output layer, which consists of slot embeddings, is individual to each app. More sharing means that less training data per app can be used and there will still be enough data to effectively train the network. The multi-task approach has lower data requirements, which leads to a large cost savings and makes this approach scalable to large numbers of applications.", + "The shared representation that we build on leverages recent work on slot filling models that use neural network based approaches. Early neural network based papers propose feedforward BIBREF5 or RNN architectures BIBREF6 , BIBREF7 . The focus shifted to RNN's with long-short term memory cells (LSTMs) BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 after LSTMs were shown to be effective for other tasks BIBREF12 . The most recent papers use variations on LSTM sequence models, including encoder-decoder, external memory, or attention architectures BIBREF13 , BIBREF14 , BIBREF15 . The particular variant that we build on is a bidirectional LSTM, similar to BIBREF16 , BIBREF11 .", + "One highly desirable property of a good slot filling model is to generalize to previously unseen slot values. For instance, we should not expect that the model will see the names of all the cities during training time, especially when only a small amount of training data is used. We address the generalizability issue by incorporating the open vocabulary embeddings from Ling et al. into our model BIBREF17 . These embeddings work by using a character RNN to process a word one letter at a time. This way the model can learn to share parameters between different words that use the same morphemes. For example BBQ restaurants frequently use words like \u201csmokehouse\u201d, \u201csteakhouse\u201d, and \u201croadhouse\u201d in their names and \u201cBayside\u201d,\u201cBayview\u201d, and \u201cBaywood\u201d are all streets in San Francisco. Recognizing these patterns would be helpful in detecting a restaurant or street name slot, respectively.", + "The two main contributions of this work are the multi-task model and the use of the open vocabulary character-based embeddings, which together allow for scalable slot filling models. Our work on multi-task learning in slot filling differs from its previous use in BIBREF18 in that we allow for soft sharing between tasks instead of explicitly matching slots to each other across different tasks. A limitation of explicit slot matching is that two slots that appear to have the same underlying type, such as location-based slots, may actually use the slot information in different ways depending on the overall intent of the task. In our model, the sharing between tasks is done implicitly by the neural network. Our approach to handling words unseen in training data is different from the delexicalization proposed in BIBREF19 in that we do not require the vocabulary items associated with slots and values to be prespecified. It is complementary to work on extending domain coverage BIBREF20 , BIBREF21 .", + "The proposed model is described in more detail in Section \"Model\" . The approach is assessed on a new data collection based on four apps, described in Section \"Data\" . The experiments described in Section \"Training and Model Configuration Details\" investigate how much data is necessary for the $n$ -th app using a multi-task model that leverages the data from the previous $n-1$ apps, with results compared against the single-task model that only utilizes the data from the $n$ -th app. We conclude in Section \"Conclusions\" with a summary of the key findings and discussion of opportunities for future work." + ], + [ + "Our model has a word embedding layer, followed by a bi-directional LSTM (bi-LSTM), and a softmax output layer. The bi-LSTM allows the model to use information from both the right and left contexts of each word when making predictions. We choose this architecture because similar models have been used in prior work on slot filling and have achieved good results BIBREF16 , BIBREF11 . The LSTM gates are used as defined by Sak et al. including the use of the linear projection layer on the output of the LSTM BIBREF22 . The purpose of the projection layer is to produce a model with fewer parameters without reducing the number of LSTM memory cells. For the multi-task model, the word embeddings and the bi-LSTM parameters are shared across tasks but each task has its own softmax layer. This means that if the multi-task model has half a million parameters, only a couple thousand of them are unique to each task and the other 99.5% are shared between all of the tasks.", + "The slot labels are encoded in BIO format BIBREF23 indicating if a word is the beginning, inside or outside any particular slot. Decoding is done greedily. If a label does not follow the BIO syntax rules, i.e. an inside tag must follow the appropriate begin tag, then it is replaced with the outside label. Evaluation is done using the CoNLL evaluation script BIBREF24 to calculate the F1 score. This is the standard way of evaluating slot-filling models in the literature.", + "In recent work on language modeling, a neural architecture that combined fixed word embeddings with character-based embeddings was found to to be useful for handling previously unseen words BIBREF25 . Based on that result, the embeddings in the open vocabulary model are a concatenation of the character-based embeddings with fixed word embeddings. When an out-of-vocabulary word is encountered, its character-based embedding is concatenated with the embedding for the unknown word token. The character-based embeddings are generated from a two layer bi-LSTM that processes each word one character at a time. The character-based word embedding is produced by concatenating the last states from each of the directional LSTM's in the second layer and passing them through a linear layer for dimensionality reduction." + ], + [ + "Crowd-sourced data was collected simulating common use cases for four different apps: United Airlines, Airbnb, Greyhound bus service and OpenTable. The corresponding actions are booking a flight, renting a home, buying bus tickets, and making a reservation at a restaurant. In order to elicit natural language, crowd workers were instructed to simulate a conversation with a friend planning an activity as opposed to giving a command to the computer. Workers were prompted with a slot type/value pair and asked to form a reply to their friend using that information. The instructions were to not include any other potential slots in the sentence but this instruction was not always followed by the workers.", + "Slot types were chosen to roughly correspond to form fields and UI elements, such as check boxes or dropdown menus, on the respective apps. The amount of data collected per app and the number of slot types is listed in Table 1 . The slot types for each app are described in Table 2 , and an example labeled sentence from each app is given in Table 3 . One thing to notice is that the the number of slot types is relatively small when compared to the popular ATIS dataset that has over one hundred slot types BIBREF0 . In ATIS, separate slot types would be used for names of cities, states, or countries whereas in this data all of those would fall under a single slot for locations.", + "Slot values were pulled from manually created lists of locations, dates and times, restaurants, etc. Values for prompting each rater were sampled from these lists. Workers were instructed to use different re-phrasings of the prompted values, but most people used the prompted value verbatim. Occasionally, workers used an unprompted slot value not in the list.", + "For the word-level LSTM, the data was lower-cased and tokenized using a standard tokenizer. Spelling mistakes were not corrected. All digits were replaced by the '#' character. Words that appear only once in the training data are replaced with an unknown word token. For the character-based word embeddings used in the open vocabulary model, no lower casing or digit replacement is done.", + "Due to the way the OpenTable data was collected some slot values were over-represented leading to over fitting to those particular values. To correct this problem sentences that used the over-represented slot values had their values replaced by sampling from a larger list of potential values. The affected slot types are the ones for cuisine, restaurant names, and locations. This substitution made the OpenTable data more realistic as well as more similar to the other data that was collected.", + "The data we collected for the United Airlines app is an exception in a few ways: we collected four times as much data for this app than the other ones; workers were occasionally prompted with up to four slot type/value pairs; and workers were instructed to give commands to their device instead of simulating a conversation with a friend. For all of the other apps, workers were prompted to use a single slot type per sentence. We argue that having varying amounts of data for different apps is a realistic scenario.", + "Another possible source of data is the Air Travel Information Service (ATIS) data set collected in the early 1990's BIBREF0 . However, this data is sufficiently similar to the United collection, that it is not likely to add sufficient variety to improve the target domains. Further, it suffers from artifacts of data collected at a time with speech recognition systems had much higher error rates. The new data collected for this work fills a need raised in BIBREF26 , which concluded that lack of data was an impediment to progress in slot filling." + ], + [ + "The section describes two sets of experiments: the first is designed to test the effectiveness of the multi-task model and the second is designed to test the generalizability of the open vocabulary model. The scenario is that we already have $n-1$ models in place and we wish to discover how much data will be necessary to build a model for an additional application." + ], + [ + "The data is split to use 30% for training with 70% to be used for test data. The reason that a majority of the data is used for testing is that in the second experiment the results are reported separately for sentences containing out of vocabulary tokens and a large amount of data is needed to get a sufficient sample size. Hyperparameter tuning presents a challenge when operating in a low resource scenario. When there is barely enough data to train the model none can be spared for a validation set. We used data from the United app for hyperparameter tuning since it is the largest and assumed that the hyperparameter settings generalized to the other apps.", + "Training is done using stochastic gradient descent with minibatches of 25 sentences. The initial learning rate is 0.3 and is set to decay to 98% of its value every 100 minibatches. For the multi-task model, training proceeds by alternating between each of the tasks when selecting the next minibatch. All the parameters are initialized uniformly in the range [-0.1, 0.1]. Dropout is used for regularization on the word embeddings and on the outputs from each LSTM layer with the dropout probability set to 60% BIBREF27 .", + "For the single-task model, the word embeddings are 60 dimensional and the LSTM is dimension 100 with a 70 dimensional projection layer on the LSTM. For the multi-task model, word embeddings are 200 dimensional, and the LSTM has 250 dimensions with a 170 dimensional projection layer. For the open vocabulary version of the model, the 200-dimensional input is a concatenation of 160-dimensional traditional word embeddings with 40-dimensional character-based word embeddings. The character embedding layer is 15 dimensions, the first LSTM layer is 40 dimensions with a 20 dimensional projection layer, and the second LSTM layer is 130 dimensions." + ], + [ + "We compare a single-task model against the multi-task model for varying amounts of training data. In the multi-task model, the full amount of data is used for $n-1$ apps and the amount of data is allowed to vary only for the $n$ -th application. These experiments use the traditional word embeddings with a closed vocabulary. Since the data for the United app is bigger than the other three apps combined, it is used as an anchor for the multi-task model. The other three apps alternate in the position of the $n$ -th app. The data usage for the $n$ -th app is varied while the other $n-1$ apps in each experiment use the full amount of available training data. The full amount of training data is different for each app. The data used for the $n$ -th app is 200, 400, or 800 sentences or all available training data depending on the experiment. The test set remains fixed for all of the experiments even as part of the training data is discarded to simulate the low resource scenario.", + "In Figure 1 we show the single-task vs. multi-task model performance for each of three different applications. The multi-task model outperforms the single-task model at all data sizes, and the relative performance increases as the size of the training data decreases. When only 200 sentences of training data are used, the performance of the multi-task model is about 60% better than the single-task model for both the Airbnb and Greyhound apps. The relative gain for the OpenTable app is 26%. Because the performance of the multi-task model decays much more slowly as the amount of training data is reduced, the multi-task model can deliver the same performance with a considerable reduction in the amount of labeled data." + ], + [ + "The open vocabulary model experiments test the ability of the model to handle unseen words in test time, which are particularly likely to occur when using a reduced amount of training data. In these experiments the open vocabulary model is compared against the fixed embedding model. The results are reported separately for the sentences that contain out of vocabulary tokens, since these are where the open vocabulary system is expected to have an advantage.", + "Figure 2 gives the OOV rate for each app for varying amounts of training data plotted on a log-log scale. The OOV words tend to be task-specific terminology. For example, the OpenTable task is the only one that has names of restaurants but names of cities are present in all four tasks so they tend to be covered better. The OOV rate dramatically increases when the size of the training data is less than 500 sentences. Since our goal is to operate in the regime of less than 500 sentences per task, handling OOVs is a priority. The multi-task model is used in these experiments. The only difference between the closed vocabulary and open vocabulary systems is that the closed vocabulary system uses the traditional word embeddings and the open vocabulary system uses the traditional word embeddings concatenated with character-based embeddings.", + "Table 4 reports F1 scores on the test set for both the closed and open vocabulary systems. The results differ between the tasks, but none have an overall benefit from the open vocabulary system. Looking at the subset of sentences that contain an OOV token, the open vocabulary system delivers increased performance on the Airbnb and Greyhound tasks. These two are the most difficult apps out of the four and therefore had the most room for improvement. The United app is also all lower case and casing is an important clue for detecting proper nouns that the open vocabulary model takes advantage of.", + "Looking a little deeper, in Figure 3 we show the breakdown in performance across individual slot types. Only those slot types which occur at least one hundred times in the test data are shown in this figure. The slot types that are above the diagonal saw a performance improvement using the open vocabulary model. The opposite is true for those that are below the diagonal. The open vocabulary system appears to do worse on slots that express quantities, dates and times and better on slots with greater slot perplexity (i.e., greater variation in slot values) like ones relating to locations. The three slots where the open vocabulary model gave the biggest gain are the Greyhound LeavingFrom and GoingTo slots along with the Airbnb Amenities slot. The three slots where the open vocabulary model did the worst relative to the closed vocabulary model are the Airbnb Price slot, along with the Greyhound DiscountType and DepartDate slots. The Amenities slot is an example of a slot with higher perplexity (with options related to pets, availability of a gym, parking, fire extinguishers, proximity to attractions), and the DiscountType is one with lower perplexity (three options cover almost all cases). We hypothesize that the reason that the numerical slots are better under the closed vocabulary model is due to their relative simplicity and not an inability of the character embeddings to learn representations for numbers." + ], + [ + "In summary, we find that using a multi-task model with shared embeddings gives a large reduction in the minimum amount of data needed to train a slot-filling model for a new app. This translates into a cost savings for deploying slot filling models for new applications. The combination of the multi-task model with the open vocabulary embeddings increases the generalizability of the model especially when there are OOVs in the sentence. These contributions allow for scalable slot filling models.", + "For future work, there are some improvements that could be made to the model such as the addition of an attentional mechanism to help with long distance dependencies BIBREF15 , use of beam-search to improve decoding, and exploring unsupervised adaptation as in BIBREF19 .", + "Another item for future work is to collect additional tasks to examine the scalability of the multi-task model beyond the four applications that were used in this work. Due to their extra depth, character-based methods usually require more data than word based models BIBREF28 . Since this paper uses limited data, the collection of additional tasks may significantly improve the performance of the open vocabulary model." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0754/instruction.md b/qasper-0754/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..1c91d67a9ca3f5a465dd6bc3b9376864c23ff06b --- /dev/null +++ b/qasper-0754/instruction.md @@ -0,0 +1,68 @@ +Name of Paper: Domain Adaptation of Recurrent Neural Networks for Natural Language Understanding + +Question: What is the size of the open vocabulary? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Model", + "Data", + "Experiments", + "Training and Model Configuration Details", + "Multi-task Model Experiments", + "Open Vocabulary Model Experiments", + "Conclusions" + ], + "paragraphs": [ + [ + "Slot filling models are a useful method for simple natural language understanding tasks, where information can be extracted from a sentence and used to perform some structured action. For example, dates, departure cities and destinations represent slots to fill in a flight booking task. This information is extracted from natural language queries leveraging typical context associated with each slot type. Researchers have been exploring data-driven approaches to learning models for automatic identification of slot information since the 90's, and significant advances have been made BIBREF0 . Our paper builds on recent work on slot-filling using recurrent neural networks (RNNs) with a focus on the problem of training from minimal annotated data, taking an approach of sharing data from multiple tasks to reduce the amount of data for developing a new task.", + "As candidate tasks, we consider the actions that a user might perform via apps on their phone. Typically, a separate slot-filling model would be trained for each app. For example, one model understands queries about classified ads for cars BIBREF1 and another model handles queries about the weather BIBREF2 . As the number of apps increases, this approach becomes impractical due to the burden of collecting and labeling the training data for each model. In addition, using independent models for each task has high storage costs for mobile devices.", + "Alternatively, a single model can be learned to handle all of the apps. This type of approach is known as multi-task learning and can lead to improved performance on all of the tasks due to information sharing between the different apps BIBREF3 . Multi-task learning in combination with neural networks has been shown to be effective for natural language processing tasks BIBREF4 . When using RNNs for slot filling, almost all of the model parameters can be shared between tasks. In our study, only the relatively small output layer, which consists of slot embeddings, is individual to each app. More sharing means that less training data per app can be used and there will still be enough data to effectively train the network. The multi-task approach has lower data requirements, which leads to a large cost savings and makes this approach scalable to large numbers of applications.", + "The shared representation that we build on leverages recent work on slot filling models that use neural network based approaches. Early neural network based papers propose feedforward BIBREF5 or RNN architectures BIBREF6 , BIBREF7 . The focus shifted to RNN's with long-short term memory cells (LSTMs) BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 after LSTMs were shown to be effective for other tasks BIBREF12 . The most recent papers use variations on LSTM sequence models, including encoder-decoder, external memory, or attention architectures BIBREF13 , BIBREF14 , BIBREF15 . The particular variant that we build on is a bidirectional LSTM, similar to BIBREF16 , BIBREF11 .", + "One highly desirable property of a good slot filling model is to generalize to previously unseen slot values. For instance, we should not expect that the model will see the names of all the cities during training time, especially when only a small amount of training data is used. We address the generalizability issue by incorporating the open vocabulary embeddings from Ling et al. into our model BIBREF17 . These embeddings work by using a character RNN to process a word one letter at a time. This way the model can learn to share parameters between different words that use the same morphemes. For example BBQ restaurants frequently use words like \u201csmokehouse\u201d, \u201csteakhouse\u201d, and \u201croadhouse\u201d in their names and \u201cBayside\u201d,\u201cBayview\u201d, and \u201cBaywood\u201d are all streets in San Francisco. Recognizing these patterns would be helpful in detecting a restaurant or street name slot, respectively.", + "The two main contributions of this work are the multi-task model and the use of the open vocabulary character-based embeddings, which together allow for scalable slot filling models. Our work on multi-task learning in slot filling differs from its previous use in BIBREF18 in that we allow for soft sharing between tasks instead of explicitly matching slots to each other across different tasks. A limitation of explicit slot matching is that two slots that appear to have the same underlying type, such as location-based slots, may actually use the slot information in different ways depending on the overall intent of the task. In our model, the sharing between tasks is done implicitly by the neural network. Our approach to handling words unseen in training data is different from the delexicalization proposed in BIBREF19 in that we do not require the vocabulary items associated with slots and values to be prespecified. It is complementary to work on extending domain coverage BIBREF20 , BIBREF21 .", + "The proposed model is described in more detail in Section \"Model\" . The approach is assessed on a new data collection based on four apps, described in Section \"Data\" . The experiments described in Section \"Training and Model Configuration Details\" investigate how much data is necessary for the $n$ -th app using a multi-task model that leverages the data from the previous $n-1$ apps, with results compared against the single-task model that only utilizes the data from the $n$ -th app. We conclude in Section \"Conclusions\" with a summary of the key findings and discussion of opportunities for future work." + ], + [ + "Our model has a word embedding layer, followed by a bi-directional LSTM (bi-LSTM), and a softmax output layer. The bi-LSTM allows the model to use information from both the right and left contexts of each word when making predictions. We choose this architecture because similar models have been used in prior work on slot filling and have achieved good results BIBREF16 , BIBREF11 . The LSTM gates are used as defined by Sak et al. including the use of the linear projection layer on the output of the LSTM BIBREF22 . The purpose of the projection layer is to produce a model with fewer parameters without reducing the number of LSTM memory cells. For the multi-task model, the word embeddings and the bi-LSTM parameters are shared across tasks but each task has its own softmax layer. This means that if the multi-task model has half a million parameters, only a couple thousand of them are unique to each task and the other 99.5% are shared between all of the tasks.", + "The slot labels are encoded in BIO format BIBREF23 indicating if a word is the beginning, inside or outside any particular slot. Decoding is done greedily. If a label does not follow the BIO syntax rules, i.e. an inside tag must follow the appropriate begin tag, then it is replaced with the outside label. Evaluation is done using the CoNLL evaluation script BIBREF24 to calculate the F1 score. This is the standard way of evaluating slot-filling models in the literature.", + "In recent work on language modeling, a neural architecture that combined fixed word embeddings with character-based embeddings was found to to be useful for handling previously unseen words BIBREF25 . Based on that result, the embeddings in the open vocabulary model are a concatenation of the character-based embeddings with fixed word embeddings. When an out-of-vocabulary word is encountered, its character-based embedding is concatenated with the embedding for the unknown word token. The character-based embeddings are generated from a two layer bi-LSTM that processes each word one character at a time. The character-based word embedding is produced by concatenating the last states from each of the directional LSTM's in the second layer and passing them through a linear layer for dimensionality reduction." + ], + [ + "Crowd-sourced data was collected simulating common use cases for four different apps: United Airlines, Airbnb, Greyhound bus service and OpenTable. The corresponding actions are booking a flight, renting a home, buying bus tickets, and making a reservation at a restaurant. In order to elicit natural language, crowd workers were instructed to simulate a conversation with a friend planning an activity as opposed to giving a command to the computer. Workers were prompted with a slot type/value pair and asked to form a reply to their friend using that information. The instructions were to not include any other potential slots in the sentence but this instruction was not always followed by the workers.", + "Slot types were chosen to roughly correspond to form fields and UI elements, such as check boxes or dropdown menus, on the respective apps. The amount of data collected per app and the number of slot types is listed in Table 1 . The slot types for each app are described in Table 2 , and an example labeled sentence from each app is given in Table 3 . One thing to notice is that the the number of slot types is relatively small when compared to the popular ATIS dataset that has over one hundred slot types BIBREF0 . In ATIS, separate slot types would be used for names of cities, states, or countries whereas in this data all of those would fall under a single slot for locations.", + "Slot values were pulled from manually created lists of locations, dates and times, restaurants, etc. Values for prompting each rater were sampled from these lists. Workers were instructed to use different re-phrasings of the prompted values, but most people used the prompted value verbatim. Occasionally, workers used an unprompted slot value not in the list.", + "For the word-level LSTM, the data was lower-cased and tokenized using a standard tokenizer. Spelling mistakes were not corrected. All digits were replaced by the '#' character. Words that appear only once in the training data are replaced with an unknown word token. For the character-based word embeddings used in the open vocabulary model, no lower casing or digit replacement is done.", + "Due to the way the OpenTable data was collected some slot values were over-represented leading to over fitting to those particular values. To correct this problem sentences that used the over-represented slot values had their values replaced by sampling from a larger list of potential values. The affected slot types are the ones for cuisine, restaurant names, and locations. This substitution made the OpenTable data more realistic as well as more similar to the other data that was collected.", + "The data we collected for the United Airlines app is an exception in a few ways: we collected four times as much data for this app than the other ones; workers were occasionally prompted with up to four slot type/value pairs; and workers were instructed to give commands to their device instead of simulating a conversation with a friend. For all of the other apps, workers were prompted to use a single slot type per sentence. We argue that having varying amounts of data for different apps is a realistic scenario.", + "Another possible source of data is the Air Travel Information Service (ATIS) data set collected in the early 1990's BIBREF0 . However, this data is sufficiently similar to the United collection, that it is not likely to add sufficient variety to improve the target domains. Further, it suffers from artifacts of data collected at a time with speech recognition systems had much higher error rates. The new data collected for this work fills a need raised in BIBREF26 , which concluded that lack of data was an impediment to progress in slot filling." + ], + [ + "The section describes two sets of experiments: the first is designed to test the effectiveness of the multi-task model and the second is designed to test the generalizability of the open vocabulary model. The scenario is that we already have $n-1$ models in place and we wish to discover how much data will be necessary to build a model for an additional application." + ], + [ + "The data is split to use 30% for training with 70% to be used for test data. The reason that a majority of the data is used for testing is that in the second experiment the results are reported separately for sentences containing out of vocabulary tokens and a large amount of data is needed to get a sufficient sample size. Hyperparameter tuning presents a challenge when operating in a low resource scenario. When there is barely enough data to train the model none can be spared for a validation set. We used data from the United app for hyperparameter tuning since it is the largest and assumed that the hyperparameter settings generalized to the other apps.", + "Training is done using stochastic gradient descent with minibatches of 25 sentences. The initial learning rate is 0.3 and is set to decay to 98% of its value every 100 minibatches. For the multi-task model, training proceeds by alternating between each of the tasks when selecting the next minibatch. All the parameters are initialized uniformly in the range [-0.1, 0.1]. Dropout is used for regularization on the word embeddings and on the outputs from each LSTM layer with the dropout probability set to 60% BIBREF27 .", + "For the single-task model, the word embeddings are 60 dimensional and the LSTM is dimension 100 with a 70 dimensional projection layer on the LSTM. For the multi-task model, word embeddings are 200 dimensional, and the LSTM has 250 dimensions with a 170 dimensional projection layer. For the open vocabulary version of the model, the 200-dimensional input is a concatenation of 160-dimensional traditional word embeddings with 40-dimensional character-based word embeddings. The character embedding layer is 15 dimensions, the first LSTM layer is 40 dimensions with a 20 dimensional projection layer, and the second LSTM layer is 130 dimensions." + ], + [ + "We compare a single-task model against the multi-task model for varying amounts of training data. In the multi-task model, the full amount of data is used for $n-1$ apps and the amount of data is allowed to vary only for the $n$ -th application. These experiments use the traditional word embeddings with a closed vocabulary. Since the data for the United app is bigger than the other three apps combined, it is used as an anchor for the multi-task model. The other three apps alternate in the position of the $n$ -th app. The data usage for the $n$ -th app is varied while the other $n-1$ apps in each experiment use the full amount of available training data. The full amount of training data is different for each app. The data used for the $n$ -th app is 200, 400, or 800 sentences or all available training data depending on the experiment. The test set remains fixed for all of the experiments even as part of the training data is discarded to simulate the low resource scenario.", + "In Figure 1 we show the single-task vs. multi-task model performance for each of three different applications. The multi-task model outperforms the single-task model at all data sizes, and the relative performance increases as the size of the training data decreases. When only 200 sentences of training data are used, the performance of the multi-task model is about 60% better than the single-task model for both the Airbnb and Greyhound apps. The relative gain for the OpenTable app is 26%. Because the performance of the multi-task model decays much more slowly as the amount of training data is reduced, the multi-task model can deliver the same performance with a considerable reduction in the amount of labeled data." + ], + [ + "The open vocabulary model experiments test the ability of the model to handle unseen words in test time, which are particularly likely to occur when using a reduced amount of training data. In these experiments the open vocabulary model is compared against the fixed embedding model. The results are reported separately for the sentences that contain out of vocabulary tokens, since these are where the open vocabulary system is expected to have an advantage.", + "Figure 2 gives the OOV rate for each app for varying amounts of training data plotted on a log-log scale. The OOV words tend to be task-specific terminology. For example, the OpenTable task is the only one that has names of restaurants but names of cities are present in all four tasks so they tend to be covered better. The OOV rate dramatically increases when the size of the training data is less than 500 sentences. Since our goal is to operate in the regime of less than 500 sentences per task, handling OOVs is a priority. The multi-task model is used in these experiments. The only difference between the closed vocabulary and open vocabulary systems is that the closed vocabulary system uses the traditional word embeddings and the open vocabulary system uses the traditional word embeddings concatenated with character-based embeddings.", + "Table 4 reports F1 scores on the test set for both the closed and open vocabulary systems. The results differ between the tasks, but none have an overall benefit from the open vocabulary system. Looking at the subset of sentences that contain an OOV token, the open vocabulary system delivers increased performance on the Airbnb and Greyhound tasks. These two are the most difficult apps out of the four and therefore had the most room for improvement. The United app is also all lower case and casing is an important clue for detecting proper nouns that the open vocabulary model takes advantage of.", + "Looking a little deeper, in Figure 3 we show the breakdown in performance across individual slot types. Only those slot types which occur at least one hundred times in the test data are shown in this figure. The slot types that are above the diagonal saw a performance improvement using the open vocabulary model. The opposite is true for those that are below the diagonal. The open vocabulary system appears to do worse on slots that express quantities, dates and times and better on slots with greater slot perplexity (i.e., greater variation in slot values) like ones relating to locations. The three slots where the open vocabulary model gave the biggest gain are the Greyhound LeavingFrom and GoingTo slots along with the Airbnb Amenities slot. The three slots where the open vocabulary model did the worst relative to the closed vocabulary model are the Airbnb Price slot, along with the Greyhound DiscountType and DepartDate slots. The Amenities slot is an example of a slot with higher perplexity (with options related to pets, availability of a gym, parking, fire extinguishers, proximity to attractions), and the DiscountType is one with lower perplexity (three options cover almost all cases). We hypothesize that the reason that the numerical slots are better under the closed vocabulary model is due to their relative simplicity and not an inability of the character embeddings to learn representations for numbers." + ], + [ + "In summary, we find that using a multi-task model with shared embeddings gives a large reduction in the minimum amount of data needed to train a slot-filling model for a new app. This translates into a cost savings for deploying slot filling models for new applications. The combination of the multi-task model with the open vocabulary embeddings increases the generalizability of the model especially when there are OOVs in the sentence. These contributions allow for scalable slot filling models.", + "For future work, there are some improvements that could be made to the model such as the addition of an attentional mechanism to help with long distance dependencies BIBREF15 , use of beam-search to improve decoding, and exploring unsupervised adaptation as in BIBREF19 .", + "Another item for future work is to collect additional tasks to examine the scalability of the multi-task model beyond the four applications that were used in this work. Due to their extra depth, character-based methods usually require more data than word based models BIBREF28 . Since this paper uses limited data, the collection of additional tasks may significantly improve the performance of the open vocabulary model." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0762/instruction.md b/qasper-0762/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..993ae32af301a03aa35f38b1224306d6efc97c91 --- /dev/null +++ b/qasper-0762/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering + +Question: What languages do they experiment with? \ No newline at end of file diff --git a/qasper-0763/instruction.md b/qasper-0763/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..0a0a51bbd4e2c400198c75f7fc73d76f740caa03 --- /dev/null +++ b/qasper-0763/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering + +Question: What are the baselines? \ No newline at end of file diff --git a/qasper-0764/instruction.md b/qasper-0764/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..e3f0300c70e892caeb5c26d0eab02ade585ac825 --- /dev/null +++ b/qasper-0764/instruction.md @@ -0,0 +1,141 @@ +Name of Paper: Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering + +Question: What was the inter-annotator agreement? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Factoid QA as Sequence Labeling", + "Overview", + "Long Short-Term Memory (LSTM)", + "Question LSTM", + "Evidence LSTMs", + "Sequence Labeling", + "Training", + "WebQA Dataset", + "Baselines", + "Evaluation Method", + "Model Settings", + "Comparison with Baselines", + "Evaluation on the Entire WebQA Dataset", + "Effect of Word Embedding", + "Effect of q-e.comm and e-e.comm Features", + "Effect of Question Representations", + "Effect of Evidence LSTMs Structures", + "Word-based v.s. Character-based Input", + "Conclusion and Future Work" + ], + "paragraphs": [ + [ + "Question answering (QA) with neural network, i.e. neural QA, is an active research direction along the road towards the long-term AI goal of building general dialogue agents BIBREF0 . Unlike conventional methods, neural QA does not rely on feature engineering and is (at least nearly) end-to-end trainable. It reduces the requirement for domain specific knowledge significantly and makes domain adaption easier. Therefore, it has attracted intensive attention in recent years.", + "Resolving QA problem requires several fundamental abilities including reasoning, memorization, etc. Various neural methods have been proposed to improve such abilities, including neural tensor networks BIBREF1 , recursive networks BIBREF2 , convolution neural networks BIBREF3 , BIBREF4 , BIBREF5 , attention models BIBREF6 , BIBREF5 , BIBREF7 , and memories BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , etc. These methods achieve promising results on various datasets, which demonstrates the high potential of neural QA. However, we believe there are still two major challenges for neural QA:", + "System development and/or evaluation on real-world data: Although several high quality and well-designed QA datasets have been proposed in recent years, there are still problems about using them to develop and/or evaluate QA system under real-world settings due to data size and the way they are created. For example, bAbI BIBREF0 and the 30M Factoid Question-Answer Corpus BIBREF13 are artificially synthesized; the TREC datasets BIBREF14 , Free917 BIBREF15 and WebQuestions BIBREF16 are human generated but only have few thousands of questions; SimpleQuestions BIBREF11 and the CNN and Daily Mail news datasets BIBREF6 are large but generated under controlled conditions. Thus, a new large-scale real-world QA dataset is needed.", + "A new design choice for answer production besides sequence generation and classification/ranking: Without loss of generality, the methods used for producing answers in existing neural QA works can be roughly categorized into the sequence generation type and the classification/ranking type. The former generates answers word by word, e.g. BIBREF0 , BIBREF10 , BIBREF6 . As it generally involves INLINEFORM0 computation over a large vocabulary, the computational cost is remarkably high and it is hard to produce answers with out-of-vocabulary word. The latter produces answers by classification over a predefined set of answers, e.g. BIBREF12 , or ranking given candidates by model score, e.g. BIBREF5 . Although it generally has lower computational cost than the former, it either also has difficulties in handling unseen answers or requires an extra candidate generating component which is hard for end-to-end training. Above all, we need a new design choice for answer production that is both computationally effective and capable of handling unseen words/answers.", + "In this work, we address the above two challenges by a new dataset and a new neural QA model. Our contributions are two-fold:", + "Experimental results show that our model outperforms baselines with a large margin on the WebQA dataset, indicating that it is effective. Furthermore, our model even achieves an F1 score of 70.97% on character-based input, which is comparable with the 74.69% F1 score on word-based input, demonstrating that our model is robust." + ], + [ + "In this work, we focus on open-domain factoid QA. Taking Figure FIGREF3 as an example, we formalize the problem as follows: given each question Q, we have one or more evidences E, and the task is to produce the answer A, where an evidence is a piece of text of any length that contains relevant information to answer the question. The advantage of this formalization is that evidences can be retrieved from web or unstructured knowledge base, which can improve system coverage significantly.", + "Inspired by BIBREF18 , we introduce end-to-end sequence labeling as a new design choice for answer production in neural QA. Given a question and an evidence, we use CRF BIBREF17 to assign a label to each word in the evidence to indicate whether the word is at the beginning (B), inside (I) or outside (O) of the answer (see Figure FIGREF3 for example). The key difference between our work and BIBREF18 is that BIBREF18 needs a lot work on feature engineering which further relies on POS/NER tagging, dependency parsing, question type analysis, etc. While we avoid feature engineering, and only use one single model to solve the problem. Furthermore, compared with sequence generation and classification/ranking methods for answer production, our method avoids expensive INLINEFORM0 computation and can handle unseen answers/words naturally in a principled way.", + "Formally, we formalize QA as a sequence labeling problem as follows: suppose we have a vocabulary INLINEFORM0 of size INLINEFORM1 , given question INLINEFORM2 and evidence INLINEFORM3 , where INLINEFORM4 and INLINEFORM5 are one-hot vectors of dimension INLINEFORM6 , and INLINEFORM7 and INLINEFORM8 are the number of words in the question and evidence respectively. The problem is to find the label sequence INLINEFORM9 which maximizes the conditional probability under parameter INLINEFORM10 DISPLAYFORM0 ", + "In this work, we model INLINEFORM0 by a neural network composed of LSTMs and CRF." + ], + [ + "Figure FIGREF4 shows the structure of our model. The model consists of three components: (1) question LSTM for computing question representation; (2) evidence LSTMs for evidence analysis; and (3) a CRF layer for sequence labeling. The question LSTM in a form of a single layer LSTM equipped with a single time attention takes the question as input and generates the question representation INLINEFORM0 . The three-layer evidence LSTMs takes the evidence, question representation INLINEFORM1 and optional features as input and produces \u201cfeatures\u201d for the CRF layer. The CRF layer takes the \u201cfeatures\u201d as input and produces the label sequence. The details will be given in the following sections." + ], + [ + "Following BIBREF19 , we define INLINEFORM0 as a function mapping its input INLINEFORM1 , previous state INLINEFORM2 and output INLINEFORM3 to current state INLINEFORM4 and output INLINEFORM5 : DISPLAYFORM0 ", + "where INLINEFORM0 are parameter matrices, INLINEFORM1 are biases, INLINEFORM2 is LSTM layer width, INLINEFORM3 is the INLINEFORM4 function, INLINEFORM5 , INLINEFORM6 and INLINEFORM7 are the input gate, forget gate and output gate respectively." + ], + [ + "The question LSTM consists of a single-layer LSTM and a single-time attention model. The question INLINEFORM0 is fed into the LSTM to produce a sequence of vector representations INLINEFORM1 DISPLAYFORM0 ", + "where INLINEFORM0 is the embedding matrix and INLINEFORM1 is word embedding dimension. Then a weight INLINEFORM2 is computed by the single-time attention model for each INLINEFORM3 DISPLAYFORM0 ", + "where INLINEFORM0 and INLINEFORM1 . And finally the weighted average INLINEFORM2 of INLINEFORM3 is used as the representation of the question DISPLAYFORM0 " + ], + [ + "The three-layer evidence LSTMs processes evidence INLINEFORM0 INLINEFORM1 to produce \u201cfeatures\u201d for the CRF layer.", + "The first LSTM layer takes evidence INLINEFORM0 , question representation INLINEFORM1 and optional features as input. We find the following two simple common word indicator features are effective:", + "Question-Evidence common word feature (q-e.comm): for each word in the evidence, the feature has value 1 when the word also occurs in the question, otherwise 0. The intuition is that words occurring in questions tend not to be part of the answers for factoid questions.", + "", + "Evidence-Evidence common word feature (e-e.comm): for each word in the evidence, the feature has value 1 when the word occurs in another evidence, otherwise 0. The intuition is that words shared by two or more evidences are more likely to be part of the answers.", + "Although counterintuitive, we found non-binary e-e.comm feature values does not work well. Because the more evidences we considered, the more words tend to get non-zero feature values, and the less discriminative the feature is.", + "The second LSTM layer stacks on top of the first LSTM layer, but processes its output in a reverse order. The third LSTM layer stacks upon the first and second LSTM layers with cross layer links, and its output serves as features for CRF layer.", + "Formally, the computations are defined as follows DISPLAYFORM0 ", + "where INLINEFORM0 and INLINEFORM1 are one-hot feature vectors, INLINEFORM2 and INLINEFORM3 are embeddings for the features, and INLINEFORM4 and INLINEFORM5 are the feature embedding dimensions. Note that we use the same word embedding matrix INLINEFORM6 as in question LSTM." + ], + [ + "Following BIBREF20 , BIBREF21 , we use CRF on top of evidence LSTMs for sequence labeling. The probability of a label sequence INLINEFORM0 given question INLINEFORM1 and evidence INLINEFORM2 is computed as DISPLAYFORM0 ", + "where INLINEFORM0 , INLINEFORM1 , INLINEFORM2 is the number of label types, INLINEFORM3 is the transition weight from label INLINEFORM4 to INLINEFORM5 , and INLINEFORM6 is the INLINEFORM7 -th value of vector INLINEFORM8 ." + ], + [ + "The objective function of our model is INLINEFORM0 ", + "where INLINEFORM0 is the golden label sequence, and INLINEFORM1 is training set.", + "We use a minibatch stochastic gradient descent (SGD) BIBREF22 algorithm with rmsprop BIBREF23 to minimize the objective function. The initial learning rate is 0.001, batch size is 120, and INLINEFORM0 . We also apply dropout BIBREF24 to the output of all the LSTM layers. The dropout rate is 0.05. All these hyper-parameters are determined empirically via grid search on validation set." + ], + [ + "In order to train and evaluate open-domain factoid QA system for real-world questions, we build a new Chinese QA dataset named as WebQA. The dataset consists of tuples of (question, evidences, answer), which is similar to example in Figure FIGREF3 . All the questions, evidences and answers are collected from web. Table TABREF20 shows some statistics of the dataset.", + "The questions and answers are mainly collected from a large community QA website Baidu Zhidao and a small portion are from hand collected web documents. Therefore, all these questions are indeed asked by real-world users in daily life instead of under controlled conditions. All the questions are of single-entity factoid type, which means (1) each question is a factoid question and (2) its answer involves only one entity (but may have multiple words). The question in Figure FIGREF3 is a positive example, while the question \u201cWho are the children of Albert Enistein?\u201d is a counter example because the answer involves three persons. The type and correctness of all the question answer pairs are verified by at least two annotators.", + "All the evidences are retrieved from Internet by using a search engine with questions as queries. We download web pages returned in the first 3 result pages and take all the text pieces which have no more than 5 sentences and include at least one question word as candidate evidences. As evidence retrieval is beyond the scope of this work, we simply use TF-IDF values to re-rank these candidates.", + "For each question in the training set, we provide the top 10 ranked evidences to annotate (\u201cAnnotated Evidence\u201d in Table TABREF20 ). An evidence is annotated as positive if the question can be answered by just reading the evidence without any other prior knowledge, otherwise negative. Only evidences whose annotations are agreed by at least two annotators are retained. We also provide trivial negative evidences (\u201cRetrieved Evidence\u201d in Table TABREF20 ), i.e. evidences that do not contain golden standard answers.", + "For each question in the validation and test sets, we provide one major positive evidence, and maybe an additional positive one to compute features. Both of them are annotated. Raw retrieved evidences are also provided for evaluation purpose (\u201cRetrieved Evidence\u201d in Table TABREF20 ).", + "The dataset will be released on the project page http://idl.baidu.com/WebQA.html." + ], + [ + "We compare our model with two sets of baselines:", + "MemN2N BIBREF12 is an end-to-end trainable version of memory networks BIBREF9 . It encodes question and evidence with a bag-of-word method and stores the representations of evidences in an external memory. A recurrent attention model is used to retrieve relevant information from the memory to answer the question.", + "Attentive and Impatient Readers BIBREF6 use bidirectional LSTMs to encode question and evidence, and do classification over a large vocabulary based on these two encodings. The simpler Attentive Reader uses a similar way as our work to compute attention for the evidence. And the more complex Impatient Reader computes attention after processing each question word.", + "The key difference between our model and the two readers is that they produce answer by doing classification over a large vocabulary, which is computationally expensive and has difficulties in handling unseen words. However, as our model uses an end-to-end trainable sequence labeling technique, it avoids both of the two problems by its nature." + ], + [ + "The performance is measured with precision (P), recall (R) and F1-measure (F1) DISPLAYFORM0 ", + "where INLINEFORM0 is the list of correctly answered questions, INLINEFORM1 is the list of produced answers, and INLINEFORM2 is the list of all questions .", + "As WebQA is collected from web, the same answer may be expressed in different surface forms in the golden standard answer and the evidence, e.g. \u201c\u5317\u4eac (Beijing)\u201d v.s. \u201c\u5317\u4eac\u5e02 (Beijing province)\u201d. Therefore, we use two ways to count correctly answered questions, which are referred to as \u201cstrict\u201d and \u201cfuzzy\u201d in the tables:", + "Strict matching: A question is counted if and only if the produced answer is identical to the golden standard answer;", + "Fuzzy matching: A question is counted if and only if the produced answer is a synonym of the golden standard answer;", + "And we also consider two evaluation settings:", + "Annotated evidence: Each question has one major annotated evidence and maybe another annotated evidence for computing q-e.comm and e-e.comm features (Section SECREF14 );", + "Retrieved evidence: Each question is provided with at most 20 automatically retrieved evidences (see Section SECREF5 for details). All the evidences will be processed by our model independently and answers are voted by frequency to decide the final result. Note that a large amount of the evidences are negative and our model should not produce any answer for them." + ], + [ + "If not specified, the following hyper-parameters will be used in the reset of this section: LSTM layer width INLINEFORM0 (Section SECREF7 ), word embedding dimension INLINEFORM1 (Section SECREF9 ), feature embedding dimension INLINEFORM2 (Section SECREF9 ). The word embeddings are initialized with pre-trained embeddings using a 5-gram neural language model BIBREF25 and is fixed during training.", + "We will show that injecting noise data is important for improving performance on retrieved evidence setting in Section SECREF37 . In the following experiments, 20% of the training evidences will be negative ones randomly selected on the fly, of which 25% are annotated negative evidences and 75% are retrieved trivial negative evidences (Section SECREF5 ). The percentages are determined empirically. Intuitively, we provide the noise data to teach the model learning to recognize unreliable evidence.", + "For each evidence, we will randomly sample another evidence from the rest evidences of the question and compare them to compute the e-e.comm feature (Section SECREF14 ). We will develop more powerful models to process multiple evidences in a more principle way in the future.", + "As the answer for each question in our WebQA dataset only involves one entity (Section SECREF5 ), we distinguish label Os before and after the first B in the label sequence explicitly to discourage our model to produce multiple answers for a question. For example, the golden labels for the example evidence in Figure FIGREF3 will became \u201cEinstein/O1 married/O1 his/O1 first/O1 wife/O1 Mileva/B Mari\u0107/I in/O2 1903/O2\u201d, where we use \u201cO1\u201d and \u201cO2\u201d to denote label Os before and after the first B . \u201cFuzzy matching\u201d is also used for computing golden standard labels for training set.", + "For each setting, we will run three trials with different random seeds and report the average performance in the following sections." + ], + [ + "As the baselines can only predict one-word answers, we only do experiments on the one-word answer subset of WebQA, i.e. only questions with one-word answers are retained for training, validation and test. As shown in Table TABREF23 , our model achieves significant higher F1 scores than all the baselines.", + "The main reason for the relative low performance of MemN2N is that it uses a bag-of-word method to encode question and evidence such that higher order information like word order is absent to the model. We think its performance can be improved by designing more complex encoding methods BIBREF26 and leave it as a future work.", + "The Attentive and Impatient Readers only have access to the fixed length representations when doing classification. However, our model has access to the outputs of all the time steps of the evidence LSTMs, and scores the label sequence as a whole. Therefore, our model achieves better performance." + ], + [ + "In this section, we evaluate our model on the entire WebQA dataset. The evaluation results are shown in Table TABREF24 . Although producing multi-word answers is harder, our model achieves comparable results with the one-word answer subset (Table TABREF23 ), demonstrating that our model is effective for both single-word and multi-word word settings.", + "\u201cSoftmax\u201d in Table TABREF24 means we replace CRF with INLINEFORM0 , i.e. replace Eq. ( EQREF19 ) with DISPLAYFORM0 ", + "CRF outperforms INLINEFORM0 significantly in all cases. The reason is that INLINEFORM1 predicts each label independently, suggesting that modeling label transition explicitly is essential for improving performance. A natural choice for modeling label transition in INLINEFORM2 is to take the last prediction into account as in BIBREF27 . The result is shown in Table TABREF24 as \u201cSoftmax( INLINEFORM3 -1)\u201d. However, its performance is only comparable with \u201cSoftmax\u201d and significantly lower than CRF. The reason is that we can enumerate all possible label sequences implicitly by dynamic programming for CRF during predicting but this is not possible for \u201cSoftmax( INLINEFORM4 -1)\u201d , which indicates CRF is a better choice.", + "\u201cNoise\u201d in Table TABREF24 means whether we inject noise data or not (Section SECREF34 ). As all evidences are positive under the annotated evidence setting, the ability for recognizing unreliable evidence will be useless. Therefore, the performance of our model with and without noise is comparable under the annotated evidence setting. However, the ability is important to improve the performance under the retrieved evidence setting because a large amount of the retrieved evidences are negative ones. As a result, we observe significant improvement by injecting noise data for this setting." + ], + [ + "As stated in Section SECREF34 , the word embedding INLINEFORM0 is initialized with LM embedding and kept fixed in training. We evaluate different initialization and optimization methods in this section. The evaluation results are shown in Table TABREF40 . The second row shows the results when the embedding is optimized jointly during training. The performance drops significantly. Detailed analysis reveals that the trainable embedding enlarge trainable parameter number and the model gets over fitting easily. The model acts like a context independent entity tagger to some extend, which is not desired. For example, the model will try to find any location name in the evidence when the word \u201c\u5728\u54ea (where)\u201d occurs in the question. In contrary, pre-trained fixed embedding forces the model to pay more attention to the latent syntactic regularities. And it also carries basic priors such as \u201c\u68a8 (pear)\u201d is fruit and \u201c\u674e\u4e16\u77f3 (Lee Sedol)\u201d is a person, thus the model will generalize better to test data with fixed embedding. The third row shows the result when the embedding is randomly initialized and jointly optimized. The performance drops significantly further, suggesting that pre-trained embedding indeed carries meaningful priors." + ], + [ + "As shown in Table TABREF41 , both the q-e.comm and e-e.comm features are effective, and the q-e.comm feature contributes more to the overall performance. The reason is that the interaction between question and evidence is limited and q-e.comm feature with value 1, i.e. the corresponding word also occurs in the question, is a strong indication that the word may not be part of the answer." + ], + [ + "In this section, we compare the single-time attention method for computing INLINEFORM0 ( INLINEFORM1 , Eq. ( EQREF12 , EQREF13 )) with two widely used options: element-wise max operation INLINEFORM2 : INLINEFORM3 and element-wise average operation INLINEFORM4 : INLINEFORM5 . Intuitively, INLINEFORM6 can distill information in a more flexible way from { INLINEFORM7 }, while INLINEFORM8 tends to hide the differences between them, and INLINEFORM9 lies between INLINEFORM10 and INLINEFORM11 . The results in Table TABREF41 suggest that the more flexible and selective the operation is, the better the performance is." + ], + [ + "We investigate the effect of evidence LSTMs layer number, layer width and cross layer links in this section. The results are shown in Figure TABREF46 . For fair comparison, we do not use cross layer links in Figure TABREF46 (a) (dotted lines in Figure FIGREF4 ), and highlight the results with cross layer links (layer width 64) with circle and square for retrieved and annotated evidence settings respectively. We can conclude that: (1) generally the deeper and wider the model is, the better the performance is; (2) cross layer links are effective as they make the third evidence LSTM layer see information in both directions." + ], + [ + "Our model achieves fuzzy matching F1 scores of 69.78% and 70.97% on character-based input in annotated and retrieved evidence settings respectively (Table TABREF46 ), which are only 3.72 and 3.72 points lower than the corresponding scores on word-based input respectively. The performance is promising, demonstrating that our model is robust and effective." + ], + [ + "In this work, we build a new human annotated real-world QA dataset WebQA for developing and evaluating QA system on real-world QA data. We also propose a new end-to-end recurrent sequence labeling model for QA. Experimental results show that our model outperforms baselines significantly.", + "There are several future directions we plan to pursue. First, multi-entity factoid and non-factoid QA are also interesting topics. Second, we plan to extend our model to multi-evidence cases. Finally, inspired by Residual Network BIBREF28 , we will investigate deeper and wider models in the future." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0765/instruction.md b/qasper-0765/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..a88de797ef32df82abc2abe43fb5ad91a9cf69f4 --- /dev/null +++ b/qasper-0765/instruction.md @@ -0,0 +1,141 @@ +Name of Paper: Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering + +Question: Did they use a crowdsourcing platform? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Factoid QA as Sequence Labeling", + "Overview", + "Long Short-Term Memory (LSTM)", + "Question LSTM", + "Evidence LSTMs", + "Sequence Labeling", + "Training", + "WebQA Dataset", + "Baselines", + "Evaluation Method", + "Model Settings", + "Comparison with Baselines", + "Evaluation on the Entire WebQA Dataset", + "Effect of Word Embedding", + "Effect of q-e.comm and e-e.comm Features", + "Effect of Question Representations", + "Effect of Evidence LSTMs Structures", + "Word-based v.s. Character-based Input", + "Conclusion and Future Work" + ], + "paragraphs": [ + [ + "Question answering (QA) with neural network, i.e. neural QA, is an active research direction along the road towards the long-term AI goal of building general dialogue agents BIBREF0 . Unlike conventional methods, neural QA does not rely on feature engineering and is (at least nearly) end-to-end trainable. It reduces the requirement for domain specific knowledge significantly and makes domain adaption easier. Therefore, it has attracted intensive attention in recent years.", + "Resolving QA problem requires several fundamental abilities including reasoning, memorization, etc. Various neural methods have been proposed to improve such abilities, including neural tensor networks BIBREF1 , recursive networks BIBREF2 , convolution neural networks BIBREF3 , BIBREF4 , BIBREF5 , attention models BIBREF6 , BIBREF5 , BIBREF7 , and memories BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , etc. These methods achieve promising results on various datasets, which demonstrates the high potential of neural QA. However, we believe there are still two major challenges for neural QA:", + "System development and/or evaluation on real-world data: Although several high quality and well-designed QA datasets have been proposed in recent years, there are still problems about using them to develop and/or evaluate QA system under real-world settings due to data size and the way they are created. For example, bAbI BIBREF0 and the 30M Factoid Question-Answer Corpus BIBREF13 are artificially synthesized; the TREC datasets BIBREF14 , Free917 BIBREF15 and WebQuestions BIBREF16 are human generated but only have few thousands of questions; SimpleQuestions BIBREF11 and the CNN and Daily Mail news datasets BIBREF6 are large but generated under controlled conditions. Thus, a new large-scale real-world QA dataset is needed.", + "A new design choice for answer production besides sequence generation and classification/ranking: Without loss of generality, the methods used for producing answers in existing neural QA works can be roughly categorized into the sequence generation type and the classification/ranking type. The former generates answers word by word, e.g. BIBREF0 , BIBREF10 , BIBREF6 . As it generally involves INLINEFORM0 computation over a large vocabulary, the computational cost is remarkably high and it is hard to produce answers with out-of-vocabulary word. The latter produces answers by classification over a predefined set of answers, e.g. BIBREF12 , or ranking given candidates by model score, e.g. BIBREF5 . Although it generally has lower computational cost than the former, it either also has difficulties in handling unseen answers or requires an extra candidate generating component which is hard for end-to-end training. Above all, we need a new design choice for answer production that is both computationally effective and capable of handling unseen words/answers.", + "In this work, we address the above two challenges by a new dataset and a new neural QA model. Our contributions are two-fold:", + "Experimental results show that our model outperforms baselines with a large margin on the WebQA dataset, indicating that it is effective. Furthermore, our model even achieves an F1 score of 70.97% on character-based input, which is comparable with the 74.69% F1 score on word-based input, demonstrating that our model is robust." + ], + [ + "In this work, we focus on open-domain factoid QA. Taking Figure FIGREF3 as an example, we formalize the problem as follows: given each question Q, we have one or more evidences E, and the task is to produce the answer A, where an evidence is a piece of text of any length that contains relevant information to answer the question. The advantage of this formalization is that evidences can be retrieved from web or unstructured knowledge base, which can improve system coverage significantly.", + "Inspired by BIBREF18 , we introduce end-to-end sequence labeling as a new design choice for answer production in neural QA. Given a question and an evidence, we use CRF BIBREF17 to assign a label to each word in the evidence to indicate whether the word is at the beginning (B), inside (I) or outside (O) of the answer (see Figure FIGREF3 for example). The key difference between our work and BIBREF18 is that BIBREF18 needs a lot work on feature engineering which further relies on POS/NER tagging, dependency parsing, question type analysis, etc. While we avoid feature engineering, and only use one single model to solve the problem. Furthermore, compared with sequence generation and classification/ranking methods for answer production, our method avoids expensive INLINEFORM0 computation and can handle unseen answers/words naturally in a principled way.", + "Formally, we formalize QA as a sequence labeling problem as follows: suppose we have a vocabulary INLINEFORM0 of size INLINEFORM1 , given question INLINEFORM2 and evidence INLINEFORM3 , where INLINEFORM4 and INLINEFORM5 are one-hot vectors of dimension INLINEFORM6 , and INLINEFORM7 and INLINEFORM8 are the number of words in the question and evidence respectively. The problem is to find the label sequence INLINEFORM9 which maximizes the conditional probability under parameter INLINEFORM10 DISPLAYFORM0 ", + "In this work, we model INLINEFORM0 by a neural network composed of LSTMs and CRF." + ], + [ + "Figure FIGREF4 shows the structure of our model. The model consists of three components: (1) question LSTM for computing question representation; (2) evidence LSTMs for evidence analysis; and (3) a CRF layer for sequence labeling. The question LSTM in a form of a single layer LSTM equipped with a single time attention takes the question as input and generates the question representation INLINEFORM0 . The three-layer evidence LSTMs takes the evidence, question representation INLINEFORM1 and optional features as input and produces \u201cfeatures\u201d for the CRF layer. The CRF layer takes the \u201cfeatures\u201d as input and produces the label sequence. The details will be given in the following sections." + ], + [ + "Following BIBREF19 , we define INLINEFORM0 as a function mapping its input INLINEFORM1 , previous state INLINEFORM2 and output INLINEFORM3 to current state INLINEFORM4 and output INLINEFORM5 : DISPLAYFORM0 ", + "where INLINEFORM0 are parameter matrices, INLINEFORM1 are biases, INLINEFORM2 is LSTM layer width, INLINEFORM3 is the INLINEFORM4 function, INLINEFORM5 , INLINEFORM6 and INLINEFORM7 are the input gate, forget gate and output gate respectively." + ], + [ + "The question LSTM consists of a single-layer LSTM and a single-time attention model. The question INLINEFORM0 is fed into the LSTM to produce a sequence of vector representations INLINEFORM1 DISPLAYFORM0 ", + "where INLINEFORM0 is the embedding matrix and INLINEFORM1 is word embedding dimension. Then a weight INLINEFORM2 is computed by the single-time attention model for each INLINEFORM3 DISPLAYFORM0 ", + "where INLINEFORM0 and INLINEFORM1 . And finally the weighted average INLINEFORM2 of INLINEFORM3 is used as the representation of the question DISPLAYFORM0 " + ], + [ + "The three-layer evidence LSTMs processes evidence INLINEFORM0 INLINEFORM1 to produce \u201cfeatures\u201d for the CRF layer.", + "The first LSTM layer takes evidence INLINEFORM0 , question representation INLINEFORM1 and optional features as input. We find the following two simple common word indicator features are effective:", + "Question-Evidence common word feature (q-e.comm): for each word in the evidence, the feature has value 1 when the word also occurs in the question, otherwise 0. The intuition is that words occurring in questions tend not to be part of the answers for factoid questions.", + "", + "Evidence-Evidence common word feature (e-e.comm): for each word in the evidence, the feature has value 1 when the word occurs in another evidence, otherwise 0. The intuition is that words shared by two or more evidences are more likely to be part of the answers.", + "Although counterintuitive, we found non-binary e-e.comm feature values does not work well. Because the more evidences we considered, the more words tend to get non-zero feature values, and the less discriminative the feature is.", + "The second LSTM layer stacks on top of the first LSTM layer, but processes its output in a reverse order. The third LSTM layer stacks upon the first and second LSTM layers with cross layer links, and its output serves as features for CRF layer.", + "Formally, the computations are defined as follows DISPLAYFORM0 ", + "where INLINEFORM0 and INLINEFORM1 are one-hot feature vectors, INLINEFORM2 and INLINEFORM3 are embeddings for the features, and INLINEFORM4 and INLINEFORM5 are the feature embedding dimensions. Note that we use the same word embedding matrix INLINEFORM6 as in question LSTM." + ], + [ + "Following BIBREF20 , BIBREF21 , we use CRF on top of evidence LSTMs for sequence labeling. The probability of a label sequence INLINEFORM0 given question INLINEFORM1 and evidence INLINEFORM2 is computed as DISPLAYFORM0 ", + "where INLINEFORM0 , INLINEFORM1 , INLINEFORM2 is the number of label types, INLINEFORM3 is the transition weight from label INLINEFORM4 to INLINEFORM5 , and INLINEFORM6 is the INLINEFORM7 -th value of vector INLINEFORM8 ." + ], + [ + "The objective function of our model is INLINEFORM0 ", + "where INLINEFORM0 is the golden label sequence, and INLINEFORM1 is training set.", + "We use a minibatch stochastic gradient descent (SGD) BIBREF22 algorithm with rmsprop BIBREF23 to minimize the objective function. The initial learning rate is 0.001, batch size is 120, and INLINEFORM0 . We also apply dropout BIBREF24 to the output of all the LSTM layers. The dropout rate is 0.05. All these hyper-parameters are determined empirically via grid search on validation set." + ], + [ + "In order to train and evaluate open-domain factoid QA system for real-world questions, we build a new Chinese QA dataset named as WebQA. The dataset consists of tuples of (question, evidences, answer), which is similar to example in Figure FIGREF3 . All the questions, evidences and answers are collected from web. Table TABREF20 shows some statistics of the dataset.", + "The questions and answers are mainly collected from a large community QA website Baidu Zhidao and a small portion are from hand collected web documents. Therefore, all these questions are indeed asked by real-world users in daily life instead of under controlled conditions. All the questions are of single-entity factoid type, which means (1) each question is a factoid question and (2) its answer involves only one entity (but may have multiple words). The question in Figure FIGREF3 is a positive example, while the question \u201cWho are the children of Albert Enistein?\u201d is a counter example because the answer involves three persons. The type and correctness of all the question answer pairs are verified by at least two annotators.", + "All the evidences are retrieved from Internet by using a search engine with questions as queries. We download web pages returned in the first 3 result pages and take all the text pieces which have no more than 5 sentences and include at least one question word as candidate evidences. As evidence retrieval is beyond the scope of this work, we simply use TF-IDF values to re-rank these candidates.", + "For each question in the training set, we provide the top 10 ranked evidences to annotate (\u201cAnnotated Evidence\u201d in Table TABREF20 ). An evidence is annotated as positive if the question can be answered by just reading the evidence without any other prior knowledge, otherwise negative. Only evidences whose annotations are agreed by at least two annotators are retained. We also provide trivial negative evidences (\u201cRetrieved Evidence\u201d in Table TABREF20 ), i.e. evidences that do not contain golden standard answers.", + "For each question in the validation and test sets, we provide one major positive evidence, and maybe an additional positive one to compute features. Both of them are annotated. Raw retrieved evidences are also provided for evaluation purpose (\u201cRetrieved Evidence\u201d in Table TABREF20 ).", + "The dataset will be released on the project page http://idl.baidu.com/WebQA.html." + ], + [ + "We compare our model with two sets of baselines:", + "MemN2N BIBREF12 is an end-to-end trainable version of memory networks BIBREF9 . It encodes question and evidence with a bag-of-word method and stores the representations of evidences in an external memory. A recurrent attention model is used to retrieve relevant information from the memory to answer the question.", + "Attentive and Impatient Readers BIBREF6 use bidirectional LSTMs to encode question and evidence, and do classification over a large vocabulary based on these two encodings. The simpler Attentive Reader uses a similar way as our work to compute attention for the evidence. And the more complex Impatient Reader computes attention after processing each question word.", + "The key difference between our model and the two readers is that they produce answer by doing classification over a large vocabulary, which is computationally expensive and has difficulties in handling unseen words. However, as our model uses an end-to-end trainable sequence labeling technique, it avoids both of the two problems by its nature." + ], + [ + "The performance is measured with precision (P), recall (R) and F1-measure (F1) DISPLAYFORM0 ", + "where INLINEFORM0 is the list of correctly answered questions, INLINEFORM1 is the list of produced answers, and INLINEFORM2 is the list of all questions .", + "As WebQA is collected from web, the same answer may be expressed in different surface forms in the golden standard answer and the evidence, e.g. \u201c\u5317\u4eac (Beijing)\u201d v.s. \u201c\u5317\u4eac\u5e02 (Beijing province)\u201d. Therefore, we use two ways to count correctly answered questions, which are referred to as \u201cstrict\u201d and \u201cfuzzy\u201d in the tables:", + "Strict matching: A question is counted if and only if the produced answer is identical to the golden standard answer;", + "Fuzzy matching: A question is counted if and only if the produced answer is a synonym of the golden standard answer;", + "And we also consider two evaluation settings:", + "Annotated evidence: Each question has one major annotated evidence and maybe another annotated evidence for computing q-e.comm and e-e.comm features (Section SECREF14 );", + "Retrieved evidence: Each question is provided with at most 20 automatically retrieved evidences (see Section SECREF5 for details). All the evidences will be processed by our model independently and answers are voted by frequency to decide the final result. Note that a large amount of the evidences are negative and our model should not produce any answer for them." + ], + [ + "If not specified, the following hyper-parameters will be used in the reset of this section: LSTM layer width INLINEFORM0 (Section SECREF7 ), word embedding dimension INLINEFORM1 (Section SECREF9 ), feature embedding dimension INLINEFORM2 (Section SECREF9 ). The word embeddings are initialized with pre-trained embeddings using a 5-gram neural language model BIBREF25 and is fixed during training.", + "We will show that injecting noise data is important for improving performance on retrieved evidence setting in Section SECREF37 . In the following experiments, 20% of the training evidences will be negative ones randomly selected on the fly, of which 25% are annotated negative evidences and 75% are retrieved trivial negative evidences (Section SECREF5 ). The percentages are determined empirically. Intuitively, we provide the noise data to teach the model learning to recognize unreliable evidence.", + "For each evidence, we will randomly sample another evidence from the rest evidences of the question and compare them to compute the e-e.comm feature (Section SECREF14 ). We will develop more powerful models to process multiple evidences in a more principle way in the future.", + "As the answer for each question in our WebQA dataset only involves one entity (Section SECREF5 ), we distinguish label Os before and after the first B in the label sequence explicitly to discourage our model to produce multiple answers for a question. For example, the golden labels for the example evidence in Figure FIGREF3 will became \u201cEinstein/O1 married/O1 his/O1 first/O1 wife/O1 Mileva/B Mari\u0107/I in/O2 1903/O2\u201d, where we use \u201cO1\u201d and \u201cO2\u201d to denote label Os before and after the first B . \u201cFuzzy matching\u201d is also used for computing golden standard labels for training set.", + "For each setting, we will run three trials with different random seeds and report the average performance in the following sections." + ], + [ + "As the baselines can only predict one-word answers, we only do experiments on the one-word answer subset of WebQA, i.e. only questions with one-word answers are retained for training, validation and test. As shown in Table TABREF23 , our model achieves significant higher F1 scores than all the baselines.", + "The main reason for the relative low performance of MemN2N is that it uses a bag-of-word method to encode question and evidence such that higher order information like word order is absent to the model. We think its performance can be improved by designing more complex encoding methods BIBREF26 and leave it as a future work.", + "The Attentive and Impatient Readers only have access to the fixed length representations when doing classification. However, our model has access to the outputs of all the time steps of the evidence LSTMs, and scores the label sequence as a whole. Therefore, our model achieves better performance." + ], + [ + "In this section, we evaluate our model on the entire WebQA dataset. The evaluation results are shown in Table TABREF24 . Although producing multi-word answers is harder, our model achieves comparable results with the one-word answer subset (Table TABREF23 ), demonstrating that our model is effective for both single-word and multi-word word settings.", + "\u201cSoftmax\u201d in Table TABREF24 means we replace CRF with INLINEFORM0 , i.e. replace Eq. ( EQREF19 ) with DISPLAYFORM0 ", + "CRF outperforms INLINEFORM0 significantly in all cases. The reason is that INLINEFORM1 predicts each label independently, suggesting that modeling label transition explicitly is essential for improving performance. A natural choice for modeling label transition in INLINEFORM2 is to take the last prediction into account as in BIBREF27 . The result is shown in Table TABREF24 as \u201cSoftmax( INLINEFORM3 -1)\u201d. However, its performance is only comparable with \u201cSoftmax\u201d and significantly lower than CRF. The reason is that we can enumerate all possible label sequences implicitly by dynamic programming for CRF during predicting but this is not possible for \u201cSoftmax( INLINEFORM4 -1)\u201d , which indicates CRF is a better choice.", + "\u201cNoise\u201d in Table TABREF24 means whether we inject noise data or not (Section SECREF34 ). As all evidences are positive under the annotated evidence setting, the ability for recognizing unreliable evidence will be useless. Therefore, the performance of our model with and without noise is comparable under the annotated evidence setting. However, the ability is important to improve the performance under the retrieved evidence setting because a large amount of the retrieved evidences are negative ones. As a result, we observe significant improvement by injecting noise data for this setting." + ], + [ + "As stated in Section SECREF34 , the word embedding INLINEFORM0 is initialized with LM embedding and kept fixed in training. We evaluate different initialization and optimization methods in this section. The evaluation results are shown in Table TABREF40 . The second row shows the results when the embedding is optimized jointly during training. The performance drops significantly. Detailed analysis reveals that the trainable embedding enlarge trainable parameter number and the model gets over fitting easily. The model acts like a context independent entity tagger to some extend, which is not desired. For example, the model will try to find any location name in the evidence when the word \u201c\u5728\u54ea (where)\u201d occurs in the question. In contrary, pre-trained fixed embedding forces the model to pay more attention to the latent syntactic regularities. And it also carries basic priors such as \u201c\u68a8 (pear)\u201d is fruit and \u201c\u674e\u4e16\u77f3 (Lee Sedol)\u201d is a person, thus the model will generalize better to test data with fixed embedding. The third row shows the result when the embedding is randomly initialized and jointly optimized. The performance drops significantly further, suggesting that pre-trained embedding indeed carries meaningful priors." + ], + [ + "As shown in Table TABREF41 , both the q-e.comm and e-e.comm features are effective, and the q-e.comm feature contributes more to the overall performance. The reason is that the interaction between question and evidence is limited and q-e.comm feature with value 1, i.e. the corresponding word also occurs in the question, is a strong indication that the word may not be part of the answer." + ], + [ + "In this section, we compare the single-time attention method for computing INLINEFORM0 ( INLINEFORM1 , Eq. ( EQREF12 , EQREF13 )) with two widely used options: element-wise max operation INLINEFORM2 : INLINEFORM3 and element-wise average operation INLINEFORM4 : INLINEFORM5 . Intuitively, INLINEFORM6 can distill information in a more flexible way from { INLINEFORM7 }, while INLINEFORM8 tends to hide the differences between them, and INLINEFORM9 lies between INLINEFORM10 and INLINEFORM11 . The results in Table TABREF41 suggest that the more flexible and selective the operation is, the better the performance is." + ], + [ + "We investigate the effect of evidence LSTMs layer number, layer width and cross layer links in this section. The results are shown in Figure TABREF46 . For fair comparison, we do not use cross layer links in Figure TABREF46 (a) (dotted lines in Figure FIGREF4 ), and highlight the results with cross layer links (layer width 64) with circle and square for retrieved and annotated evidence settings respectively. We can conclude that: (1) generally the deeper and wider the model is, the better the performance is; (2) cross layer links are effective as they make the third evidence LSTM layer see information in both directions." + ], + [ + "Our model achieves fuzzy matching F1 scores of 69.78% and 70.97% on character-based input in annotated and retrieved evidence settings respectively (Table TABREF46 ), which are only 3.72 and 3.72 points lower than the corresponding scores on word-based input respectively. The performance is promising, demonstrating that our model is robust and effective." + ], + [ + "In this work, we build a new human annotated real-world QA dataset WebQA for developing and evaluating QA system on real-world QA data. We also propose a new end-to-end recurrent sequence labeling model for QA. Experimental results show that our model outperforms baselines significantly.", + "There are several future directions we plan to pursue. First, multi-entity factoid and non-factoid QA are also interesting topics. Second, we plan to extend our model to multi-evidence cases. Finally, inspired by Residual Network BIBREF28 , we will investigate deeper and wider models in the future." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0790/instruction.md b/qasper-0790/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..19140d0949bf70ab6f23f89101ebfea87bf47c88 --- /dev/null +++ b/qasper-0790/instruction.md @@ -0,0 +1,84 @@ +Name of Paper: Gibberish Semantics: How Good is Russian Twitter in Word Semantic Similarity Task? + +Question: Which Twitter corpus was used to train the word vectors? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Goals of this paper", + "Previous work", + "Data processing", + "Acquiring data", + "Corpus preprocessing", + "Training the model", + "Experimental results", + "Properties of the data", + "Determining optimal corpus size", + "Determining optimal context size", + "Some further observations", + "Conclusion" + ], + "paragraphs": [ + [ + "Word semantic similarity task is an important part of contemporary NLP. It can be applied in many areas, like word sense disambiguation, information retrieval, information extraction and others. It has long history of improvements, starting with simple models, like bag-of-words (often weighted by TF-IDF score), continuing with more complex ones, like LSA BIBREF0 , which attempts to find \u201clatent\u201d meanings of words and phrases, and even more abstract models, like NNLM BIBREF1 . Latest results are based on neural network experience, but are far more simple: various versions of Word2Vec, Skip-gram and CBOW models BIBREF2 , which currently show the State-of-the-Art results and have proven success with morphologically complex languages like Russian BIBREF3 , BIBREF4 .", + "These are corpus-based approaches, where one computes or trains the model from a large corpus. They usually consider some word context, like in bag-of-words, where model is simple count of how often can some word be seen in context of a word being described. This model anyhow does not use semantic information. A step in semantic direction was made by LSA, which requires SVD transformation of co-occurrence matrix and produces vectors with latent, unknown structure. However, this method is rather computationally expensive, and can rarely be applied to large corpora. Distributed language model was proposed, where every word is initially assigned a random fixed-size vector. During training semantically close vectors (or close by means of context) become closer to each other; as matter of closeness the cosine similarity is usually chosen. This trick enables usage of neural networks and other machine learning techniques, which easily deal with fixed-size real vectors, instead of large and sparse co-occurrence vectors.", + "It is worth mentioning non-corpus based techniques to estimate word semantic similarity. They usually make use of knowledge databases, like WordNet, Wikipedia, Wiktionary and others BIBREF5 , BIBREF6 . It was shown that Wikipedia data can be used in graph-based methods BIBREF7 , and also in corpus-based ones. In this paper we are not focusing on non-corpus based techniques.", + "In this paper we concentrate on usage of Russian Twitter stream as training corpus for Word2Vec model in semantic similarity task, and show results comparable with current (trained on a single corpus). This research is part of molva.spb.ru project, which is a trending topic detection engine for Russian Twitter. Thus the choice of language of interest is narrowed down to only Russian, although there is strong intuition that one can achieve similar results with other languages." + ], + [ + "The primary goal of this paper is to prove usefulness of Russian Twitter stream as word semantic similarity resource. Twitter is a popular social network, or also called \"microblogging service\", which enables users to share and interact with short messages instantly and publicly (although private accounts are also available). Users all over the world generate hundreds of millions of tweets per day, all over the world, in many languages, generating enormous amount of verbal data.", + "Traditional corpora for the word semantic similarity task are News, Wikipedia, electronic libraries and others (e.g. RUSSE workshop BIBREF4 ). It was shown that type of corpus used for training affects the resulting accuracy. Twitter is not usually considered, and intuition behind this is that probably every-day language is too simple and too occasional to produce good results. On the other hand, the real-time nature of this user message stream seems promising, as it may reveal what certain word means in this given moment.", + "The other counter-argument against Twitter-as-Dataset is the policy of Twitter, which disallows publication of any dump of Twitter messages larger than 50K . However, this policy permits publication of Twitter IDs in any amount. Thus the secondary goal of this paper is to describe how to create this kind of dataset from scratch. We provide the sample of Twitter messages used, as well as set of Twitter IDs used during experiments ." + ], + [ + "Semantic similarity and relatedness task received significant amount of attention. Several \"Gold standard\" datasets were produced to facilitate the evaluation of algorithms and models, including WordSim353 BIBREF8 , RG-65 BIBREF9 for English language and others. These datasets consist of several pairs of words, where each pair receives a score from human annotators. The score represents the similarity between two words, from 0% (not similar) to 100% (identical meaning, words are synonyms). Usually these scores are filled out by a number of human annotators, for instance, 13 in case of WordSim353 . The inter-annotator agreement is measured and the mean value is put into dataset.", + "Until recent days there was no such dataset for Russian language. To mitigate this the \u201cRUSSE: The First Workshop on Russian Semantic Similarity\u201d BIBREF4 was conducted, producing RUSSE Human-Judgements evaluation dataset (we will refer to it as HJ-dataset). RUSSE dataset was constructed the following way. Firstly, datasets WordSim353, MC BIBREF10 and RG-65 were combined and translated. Then human judgements were obtained by crowdsourcing (using custom implementation). Final size of the dataset is 333 word pairs, it is available on-line.", + "The RUSSE contest was followed by paper from its organizers BIBREF4 and several participators BIBREF3 , BIBREF11 , thus filling the gap in word semantic similarity task for Russian language. In this paper we evaluate a Word2Vec model, trained on Russian Twitter corpus against RUSSE HJ-dataset, and show results comparable to top results of other RUSSE competitors." + ], + [ + "In this section we describe how we receive data from Twitter, how we filter it and how we feed it to the model." + ], + [ + "Twitter provides well-documented API, which allows to request any information about Tweets, users and their profiles, with respect to rate limits. There is special type of API, called Streaming API, that provides a real-time stream of tweets. The key difference with regular API is that connection is kept alive as long as possible, and Tweets are sent in real-time to the client. There are three endpoints of Streaming API of our interest: \u201csample\u201d, \u201cfilter\u201d and \u201cfirehose\u201d. The first one provides a sample (random subset) of the full Tweet stream. The second one allows to receive Tweets matching some search criteria: matching to one or more search keywords, produced by subset of users, or coming from certain geo location. The last one provides the full set of Tweets, although it is not available by default. In order to get Twitter \u201cfirehose\u201d one can contact Twitter, or buy this stream from third-parties.", + "In our case the simplest approach would be to use \u201csample\u201d endpoint, but it provides Tweets in all possible languages from all over the World, while we are concerned only about one language (Russian). In order to use this endpoint we implemented filtering based on language. The filter is simple: if Tweet does not contain a substring of 3 or more cyrillic symbols, it is considered non-Russian. Although this approach keeps Tweets in Mongolian, Ukrainian and other slavic languages (because they use cyrillic alphabet), the total amount of false-positives in this case is negligible. To demonstrate this we conducted simple experiment: on a random sample of 200 tweets only 5 were in a language different from Russian. In order not to rely on Twitter language detection, we chose to proceed with this method of language-based filtering.", + "However, the amount of Tweets received through \u201csample\u201d endpoint was not satisfying. This is probably because \u201csample\u201d endpoint always streams the same content to all its clients, and small portion of it comes in Russian language. In order to force mining of Tweets in Russian language, we chose \"filter\" endpoint, which requires some search query. We constructed heuristic query, containing some auxiliary words, specific to Russian language: conjunctions, pronouns, prepositions. The full list is as follows:", + "russian \u044f, \u0443, \u043a, \u0432, \u043f\u043e, \u043d\u0430, \u0442\u044b, \u043c\u044b, \u0434\u043e, \u043d\u0430, \u043e\u043d\u0430, \u043e\u043d, \u0438, \u0434\u0430.", + "We evaluated our search query on data obtained from \u201csample\u201d endpoint, and 95% of Tweets matched it. We consider this coverage as reasonable and now on use \u201cfilter\u201d endpoint with the query and language filtering described above. In this paper we work with Tweet stream acquired from 2015/07/21 till 2015/08/04. We refer to parts of the dataset by the day of acquisition: 2015/07/21, etc. Tweet IDs used in our experiments are listed on-line." + ], + [ + "Corpus-based algorithms like BoW and Word2Vec require text to be tokenized, and sometimes to be stemmed as well. It is common practice to filter out Stop-Words (e.g. BIBREF11 ), but in this work we don\u2019t use it. Morphological richness of Russian language forces us to use stemming, even though models like Word2Vec does not require it. In our experiments stemmed version performs significantly better than unstemmed, so we only report results of stemmed one. To do stemming we use Yandex Tomita Parser , which is an extractor of simple facts from text in Russian language. It is based on Yandex stemmer mystem BIBREF12 . It requires a set of grammar rules and facts (i.e. simple data structures) to be extracted. In this paper we use it with one simple rule:", + "S -> Word interp (SimpleFact.Word);", + "This rule tells parser to interpret each word it sees and return it back immediately. We use Tomita Parser as we find it more user-friendly than mystem. Tomita Parser performs following operations: sentence splitting, tokenization, stemming, removing punctuation marks, transforming words to lowercase. Each Tweet is transformed into one or several lines of tab-separated sequences of words (if there are several sentences or lines in a Tweet). Twitter-specific \u201cHashtags\u201d and \u201cUser mentions\u201d are treated by Tomita Parser as normal words, except that \u201c@\u201d and \u201c#\u201d symbols are stripped off.", + "HJ-dataset contains non-lemmatized words. This is understandable, because the task of this dataset was oriented to human annotators. In several cases plural form is used (consider this pair: \"russian\u0442\u0438\u0433\u0440, russian\u043a\u043e\u0448\u0430\u0447\u044c\u0438\"). In order to compute similarity for those pairs, and having in mind that Twitter data is pre-stemmed, we have to stem HJ-dataset with same parser as well." + ], + [ + "We use Word2Vec to obtain word vectors from Twitter corpus. In this model word vectors are initialized randomly for each unique word and are fed to a sort of neural network. Authors of Word2Vec propose two different models: Skip-gram and CBOW. The first one is trained to predict the context of the word given just the word vector itself. The second one is somewhat opposite: it is trained to predict the word vector given its context. In our study CBOW always performs worse than Skip-gram, hence we describe only results with Skip-gram model. Those models have several training parameters, namely: vector size, size of vocabulary (or minimal frequency of a word), context size, threshold of downsampling, amount of training epochs. We choose vector size based on size of corpus. We use \u201ccontext size\u201d as \u201cnumber of tokens before or after current token\u201d. In all experiments presented in this paper we use one training epoch.", + "There are several implementations of Word2Vec available, including original C utility and a Python library gensim. We use the latter one as we find it more convenient. Output of Tomita Parser is fed directly line-by-line to the model. It produces the set of vectors, which we then query to obtain similarity between word vectors, in order to compute the correlation with HJ-dataset. To compute correlation we use Spearman coefficient, since it was used as accuracy measure in RUSSE BIBREF4 ." + ], + [ + "In this section we describe properties of data obtained from Twitter, describe experiment protocols and results." + ], + [ + "In order to train Word2Vec model for semantic similarity task we collected Twitter messages for 15 full days, from 2015/07/21 till 2015/08/04. Each day contains on average 3M of Tweets and 40M of tokens. All properties measured are shown in Table 1. Our first observation was that given one day of Twitter data we cannot estimate all of the words from HJ-dataset, because they appear too rarely. We fixed the frequency threshold on value of 40 occurrences per day and counted how many words from HJ-dataset are below this threshold.", + "Our second observation was that words \"missing\" from HJ-dataset are different from day to day. This is not very surprising having in mind the dynamic nature of Twitter data. Thus estimation of word vectors is different from day to day. In order to estimate the fluctuation of this semantic measure, we conduct training of Word2Vec on each day in our corpus. We fix vector size to 300, context size to 5, downsampling threshold to 1e-3, and minimal word occurrence threshold (also called min-freq) to 40. The results are shown in Table 2. Mean Spearman correlation between daily Twitter splits and HJ-dataset is 0.36 with std.dev. of 0.04. Word pairs for missing words (infrequent ones) were excluded. We also create superset of all infrequent words, i.e. words having frequency below 40 in at least one daily split. This set contains 50 words and produces 76 \"infrequent word\" pairs (out of 333). Every pair containing at least one infrequent word was excluded. On that subset of HJ-dataset mean correlation is 0.29 with std.dev. of 0.03. We consider this to be reasonably stable result." + ], + [ + "Word2Vec model was designed to be trained on large corpora. There are results of training it in reasonable time with corpus size of 1 billion of tokens BIBREF2 . It was mentioned that accuracy of estimated word vectors improves with size of corpus. Twitter provides an enormous amount of data, thus it is a perfect job for Word2Vec. We fix parameters for the model with following values: vector size of 300, min-freq of 40, context size of 5 and downsampling of 1e-3. We train our model subsequently with 1, 7 and 15 days of Twitter data (each starting with 07/21 and followed by subsequent days) . The largest corpus of 15 days contains 580M tokens. Results of training are shown in Table 3. In this experiment the best result belongs to 7-day corpus with 0.56 correlation with HJ-dataset, and 15-day corpus has a little less, 0.55. This can be explained by following: in order to achieve better results with Word2Vec one should increase both corpus and vector sizes. Indeed, training model with vector size of 600 on full Twitter corpus (15 days) shows the best result of 0.59. It is also worth noting that number of \"missing\" pairs is negligible in 7-days corpus: the only missing word (and pair) is \"russian\u0439\u0435\u043b\u044c\", Yale, the name of university in the USA. There are no \"missing\" words in 15-days corpus.", + "Training the model on 15-days corpus took 8 hours on our machine with 2 cores and 4Gb of RAM. We have an intuition that further improvements are possible with larger corpus. Comparing our results to ones reported by RUSSE participants, we conclude that our best result of 0.598 is comparable to other results, as it (virtually) encloses the top-10 of results. However, best submission of RUSSE has huge gap in accuracy of 0.16, compared to our Twitter corpus. Having in mind that best results in RUSSE combine several corpora, it is reasonable to compare Twitter results to other single-corpus results. For convenience we replicate results for these corpora, originally presented in BIBREF4 , alongside with our result in Table 5. Given these considerations we conclude that with size of Twitter corpus of 500M one can achieve reasonably good results on task of word semantic similarity." + ], + [ + "Authors of Word2Vec BIBREF2 and Paragraph Vector BIBREF13 advise to determine the optimal context size for each distinct training session. In our Twitter corpus average length of the sentence appears to be 9.8 with std.dev. of 4.9; it means that most of sentences have less than 20 tokens. This is one of peculiarities of Twitter data: Tweets are limited in size, hence sentences are short. Context size greater than 10 is redundant. We choose to train word vectors with 3 different context size values: 2, 5, 10. We make two rounds of training: first one, with Twitter data from days from 07/21 till 07/25, and second, from 07/26 till 07/30. Results of measuring correlation with HJ-dataset are shown in Table 4. According to these results context size of 5 is slightly better than others, but the difference is negligible compared to fluctuation between several attempts of training." + ], + [ + "Vector space model is capable to give more information than just measure of semantic distance of two given words. It was shown that word vectors can have multiple degrees of similarity. In particular, it is possible to model simple relations, like \"country\"-\"capital city\", gender, syntactic relations with algebraic operations over these vectors. Authors of BIBREF2 propose to assess quality of these vectors on task of exact prediction of these word relations. However, word vectors learned from Twitter seem to perform poorly on this task. We don\u2019t make systematic research on this subject here because it goes outside of the scope of the current paper, though it is an important direction of future studies.", + "Twitter post often contains three special types of words: user mentions, hashtags and hyperlinks. It can be beneficial to filter them (consider as Stop-Words). In results presented in this paper, and in particular in Tables 3 and 4, we don\u2019t filter such words. It is highly controversial if one should remove hashtags from analysis since they are often valid words or multiwords. It can also be beneficial, in some tasks, to estimate word vectors for a username. Hyperlinks in Twitter posts are mandatory shortened. It is not clear how to treat them: filter out completely, keep them or even un-short them. However, some of our experiments show that filtering of \"User Mentions\" and hyperlinks can improve accuracy on the word semantic relatedness task by 3-5%." + ], + [ + "In this paper we investigated the use of Twitter corpus for training Word2Vec model for task of word semantic similarity. We described a method to obtain stream of Twitter messages and prepare them for training. We use HJ-dataset, which was created for RUSSE contest BIBREF4 to measure correlation between similarity of word vectors and human judgements on word pairs similarity. We achieve results comparable with results obtained while training Word2Vec on traditional corpora, like Wikipedia and Web pages BIBREF3 , BIBREF11 . This is especially important because Twitter data is highly dynamic, and traditional sources are mostly static (rarely change over time). Thus verbal data acquired from Twitter may be used to estimate word vectors for neologisms, or determine other changes in word semantic, as soon as they appear in human speech." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0791/instruction.md b/qasper-0791/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..995ae29221e420c9c2afd59079fd2014532a3890 --- /dev/null +++ b/qasper-0791/instruction.md @@ -0,0 +1,180 @@ +Name of Paper: A New Corpus for Low-Resourced Sindhi Language with Word Embeddings + +Question: How does proposed word embeddings compare to Sindhi fastText word representations? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related work", + "Methodology", + "Methodology ::: Task description", + "Methodology ::: Corpus acquisition", + "Methodology ::: Preprocessing", + "Methodology ::: Word embedding models", + "Methodology ::: GloVe", + "Methodology ::: Continuous bag-of-words", + "Methodology ::: Skip gram", + "Methodology ::: Hyperparameters ::: Sub-sampling", + "Methodology ::: Hyperparameters ::: Dynamic context window", + "Methodology ::: Hyperparameters ::: Sub-word model", + "Methodology ::: Hyperparameters ::: Position-dependent weights", + "Methodology ::: Hyperparameters ::: Shifted point-wise mutual information", + "Methodology ::: Hyperparameters ::: Deleting rare words", + "Methodology ::: Evaluation methods", + "Methodology ::: Evaluation methods ::: Cosine similarity", + "Methodology ::: Evaluation methods ::: WordSim353", + "Statistical analysis of corpus", + "Statistical analysis of corpus ::: Letter occurrences", + "Statistical analysis of corpus ::: Letter n-grams frequency", + "Statistical analysis of corpus ::: Word Frequencies", + "Statistical analysis of corpus ::: Stop words", + "Experiments and results", + "Experiments and results ::: Hyperparameter optimization", + "Word similarity comparison of Word Embeddings ::: Nearest neighboring words", + "Word similarity comparison of Word Embeddings ::: Word pair relationship", + "Word similarity comparison of Word Embeddings ::: Comparison with WordSim353", + "Word similarity comparison of Word Embeddings ::: Visualization", + "Discussion and future work", + "Conclusion" + ], + "paragraphs": [ + [ + "Sindhi is a rich morphological, mutltiscript, and multidilectal language. It belongs to the Indo-Aryan language family BIBREF0, with significant cultural and historical background. Presently, it is recognized as is an official language BIBREF1 in Sindh province of Pakistan, also being taught as a compulsory subject in Schools and colleges. Sindhi is also recognized as one of the national languages in India. Ulhasnagar, Rajasthan, Gujarat, and Maharashtra are the largest Indian regions of Sindhi native speakers. It is also spoken in other countries except for Pakistan and India, where native Sindhi speakers have migrated, such as America, Canada, Hong Kong, British, Singapore, Tanzania, Philippines, Kenya, Uganda, and South, and East Africa. Sindhi has rich morphological structure BIBREF2 due to a large number of homogeneous words. Historically, it was written in multiple writing systems, which differ from each other in terms of orthography and morphology. The Persian-Arabic is the standard script of Sindhi, which was officially accepted in 1852 by the British government. However, the Sindhi-Devanagari is also a popular writing system in India being written in left to right direction like the Hindi language. Formerly, Khudabadi, Gujrati, Landa, Khojki, and Gurumukhi were also adopted as its writing systems. Even though, Sindhi has great historical and literal background, presently spoken by nearly 75 million people BIBREF1. The research on SNLP was coined in 2002, however, IT grabbed research attention after the development of its Unicode system BIBREF3. But still, Sindhi stands among the low-resourced languages due to the scarcity of core language processing resources of the raw and annotated corpus, which can be utilized for training robust word embeddings or the use of machine learning algorithms. Since the development of annotated datasets requires time and human resources.", + "The Language Resources (LRs) are fundamental elements for the development of high quality NLP systems based on automatic or NN based approaches. The LRs include written or spoken corpora, lexicons, and annotated corpora for specific computational purposes. The development of such resources has received great research interest for the digitization of human languages BIBREF4. Many world languages are rich in such language processing resources integrated in their software tools including English BIBREF5 BIBREF6, Chinese BIBREF7 and other languages BIBREF8 BIBREF9. The Sindhi language lacks the basic computational resources BIBREF10 of a large text corpus, which can be utilized for training robust word embeddings and developing language independent NLP applications including semantic analysis, sentiment analysis, parts of the speech tagging, named entity recognition, machine translation BIBREF11, multitasking BIBREF12, BIBREF13. Presently Sindhi Persian-Arabic is frequently used for online communication, newspapers, public institutions in Pakistan, and India BIBREF1. But little work has been carried out for the development of LRs such as raw corpus BIBREF14, BIBREF15, annotated corpus BIBREF16, BIBREF17, BIBREF1, BIBREF18. In the best of our knowledge, Sindhi lacks the large unlabelled corpus which can be utilized for generating and evaluating word embeddings for Statistical Sindhi Language Processing (SSLP).", + "One way to to break out this loop is to learn word embeddings from unlabelled corpora, which can be utilized to bootstrap other downstream NLP tasks. The word embedding is a new term of semantic vector space BIBREF19, distributed representations BIBREF20, and distributed semantic models. It is a language modeling approach BIBREF21 used for the mapping of words and phrases into $n$-dimensional dense vectors of real numbers that effectively capture the semantic and syntactic relationship with neighboring words in a geometric way BIBREF22 BIBREF23. Such as \u201cEinstein\u201d and \u201cScientist\u201d would have greater similarity compared with \u201cEinstein\u201d and \u201cdoctor.\u201d In this way, word embeddings accomplish the important linguistic concept of \u201ca word is characterized by the company it keeps\". More recently NN based models yield state-of-the-art performance in multiple NLP tasks BIBREF24 BIBREF25 with the word embeddings. One of the advantages of such techniques is they use unsupervised approaches for learning representations and do not require annotated corpus which is rare for low-resourced Sindhi language. Such representions can be trained on large unannotated corpora, and then generated representations can be used in the NLP tasks which uses a small amount of labelled data.", + "In this paper, we address the problems of corpus construction by collecting a large corpus of more than 61 million words from multiple web resources using the web-scrappy framework. After the collection of the corpus, we carefully preprocessed for the filtration of noisy text, e.g., the HTML tags and vocabulary of the English language. The statistical analysis is also presented for the letter, word frequencies and identification of stop-words. Finally, the corpus is utilized to generate Sindhi word embeddings using state-of-the-art GloVe BIBREF26 SG and CBoW BIBREF27 BIBREF20 BIBREF24 algorithms. The popular intrinsic evaluation method BIBREF20 BIBREF28 BIBREF29 of calculating cosine similarity between word vectors and WordSim353 BIBREF30 are employed to measure the performance of the learned Sindhi word embeddings. We translated English WordSim353 word pairs into Sindhi using bilingual English to Sindhi dictionary. The intrinsic approach typically involves a pre-selected set of query terms BIBREF23 and semantically related target words, which we refer to as query words. Furthermore, we also compare the proposed word embeddings with recently revealed Sindhi fastText (SdfastText) BIBREF25 word representations. To the best of our knowledge, this is the first comprehensive work on the development of large corpus and generating word embeddings along with systematic evaluation for low-resourced Sindhi Persian-Arabic. The synopsis of our novel contributions is listed as follows:", + "We present a large corpus of more than 61 million words obtained from multiple web resources and reveal a list of Sindhi stop words.", + "We develop a text cleaning pipeline for the preprocessing of the raw corpus.", + "Generate word embeddings using GloVe, CBoW, and SG Word2Vec algorithms also evaluate and compare them using the intrinsic evaluation approaches of cosine similarity matrix and WordSim353.", + "We are the first to evaluate SdfastText word representations and compare them with our proposed Sindhi word embeddings.", + "The remaining sections of the paper are organized as; Section SECREF2 presents the literature survey regarding computational resources, Sindhi corpus construction, and word embedding models. Afterwards, Section SECREF3 presents the employed methodology, Section SECREF4 consist of statistical analysis of the developed corpus. Section SECREF5 present the experimental setup. The intrinsic evaluation results along with comparison are given in Section SECREF6. The discussion and future work are given in Section SECREF7, and lastly, Section SECREF8 presents the conclusion." + ], + [ + "The natural language resources refer to a set of language data and descriptions BIBREF31 in machine readable form, used for building, improving, and evaluating NLP algorithms or softwares. Such resources include written or spoken corpora, lexicons, and annotated corpora for specific computational purposes. Many world languages are rich in such language processing resources integrated in the software tools including NLTK for English BIBREF5, Stanford CoreNLP BIBREF6, LTP for Chinese BIBREF7, TectoMT for German, Russian, Arabic BIBREF8 and multilingual toolkit BIBREF9. But Sindhi language is at an early stage for the development of such resources and software tools.", + "The corpus construction for NLP mainly involves important steps of acquisition, preprocessing, and tokenization. Initially, BIBREF14 discussed the morphological structure and challenges concerned with the corpus development along with orthographical and morphological features in the Persian-Arabic script. The raw and annotated corpus BIBREF1 for Sindhi Persian-Arabic is a good supplement towards the development of resources, including raw and annotated datasets for parts of speech tagging, morphological analysis, transliteration between Sindhi Persian-Arabic and Sindhi-Devanagari, and machine translation system. But the corpus is acquired only form Wikipedia-dumps. A survey-based study BIBREF4 provides all the progress made in the Sindhi Natural Language Processing (SNLP) with the complete gist of adopted techniques, developed tools and available resources which show that work on resource development on Sindhi needs more sophisticated efforts. The raw corpus is utilized for word segmentation BIBREF32 of Sindhi Persian-Arabic. More recently, an initiative towards the development of resources is taken BIBREF16 by open sourcing annotated dataset of Sindhi Persian-Arabic obtained from news and social blogs. The existing and proposed work is presented in Table TABREF9 on the corpus development, word segmentation, and word embeddings, respectively.", + "The power of word embeddings in NLP was empirically estimated by proposing a neural language model BIBREF21 and multitask learning BIBREF12, but recently usage of word embeddings in deep neural algorithms has become integral element BIBREF33 for performance acceleration in deep NLP applications. The CBoW and SG BIBREF27 BIBREF20 popular word2vec neural architectures yielded high quality vector representations in lower computational cost with integration of character-level learning on large corpora in terms of semantic and syntactic word similarity later extended BIBREF33 BIBREF24. Both approaches produce state-of-the-art accuracy with fast training performance, better representations of less frequent words and efficient representation of phrases as well. BIBREF34 proposed NN based approach for generating morphemic-level word embeddings, which surpassed all the existing embedding models in intrinsic evaluation. A count-based GloVe model BIBREF26 also yielded state-of-the-art results in an intrinsic evaluation and downstream NLP tasks.", + "The performance of Word embeddings is evaluated using intrinsic BIBREF23 BIBREF29 and extrinsic evaluation BIBREF28 methods. The performance of word embeddings can be measured with intrinsic and extrinsic evaluation approaches. The intrinsic approach is used to measure the internal quality of word embeddings such as querying nearest neighboring words and calculating the semantic or syntactic similarity between similar word pairs. A method of direct comparison for intrinsic evaluation of word embeddings measures the neighborhood of a query word in vector space. The key advantage of that method is to reduce bias and create insight to find data-driven relevance judgment. An extrinsic evaluation approach is used to evaluate the performance in downstream NLP tasks, such as parts-of-speech tagging or named-entity recognition BIBREF23, but the Sindhi language lacks annotated corpus for such type of evaluation. Moreover, extrinsic evaluation is time consuming and difficult to interpret. Therefore, we opt intrinsic evaluation method BIBREF28 to get a quick insight into the quality of proposed Sindhi word embeddings by measuring the cosine distance between similar words and using WordSim353 dataset. A study reveals that the choice of optimized hyper-parameters BIBREF35 has a great impact on the quality of pretrained word embeddings as compare to desing a novel algorithm. Therefore, we optimized the hyperparameters for generating robust Sindhi word embeddings using CBoW, SG and GloVe models. The embedding visualization is also useful to visualize the similarity of word clusters. Therefore, we use t-SNE BIBREF36 dimensionality reduction algorithm for compressing high dimensional embedding into 2-dimensional $x$,$y$ coordinate pairs with PCA BIBREF37. The PCA is useful to combine input features by dropping the least important features while retaining the most valuable features." + ], + [ + "This section presents the employed methodology in detail for corpus acquisition, preprocessing, statistical analysis, and generating Sindhi word embeddings." + ], + [ + "We initiate this work from scratch by collecting large corpus from multiple web resources. After preprocessing and statistical analysis of the corpus, we generate Sindhi word embeddings with state-of-the-art CBoW, SG, and GloVe algorithms. The generated word embeddings are evaluated using the intrinsic evaluation approaches of cosine similarity between nearest neighbors, word pairs, and WordSim-353 for distributional semantic similarity. Moreover, we use t-SNE with PCA for the comparison of the distance between similar words via visualization." + ], + [ + "The corpus is a collection of human language text BIBREF31 built with a specific purpose. However, the statistical analysis of the corpus provides quantitative, reusable data, and an opportunity to examine intuitions and ideas about language. Therefore, the corpus has great importance for the study of written language to examine the text. In fact, realizing the necessity of large text corpus for Sindhi, we started this research by collecting raw corpus from multiple web resource using web-scrappy framwork for extraction of news columns of daily Kawish and Awami Awaz Sindhi newspapers, Wikipedia dumps, short stories and sports news from Wichaar social blog, news from Focus Word press blog, historical writings, novels, stories, books from Sindh Salamat literary websites, novels, history and religious books from Sindhi Adabi Board and tweets regarding news and sports are collected from twitter." + ], + [ + "The preprocessing of text corpus obtained from multiple web resources is a challenging task specially it becomes more complicated when working on low-resourced language like Sindhi due to the lack of open-source preprocessing tools such as NLTK BIBREF5 for English. Therefore, we design a preprocessing pipeline depicted in Figure FIGREF22 for the filtration of unwanted data and vocabulary of other languages such as English to prepare input for word embeddings. Whereas, the involved preprocessing steps are described in detail below the Figure FIGREF22. Moreover, we reveal the list of Sindhi stop words BIBREF38 which is labor intensive and requires human judgment as well. Hence, the most frequent and least important words are classified as stop words with the help of a Sindhi linguistic expert. The partial list of Sindhi stop words is given in TABREF61. We use python programming language for designing the preprocessing pipeline using regex and string functions.", + "Input: The collected text documents were concatenated for the input in UTF-8 format.", + "Replacement symbols: The punctuation marks of a full stop, hyphen, apostrophe, comma, quotation, and exclamation marks replaced with white space for authentic tokenization because without replacing these symbols with white space the words were found joined with their next or previous corresponding words.", + "Filtration of noisy data: The text acquisition from web resources contain a huge amount of noisy data. Therefore, we filtered out unimportant data such as the rest of the punctuation marks, special characters, HTML tags, all types of numeric entities, email, and web addresses.", + "Normalization: In this step, We tokenize the corpus then normalize to lower-case for the filtration of multiple white spaces, English vocabulary, and duplicate words. The stop words were only filtered out for preparing input for GloVe. However, the sub-sampling approach in CBoW and SG can discard most frequent or stop words automatically." + ], + [ + "The NN based approaches have produced state-of-the-art performance in NLP with the usage of robust word embedings generated from the large unlabelled corpus. Therefore, word embeddings have become the main component for setting up new benchmarks in NLP using deep learning approaches. Most recently, the use cases of word embeddings are not only limited to boost statistical NLP applications but can also be used to develop language resources such as automatic construction of WordNet BIBREF39 using the unsupervised approach.", + "The word embedding can be precisely defined as the encoding of vocabulary $V$ into $N$ and the word $w$ from $V$ to vector $\\overrightarrow{w} $ into $N$-dimensional embedding space. They can be broadly categorized into predictive and count based methods, being generated by employing co-occurrence statistics, NN algorithms, and probabilistic models. The GloVe BIBREF26 algorithm treats each word as a single entity in the corpus and generates a vector of each word. However, CBoW and SG BIBREF27 BIBREF20, later extended BIBREF33 BIBREF24, well-known as word2vec rely on simple two layered NN architecture which uses linear activation function in hidden layer and softmax in the output layer. The work2vec model treats each word as a bag-of-character n-gram." + ], + [ + "The GloVe is a log-bilinear regression model BIBREF26 which combines two methods of local context window and global matrix factorization for training word embeddings of a given vocabulary in an unsupervised way. It weights the contexts using the harmonic function, for example, a context word four tokens away from an occurrence will be counted as $\\frac{1}{4}$. The Glove\u2019s implementation represents word $w \\in V_{w}$ and context $c \\in V_{c}$ in $D$-dimensional vectors $\\overrightarrow{w}$ and $\\overrightarrow{c}$ in a following way,", + "Where, $b^{\\overrightarrow{w}}$ is row vector $\\left|V_{w}\\right|$ and $b^{\\overrightarrow{c}}$ is $\\left|V_{c}\\right|$ is column vector." + ], + [ + "The standard CBoW is the inverse of SG BIBREF27 model, which predicts input word on behalf of the context. The length of input in the CBoW model depends on the setting of context window size which determines the distance to the left and right of the target word. Hence the context is a window that contain neighboring words such as by giving $w=\\left\\lbrace w_{1}, w_{2}, \\dots \\dots w_{t}\\right\\rbrace $ a sequence of words $T$, the objective of the CBoW is to maximize the probability of given neighboring words such as,", + "Where, $c_{t}$ is context of $t^{\\text{th}}$ word for example with window $w_{t-c}, \\ldots w_{t-1}, w_{t+1}, \\ldots w_{t+c}$ of size $2 c$." + ], + [ + "The SG model predicts surrounding words by giving input word BIBREF20 with training objective of learning good word embeddings that efficiently predict the neighboring words. The goal of skip-gram is to maximize average log-probability of words $w=\\left\\lbrace w_{1}, w_{2}, \\dots \\dots w_{t}\\right\\rbrace $ across the entire training corpus,", + "Where, $c_{t}$ denotes the context of words indices set of nearby $w_{t}$ words in the training corpus." + ], + [ + "Th sub-sampling BIBREF20 approach is useful to dilute most frequent or stop words, also accelerates learning rate, and increases accuracy for learning rare word vectors. Numerous words in English, e.g., \u2018the\u2019, \u2018you\u2019, \u2019that\u2019 do not have more importance, but these words appear very frequently in the text. However, considering all the words equally would also lead to over-fitting problem of model parameters BIBREF24 on the frequent word embeddings and under-fitting on the rest. Therefore, it is useful to count the imbalance between rare and repeated words. The sub-sampling technique randomly removes most frequent words with some threshold $t$ and probability $p$ of words and frequency $f$ of words in the corpus.", + "Where each word$w_{i}$ is discarded with computed probability in training phase, $f(w_i )$ is frequency of word $w_{i}$ and $t>0$ are parameters." + ], + [ + "The traditional word embedding models usually use a fixed size of a context window. For instance, if the window size ws=6, then the target word apart from 6 tokens will be treated similarity as the next word. The scheme is used to assign more weight to closer words, as closer words are generally considered to be more important to the meaning of the target word. The CBoW, SG and GloVe models employ this weighting scheme. The GloVe model weights the contexts using a harmonic function, for example, a context word four tokens away from an occurrence will be counted as $\\frac{1}{4}$. However, CBoW and SG implementation equally consider the contexts by dividing the ws with the distance from target word, e.g. ws=6 will weigh its context by $\\frac{6}{6} \\frac{5}{6} \\frac{4}{6} \\frac{3}{6} \\frac{2}{6} \\frac{1}{6}$." + ], + [ + "The sub-word model BIBREF24 can learn the internal structure of words by sharing the character representations across words. In that way, the vector for each word is made of the sum of those character $n-gram$. Such as, a vector of a word \u201ctable\u201d is a sum of $n-gram$ vectors by setting the letter $n-gram$ size $min=3$ to $max=6$ as, $, abl, able, able>, ble, ble>, le>$, we can get all sub-words of \"table\" with minimum length of $minn=3$ and maximum length of $maxn=6$. The $<$ and $>$ symbols are used to separate prefix and suffix words from other character sequences. In this way, the sub-word model utilizes the principles of morphology, which improves the quality of infrequent word representations. In addition to character $n-grams$, the input word $w$ is also included in the set of character $n-gram$, to learn the representation of each word. We obtain scoring function using a input dictionary of $n-grams$ with size $K$ by giving word $w$ , where $K_{w} \\subset \\lbrace 1, \\ldots , K\\rbrace $. A word representation $Z_{k}$ is associated to each $n-gram$ $Z$. Hence, each word is represented by the sum of character $n-gram$ representations, where, $s$ is the scoring function in the following equation," + ], + [ + "The position-dependent weighting approach BIBREF40 is used to avoid direct encoding of representations for words and their positions which can lead to over-fitting problem. The approach learns positional representations in contextual word representations and used to reweight word embedding. Thus, it captures good contextual representations at lower computational cost,", + "Where, $p$ is individual position in context window associated with $d_{p}$ vector. Afterwards the context vector reweighted by their positional vectors is average of context words. The relative positional set is $P$ in context window and $v_{C}$ is context vector of $w_{t}$ respectively." + ], + [ + "The use sparse Shifted Positive Point-wise Mutual Information (SPPMI) BIBREF41 word-context matrix in learning word representations improves results on two word similarity tasks. The CBoW and SG have $k$ (number of negatives) BIBREF27 BIBREF20 hyperparameter, which affects the value that both models try to optimize for each $(w, c): P M I(w, c)-\\log k$. Parameter $k$ has two functions of better estimation of negative examples, and it performs as before observing the probability of positive examples (actual occurrence of $w,c$)." + ], + [ + "Before creating a context window, the automatic deletion of rare words also leads to performance gain in CBoW, SG and GloVe models, which further increases the actual size of context windows." + ], + [ + "The intrinsic evaluation is based on semantic similarity BIBREF23 in word embeddings. The word similarity measure approach states BIBREF35 that the words are similar if they appear in the similar context. We measure word similarity of proposed Sindhi word embeddings using dot product method and WordSim353." + ], + [ + "The cosine similarity between two non-zero vectors is a popular measure that calculates the cosine of the angle between them which can be derived by using the Euclidean dot product method. The dot product is a multiplication of each component from both vectors added together. The result of a dot product between two vectors isn\u2019t another vector but a single value or a scalar. The dot product for two vectors can be defined as: $\\overrightarrow{a}=\\left(a_{1}, a_{2}, a_{3}, \\dots , a_{n}\\right)$ and $\\overrightarrow{b}=\\left({b}_{1}, {b}_{2}, {b}_{3}, \\ldots , {b}_{n}\\right)$ where $a_{n}$ and $b_{n}$ are the components of the vector and $n$ is dimension of vectors such as,", + "However, the cosine of two non-zero vectors can be derived by using the Euclidean dot product formula,", + "Given $a_{i}$ two vectors of attributes $a$ and $b$, the cosine similarity, $\\cos ({\\theta })$, is represented using a dot product and magnitude as,", + "where $a_{i}$ and $b_{i}$ are components of vector $\\overrightarrow{a}$ and $\\overrightarrow{b}$, respectively." + ], + [ + "The WordSim353 BIBREF42 is popular for the evaluation of lexical similarity and relatedness. The similarity score is assigned with 13 to 16 human subjects with semantic relations BIBREF30 for 353 English noun pairs. Due to the lack of annotated datasets in the Sindhi language, we translated WordSim353 using English to Sindhi bilingual dictionary for the evaluation of our proposed Sindhi word embeddings and SdfastText. We use the Spearman correlation coefficient for the semantic and syntactic similarity comparison which is used to used to discover the strength of linear or nonlinear relationships if there are no repeated data values. A perfect Spearman\u2019s correlation of $+1$ or $-1$ discovers the strength of a link between two sets of data (word-pairs) when observations are monotonically increasing or decreasing functions of each other in a following way,", + "where $r_s$ is the rank correlation coefficient, $n$ denote the number of observations, and $d^i$ is the rank difference between $i^{th}$ observations." + ], + [ + "The large corpus acquired from multiple resources is rich in vocabulary. We present the complete statistics of collected corpus (see Table TABREF52) with number of sentences, words and unique tokens." + ], + [ + "The frequency of letter occurrences in human language is not arbitrarily organized but follow some specific rules which enable us to describe some linguistic regularities. The Zipf\u2019s law BIBREF43 suggests that if the frequency of letter or word occurrence ranked in descending order such as,", + "Where, $F_{r}$ is the letter frequency of rth rank, $a$ and $b$ are parameters of input text. The comparative letter frequency in the corpus is the total number of occurrences of a letter divided by the total number of letters present in the corpus. The letter frequencies in our developed corpus are depicted in Figure FIGREF55; however, the corpus contains 187,620,276 total number of the character set. Sindhi Persian-Arabic alphabet consists of 52 letters but in the vocabulary 59 letters are detected, additional seven letters are modified uni-grams and standalone honorific symbols." + ], + [ + "We denote the combination of letter occurrences in a word as n-grams, where each letter is a gram in a word. The letter n-gram frequency is carefully analyzed in order to find the length of words which is essential to develop NLP systems, including learning of word embeddings such as choosing the minimum or maximum length of sub-word for character-level representation learning BIBREF24. We calculate the letter n-grams in words along with their percentage in the developed corpus (see Table TABREF57). The bi-gram words are most frequent, mostly consists of stop words and secondly, 4-gram words have a higher frequency." + ], + [ + "The word frequency count is an observation of word occurrences in the text. The commonly used words are considered to be with higher frequency, such as the word \u201cthe\" in English. Similarly, the frequency of rarely used words to be lower. Such frequencies can be calculated at character or word-level. We calculate word frequencies by counting a word $w$ occurrence in the corpus $c$, such as,", + "Where the frequency of $w$ is the sum of every occurrence $k$ of $w$ in $c$." + ], + [ + "The most frequent and least important words in NLP are often classified as stop words. The removal of such words can boost the performance of the NLP model BIBREF38, such as sentiment analysis and text classification. But the construction of such words list is time consuming and requires user decisions. Firstly, we determined Sindhi stop words by counting their term frequencies using Eq. DISPLAY_FORM59, and secondly, by analysing their grammatical status with the help of Sindhi linguistic expert because all the frequent words are not stop words (see Figure FIGREF62). After determining the importance of such words with the help of human judgment, we placed them in the list of stop words. The total number of detected stop words is 340 in our developed corpus. The partial list of most frequent Sindhi stop words is depicted in Table TABREF61 along with their frequency. The filtration of stop words is an essential preprocessing step for learning GloVe BIBREF26 word embeddings; therefore, we filtered out stop words for preparing input for the GloVe model. However, the sub-sampling approach BIBREF33 BIBREF24 is used to discard such most frequent words in CBoW and SG models." + ], + [ + "Hyperparameter optimization BIBREF23is more important than designing a novel algorithm. We carefully choose to optimize the dictionary and algorithm-based parameters of CBoW, SG and GloVe algorithms. Hence, we conducted a large number of experiments for training and evaluation until the optimization of most suitable hyperparameters depicted in Table TABREF64 and discussed in Section SECREF63. The choice of optimized hyperparameters is based on The high cosine similarity score in retrieving nearest neighboring words, the semantic, syntactic similarity between word pairs, WordSim353, and visualization of the distance between twenty nearest neighbours using t-SNE respectively. All the experiments are conducted on GTX 1080-TITAN GPU." + ], + [ + "The state-of-the-art SG, CBoW BIBREF27 BIBREF33 BIBREF20 BIBREF24 and Glove BIBREF26 word embedding algorithms are evaluated by parameter tuning for development of Sindhi word embeddings. These parameters can be categories into dictionary and algorithm based, respectively. The integration of character n-gram in learning word representations is an ideal method especially for rich morphological languages because this approach has the ability to compute rare and misspelled words. Sindhi is also a rich morphological language. Therefore more robust embeddings became possible to train with the hyperparameter optimization of SG, CBoW and GloVe algorithms. We tuned and evaluated the hyperparameters of three algorithms individually which are discussed as follows:", + "Number of Epochs: Generally, more epochs on the corpus often produce better results but more epochs take long training time. Therefore, we evaluate 10, 20, 30 and 40 epochs for each word embedding model, and 40 epochs constantly produce good results.", + "Learning rate (lr): We tried lr of $0.05$, $0.1$, and $0.25$, the optimal lr $(0.25)$ gives the better results for training all the embedding models.", + "Dimensions ($D$): We evaluate and compare the quality of $100-D$, $200-D$, and $300-D$ using WordSim353 on different $ws$, and the optimal $300-D$ are evaluated with cosine similarity matrix for querying nearest neighboring words and calculating the similarity between word pairs. The embedding dimensions have little affect on the quality of the intrinsic evaluation process. However, the selection of embedding dimensions might have more impact on the accuracy in certain downstream NLP applications. The lower embedding dimensions are faster to train and evaluate.", + "Character n-grams: The selection of minimum (minn) and the maximum (maxn) length of character $n-grams$ is an important parameter for learning character-level representations of words in CBoW and SG models. Therefore, the n-grams from $3-9$ were tested to analyse the impact on the accuracy of embedding. We optimized the length of character n-grams from $minn=2$ and $maxn=7$ by keeping in view the word frequencies depicted in Table TABREF57.", + "Window size (ws): The large ws means considering more context words and similarly less ws means to limit the size of context words. By changing the size of the dynamic context window, we tried the ws of 3, 5, 7 the optimal ws=7 yield consistently better performance.", + "Negative Sampling (NS): : The more negative examples yield better results, but more negatives take long training time. We tried 10, 20, and 30 negative examples for CBoW and SG. The best negative examples of 20 for CBoW and SG significantly yield better performance in average training time.", + "Minimum word count (minw): We evaluated the range of minimum word counts from 1 to 8 and analyzed that the size of input vocabulary is decreasing at a large scale by ignoring more words similarly the vocabulary size was increasing by considering rare words. Therefore, by ignoring words with a frequency of less than 4 in CBoW, SG, and GloVe consistently yields better results with the vocabulary of 200,000 words.", + "Loss function (ls): we use hierarchical softmax (hs) for CBoW, negative sampling (ns) for SG and default loss function for GloVe BIBREF26.", + "The recommended verbosity level, number of buckets, sampling threshold, number of threads are used for training CBoW, SG BIBREF24, and GloVe BIBREF26." + ], + [ + "The cosine similarity matrix BIBREF35 is a popular approach to compute the relationship between all embedding dimensions of their distinct relevance to query word. The words with similar context get high cosine similarity and geometrical relatedness to Euclidean distance, which is a common and primary method to measure the distance between a set of words and nearest neighbors. Each word contains the most similar top eight nearest neighboring words determined by the highest cosine similarity score using Eq. DISPLAY_FORM48. We present the English translation of both query and retrieved words also discuss with their English meaning for ease of relevance judgment between the query and retrieved words.To take a closer look at the semantic and syntactic relationship captured in the proposed word embeddings, Table TABREF74 shows the top eight nearest neighboring words of five different query words Friday, Spring, Cricket, Red, Scientist taken from the vocabulary. As the first query word Friday returns the names of days Saturday, Sunday, Monday, Tuesday, Wednesday, Thursday in an unordered sequence. The SdfastText returns five names of days Sunday, Thursday, Monday, Tuesday and Wednesday respectively. The GloVe model also returns five names of days. However, CBoW and SG gave six names of days except Wednesday along with different writing forms of query word Friday being written in the Sindhi language which shows that CBoW and SG return more relevant words as compare to SdfastText and GloVe. The CBoW returned Add and GloVe returns Honorary words which are little similar to the querry word but SdfastText resulted two irrelevant words Kameeso (N) which is a name (N) of person in Sindhi and Phrase is a combination of three Sindhi words which are not tokenized properly. Similarly, nearest neighbors of second query word Spring are retrieved accurately as names and seasons and semantically related to query word Spring by CBoW, SG and Glove but SdfastText returned four irrelevant words of Dilbahar (N), Pharase, Ashbahar (N) and Farzana (N) out of eight. The third query word is Cricket, the name of a popular game. The first retrieved word in CBoW is Kabadi (N) that is a popular national game in Pakistan. Including Kabadi (N) all the returned words by CBoW, SG and GloVe are related to Cricket game or names of other games. But the first word in SdfastText contains a punctuation mark in retrieved word Gone.Cricket that are two words joined with a punctuation mark (.), which shows the tokenization error in preprocessing step, sixth retrieved word Misspelled is a combination of three words not related to query word, and Played, Being played are also irrelevant and stop words. Moreover, fourth query word Red gave results that contain names of closely related to query word and different forms of query word written in the Sindhi language. The last returned word Unknown by SdfastText is irrelevant and not found in the Sindhi dictionary for translation. The last query word Scientist also contains semantically related words by CBoW, SG, and GloVe, but the first Urdu word given by SdfasText belongs to the Urdu language which means that the vocabulary may also contain words of other languages. Another unknown word returned by SdfastText does not have any meaning in the Sindhi dictionary. More interesting observations in the presented results are the diacritized words retrieved from our proposed word embeddings and The authentic tokenization in the preprocessing step presented in Figure FIGREF22. However, SdfastText has returned tri-gram words of Phrase in query words Friday, Spring, a Misspelled word in Cricket and Scientist query words. Hence, the overall performance of our proposed SG, CBoW, and GloVe demonstrate high semantic relatedness in retrieving the top eight nearest neighbor words." + ], + [ + "Generally, closer words are considered more important to a word\u2019s meaning. The word embeddings models have the ability to capture the lexical relations between words. Identifying such relationship that connects words is important in NLP applications. We measure that semantic relationship by calculating the dot product of two vectors using Eq. DISPLAY_FORM48. The high cosine similarity score denotes the closer words in the embedding matrix, while less cosine similarity score means the higher distance between word pairs. We present the cosine similarity score of different semantically or syntactically related word pairs taken from the vocabulary in Table TABREF77 along with English translation, which shows the average similarity of 0.632, 0.650, 0.591 yields by CBoW, SG and GloVe respectively. The SG model achieved a high average similarity score of 0.650 followed by CBoW with a 0.632 average similarity score. The GloVe also achieved a considerable average score of 0.591 respectively. However, the average similarity score of SdfastText is 0.388 and the word pair Microsoft-Bill Gates is not available in the vocabulary of SdfastText. This shows that along with performance, the vocabulary in SdfastText is also limited as compared to our proposed word embeddings.", + "Moreover, the average semantic relatedness similarity score between countries and their capitals is shown in Table TABREF78 with English translation, where SG also yields the best average score of 0.663 followed by CBoW with 0.611 similarity score. The GloVe also yields better semantic relatedness of 0.576 and the SdfastText yield an average score of 0.391. The first query word China-Beijing is not available the vocabulary of SdfastText. However, the similarity score between Afghanistan-Kabul is lower in our proposed CBoW, SG, GloVe models because the word Kabul is the name of the capital of Afghanistan as well as it frequently appears as an adjective in Sindhi text which means able." + ], + [ + "We evaluate the performance of our proposed word embeddings using the WordSim353 dataset by translation English word pairs to Sindhi. Due to vocabulary differences between English and Sindhi, we were unable to find the authentic meaning of six terms, so we left these terms untranslated. So our final Sindhi WordSim353 consists of 347 word pairs. Table TABREF80 shows the Spearman correlation results using Eq. DISPLAY_FORM51 on different dimensional embeddings on the translated WordSim353. The Table TABREF80 presents complete results with the different ws for CBoW, SG and GloVe in which the ws=7 subsequently yield better performance than ws of 3 and 5, respectively. The SG model outperforms CBoW and GloVe in semantic and syntactic similarity by achieving the performance of 0.629 with ws=7. In comparison with English BIBREF27 achieved the average semantic and syntactic similarity of 0.637, 0.656 with CBoW and SG, respectively. Therefore, despite the challenges in translation from English to Sindhi, our proposed Sindhi word embeddings have efficiently captured the semantic and syntactic relationship." + ], + [ + "We use t-Distributed Stochastic Neighboring (t-SNE) dimensionality BIBREF36 reduction algorithm with PCA BIBREF37 for exploratory embeddings analysis in 2-dimensional map. The t-SNE is a non-linear dimensionality reduction algorithm for visualization of high dimensional datasets. It starts the probability calculation of similar word clusters in high-dimensional space and calculates the probability of similar points in the corresponding low-dimensional space. The purpose of t-SNE for visualization of word embeddings is to keep similar words close together in 2-dimensional $x,y$ coordinate pairs while maximizing the distance between dissimilar words. The t-SNE has a perplexity (PPL) tunable parameter used to balance the data points at both the local and global levels. We visualize the embeddings using PPL=20 on 5000-iterations of 300-D models. We use the same query words (see Table TABREF74) by retrieving the top 20 nearest neighboring word clusters for a better understanding of the distance between similar words. Every query word has a distinct color for the clear visualization of a similar group of words. The closer word clusters show the high similarity between the query and retrieved word clusters. The word clusters in SG (see Fig. FIGREF83) are closer to their group of semantically related words. Secondly, the CBoW model depicted in Fig. FIGREF82 and GloVe Fig. FIGREF84 also show the better cluster formation of words than SdfastText Fig. FIGREF85, respectively." + ], + [ + "In this era of the information age, the existence of LRs plays a vital role in the digital survival of natural languages because the NLP tools are used to process a flow of un-structured data from disparate sources. It is imperative to mention that presently, Sindhi Persian-Arabic is frequently used in online communication, newspapers, public institutions in Pakistan and India. Due to the growing use of Sindhi on web platforms, the need for its LRs is also increasing for the development of language technology tools. But little work has been carried out for the development of resources which is not sufficient to design a language independent or machine learning algorithms. The present work is a first comprehensive initiative on resource development along with their evaluation for statistical Sindhi language processing. More recently, the NN based approaches have produced a state-of-the-art performance in NLP by exploiting unsupervised word embeddings learned from the large unlabelled corpus. Such word embeddings have also motivated the work on low-resourced languages. Our work mainly consists of novel contributions of resource development along with comprehensive evaluation for the utilization of NN based approaches in SNLP applications. The large corpus obtained from multiple web resources is utilized for the training of word embeddings using SG, CBoW and Glove models. The intrinsic evaluation along with comparative results demonstrates that the proposed Sindhi word embeddings have accurately captured the semantic information as compare to recently revealed SdfastText word vectors. The SG yield best results in nearest neighbors, word pair relationship and semantic similarity. The performance of CBoW is also close to SG in all the evaluation matrices. The GloVe also yields better word representations; however SG and CBoW models surpass the GloVe model in all evaluation matrices. Hyperparameter optimization is as important as designing a new algorithm. The choice of optimal parameters is a key aspect of performance gain in learning robust word embeddings. Moreover, We analysed that the size of the corpus and careful preprocessing steps have a large impact on the quality of word embeddings. However, in algorithmic perspective, the character-level learning approach in SG and CBoW improves the quality of representation learning, and overall window size, learning rate, number of epochs are the core parameters that largely influence the performance of word embeddings models. Ultimately, the new corpus of low-resourced Sindhi language, list of stop words and pretrained word embeddings along with empirical evaluation, will be a good supplement for future research in SSLP applications. In the future, we aim to use the corpus for annotation projects such as parts-of-speech tagging, named entity recognition. The proposed word embeddings will be refined further by creating custom benchmarks and the extrinsic evaluation approach will be employed for the performance analysis of proposed word embeddings. Moreover, we will also utilize the corpus using Bi-directional Encoder Representation Transformer BIBREF13 for learning deep contextualized Sindhi word representations. Furthermore, the generated word embeddings will be utilized for the automatic construction of Sindhi WordNet." + ], + [ + "In this paper, we mainly present three novel contributions of large corpus development contains large vocabulary of more than 61 million tokens, 908,456 unique words. Secondly, the list of Sindhi stop words is constructed by finding their high frequency and least importance with the help of Sindhi linguistic expert. Thirdly, the unsupervised Sindhi word embeddings are generated using state-of-the-art CBoW, SG and GloVe algorithms and evaluated using popular intrinsic evaluation approaches of cosine similarity matrix and WordSim353 for the first time in Sindhi language processing. We translate English WordSim353 using the English-Sindhi bilingual dictionary, which will also be a good resource for the evaluation of Sindhi word embeddings. Moreover, the proposed word embeddings are also compared with recently revealed SdfastText word representations.", + "Our empirical results demonstrate that our proposed Sindhi word embeddings have captured high semantic relatedness in nearest neighboring words, word pair relationship, country, and capital and WordSim353. The SG yields the best performance than CBoW and GloVe models subsequently. However, the performance of GloVe is low on the same vocabulary because of character-level learning of word representations and sub-sampling approaches in SG and CBoW. Our proposed Sindhi word embeddings have surpassed SdfastText in the intrinsic evaluation matrix. Also, the vocabulary of SdfastText is limited because they are trained on a small Wikipedia corpus of Sindhi Persian-Arabic. We will further investigate the extrinsic performance of proposed word embeddings on the Sindhi text classification task in the future. The proposed resources along with systematic evaluation will be a sophisticated addition to the computational resources for statistical Sindhi language processing." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0796/instruction.md b/qasper-0796/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..b6e2662d3cb59b98742e63d57d82d066fdea97f4 --- /dev/null +++ b/qasper-0796/instruction.md @@ -0,0 +1,53 @@ +Name of Paper: The Wiki Music dataset: A tool for computational analysis of popular music + +Question: Which decades did they look at? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Motivation, Background and Related Work", + "Brief introduction to popular music", + "Data Description", + "Experiments", + "Conclusion Acknowledgments and Future" + ], + "paragraphs": [ + [ + "Until recent times, the research in popular music was mostly bound to a non-computational approach BIBREF0 but the availability of new data, models and algorithms helped the rise of new research trends. Computational analysis of music structure BIBREF1 is focused on parsing and annotate patters in music files; computational music generation BIBREF2 trains systems able to generate songs with specific music styles; computational sociology of music analyzes databases annotated with metadata such as tempo, key, BPMs and similar (generally referred to as sonic features); even psychology of music use data to find new models.", + "Recent papers in computational sociology investigated novelty in popular music, finding that artists who are highly culturally and geographically connected are more likely to create novel songs, especially when they span multiple genres, are women, or are in the early stages of their careers BIBREF3. Using the position in Billboard charts and the sonic features of more than 20K songs, it has been demonstrated that the songs exhibiting some degree of optimal differentiation in novelty are more likely to rise to the top of the charts BIBREF4. These findings offer very interesting perspectives on how popular culture impacts the competition of novel genres in cultural markets. Another problem addressed in this research field is the distinction between what is popular and what is significative to a musical context BIBREF5. Using a user-generated set of tags collected through an online music platform, it has been possible to compute a set of metrics, such as novelty, burst or duration, from a co-occurrence tag network relative to music albums, in order to find the tags that propagate more and the albums having a significative impact. Combining sonic features and topic extraction techniques from approximately 17K tracks, scholars demonstrate quantitative trends in harmonic and timbral properties that brought changes in music sound around 1964, 1983 and 1991 BIBREF6. Beside these research fields, there is a trend in the psychology of music that studies how the musical preferences are reflected in the dimensions of personality BIBREF7. From this kind of research emerged the MUSIC model BIBREF8, which found that genre preferences can be decomposed into five factors: Mellow (relaxed, slow, and romantic), Unpretentious, (easy, soft, well-known), Sophisticated (complex, intelligent or avant-garde), Intense (loud, aggressive, and tense) and Contemporary (catchy, rhythmic or danceable).", + "Is it possible to find trends in the characteristics of the genres? And is it possible to predict the characteristics of future genres? To answer these questions, we produced a hand-crafted dataset with the intent to put together MUSIC, style and sonic features, annotated by music genre and indexed by time and decade. To do so, we collected a list of popular music genres by decade from Wikipedia and instructed annotators to score them. The paper is structured as follows: In section SECREF2 we provide a brief history of popular music, in section SECREF3 we describe the dataset and in section SECREF4 we provide the results of the experiments. In the end we draw some conclusions." + ], + [ + "We define \u201dpopular music\u201d as the music which finds appeal out of culturally closed music groups, also thanks to its commercial nature. Non-popular music can be divided into three broad groups: classical music (produced and performed by experts with a specific education), folk/world music (produced and performed by traditional cultures), and utility music (such as hymns and military marches, not primarily intended for commercial purposes). Popular music is a great mean for spreading culture, and a perfect ground where cultural practices and industry processes combine. In particular the cultural processes select novelties, broadly represented by means of underground music genres, and the industry tries to monetize, making them commercially successful. In the following description we include almost all the genres that reach commercial success and few of the underground genres that are related to them.", + "Arguably the beginning of popular music is in the USA between 1880s and 1890s with spirituals, work and shout chants BIBREF9, that we classify half-way between world music and popular music. The first real popular music genres in the 1900s were ragtime, pioneer of piano blues and jazz, and gospel, derived from religious chants of afro-american communities and pioneer of soul and RnB. The 1910s saw the birth of tin pan alley (simple pop songs for piano composed by professionals) and dixieland jazz, a spontaneous melting pot of ragtime, classical, afroamerican and haitian music BIBREF10. In the 1920s, blues and hillbilly country became popular. The former was born as a form of expression of black communities and outcasts, while the latter was a form of entertainment of the white rural communities. Tin pan alley piano composers soon commercialized tracks in the style of blues, generating boogie-woogie as a reaction, an underground and very aggressive piano blues played by black musicians. In Chicago and New York jazz became more sophisticated and spread to Europe, where gipsy jazz became popular soon after. Both in US and Europe, the 1930s were dominated by swing, the most popular form of jazz, which was at the same time danceable, melanchonic, catchy and intelligent. In the US the west swing, a mellow and easy type of country music, became popular thanks to western movies. The 1940s in the US saw a revival of dixieland jazz, the rise of be-bop (one of the most mellow and intelligent forms of jazz), the advent of crooners (male pop singers) and the establishment of back-to-the-roots types of country music such as bluegrass, a reaction against west swing, modernity and electric guitars. In the underground there was honky-tonk, a sad kind of country music that will influence folk rock. In the 1950s rock and roll was created by black communities with the electric fusion of blues, boogie-woogie and hillbilly and soon commercialized for large white audiences. Beside this, many things happened: urban blues forged its modern sound using electric guitars and harmonicas; cool jazz, played also by white people, launched a more commercial and clean style; gospel influenced both doo-wop, (a-cappella music performed by groups of black singers imitating crooners) and RnB, where black female singers played with a jazz or blues band. The 1960s saw an explosion of genres: countrypolitan, an electric and easy form of country music, became the most commercialized genre in the US; the first independent labels (in particular the Motown) turned doo-wop into well-arranged and hyper-produced soul music with a good commercial success BIBREF11; ska, a form of dance music with a very typical offbeat, became popular outside of Jamaica; garage (and also surf) rock arose as the first forms of independent commercial rock music, sometimes aggressive and sometimes easy; in the UK, beat popularized a new style of hyper-produced rock music that had a very big commercial success; blues rock emerged as the mix of the two genres; teenypop was created in order to sell records to younger audiences; independent movements like beat generation and hippies helped the rise of folk rock and psychedelic rock respectively BIBREF12; funk emerged from soul and jazz (while jazz turned into the extremely complex free jazz as a reaction against the commercial cool jazz, but remained underground). In the 1970s progressive rock turned psychedelia into a more complex form, independent radios contribute to its diffusion as well as the popularity of songwriters, an evolution of folk singers that proliferated from latin america (nueva canci\u00f3n) to western Europe. In the meanwhile, TV became a new channel for music marketing , exploited by glam rock, that emerged as a form of pop rock music with a fake trasgressive image and eclectic arrangements; fusion jazz begun to include funk and psychedelic elements; the disillusion due to the end of hippie movement left angry and frustrated masses listening to hard rock and blues rock, that included anti-religious symbols and merged into heavy metal. Then garage and independent rock, fueled by anger and frustration, was commercialized as punk rock at the end of the decade, while disco music (a catchy and hyper-danceable version of soul and RnB) was played in famous clubs and linked to sex and fun, gathering the LGBT communities. The poorest black communities, kept out from the disco clubs, begun to perform in house-parties, giving rise to old skool rap, whose sampled sounds and rhythmic vocals were a great novelty but remained underground. The real novelties popularized in this decade were ambient (a very intelligent commercial downtempo music derived from classical music), reggae (which mixed ska, rock and folk and from Jamaica conquered the UK) and above all synth electronica, a type of industrial experimental music that became popular for its new sound and style, bridging the gap between rock and electronic music. This will deeply change the sound of the following decades BIBREF13. The 1980s begun with the rise of synth pop and new wave. The former, also referred to as \u201dnew romantics\u201d, was a popular music that mixed catchy rhythms with simple melodies and synthetic sounds while the latter was an hipster mix of glam rock and post-punk with a positive view (as opposed to the depressive mood of the real post-punk), with minor influences from synth electronica and reggae. The music industry created also glam metal for the heavy metal audiences, that reacted with extreme forms like thrash metal; a similar story happened with punk audiences, that soon moved to extreme forms like hardcore, which remained underground but highlighted a serious tensions between industry and the audiences that wanted spontaneous genres BIBREF14. In the meanwhile discopop produced a very catchy, easy and danceable music mix of disco, funk and synthetic sounds, that greatly improved the quality of records, yielding to one of the best selling genres in the whole popular music history. In a similar way smooth jazz (a mix of mellow and easy melodies with synthetic rhythmical bases) and soft adult (a mellow and easy form of pop) obtained a good commercial success. Techno music emerged as a new form of danceable synthetic and funky genre and hard rap became popular both in black and white audiences, while electro (break dance at the time) and (pioneering) house music remained underground for their too much innovative sampled sounds. In the 1990s alternative/grunge rock solved the tension between commercial and spontaneous genres with a style of rock that was at the same time aggressive, intelligent and easy to listen to. The same happened with skatepunk (a fast, happy and commercial form of rock) and rap metal (a mix of the two genres) while britpop continued the tradition of pop rock initiated with beat. RnB evolved into new jack swing (a form of softer, rhythmical and easy funk) and techno split into the commercial eurodance (a mix of techno and disco music with synthetic sounds, manipulated RnB vocals and strong beats) and the subculture of rave (an extremely aggressive form of techno played in secret parties and later in clubs), which helped the creation of goa trance, that new hippie communities used for accompany drug trips BIBREF15. An intelligent and slow mix of electro and RnB became popular as trip hop while an aggressive and extremely fast form of electro with reggae influences became popular as jungle/DnB. By the end of the decade the most commercially successful genres were dancepop (a form of pop that included elements of funk, disco and eurodance in a sexy image) and gangsta rap/hip hop that reached its stereotypical form and became mainstream, while independent labels (that produced many subgenres from shoegaze/indie rock to electro and house) remained in the underground. In the underground -but in latin america- there was also reggaet\u00f3n, a latin form of rap. The rise of free download and later social networks websites in 2000s opened new channels for independent genres, that allowed the rise of grime (a type of electro mixing DnB and rap), dubstep (a very intelligent and slow mix of techno, DnB and electro low-fi samples), indietronica (a broad genre mixing intelligent indie rock, electro and a lot of minor influences) and later nu disco (a revival of stylish funk and disco updated with electro and house sounds) BIBREF16. In the meanwhile there were popular commercial genres like garage rock revival (that updated rock and punk with danceable beats), emo rock/post grunge (aggressive, easy and even more catchy), urban breaks (a form of RnB with heavy electro and rap influences) and above all electropop (the evolution of dancepop, that included elements of electro/house and consolidated the image of seductive female singers, also aimed at the youngest audiences of teens). Among those genres epic trance (an euphoric, aggressive and easy form of melodic techno) emerged from the biggest dedicated festivals and became mainstream with over-payed DJ-superstars BIBREF17. In the underground remained various forms of nu jazz, hardcore techno, metal and house music. Then in 2010s finally euro EDM house music (a form of sample-based and heavily danceable mix of house and electro) came out of underground communities and, borrowing the figure of DJ-superstar from trance, reached commercial success, but left underground communities unsatisfied (they were mostly producing complex electro, a mix of dubstep and avant-garde house). Also drumstep (a faster and aggressive version of dubstep, influenced by EDM and techno) and trap music (a form of dark and heavy techno rap) emerged from underground and had good commercial success. Genres like indiefolk (a modern and eclectic folk rock with country influences) and nu prog rock (another eclectic, experimental and aggressive form of rock with many influences from electro, metal and rap) had moderate success. The availability of websites for user-generated contents such as Youtube helped to popularize genres like electro reggaet\u00f3n (latin rap with new influences from reggae and electro), cloud rap (an eclectic and intelligent form of rap with electro influences) and JK-pop (a broad label that stands for Japanese and Korean pop, but emerged from all over the world with common features: Youtubers that produce easy and catchy pop music with heavy influences from electropop, discopop and eurodance) BIBREF18. Moreover, technologies helped the creation of mainstream genres such as tropical house (a very melodic, soft and easy form of house music singed in an modern RnB style). In the underground there are yet many minor genres, such as bro country (an easy form of country played by young and attractive guys and influenced by electro and rap), future hardstyle (a form of aggressive trance with easy vocals similar to tropical house) and afrobeat (a form of rap that is popular in western africa with influences from reggaet\u00f3n and traditional african music).", + "From this description we can highlight some general and recurrent tendencies, for example the fact that music industry converts spontaneous novelties into commercial success, but when its products leave audiences frustrated (it happened with west swing, glam metal, cool jazz, punk and many others), they generate reactions in underground cultures, that trigger a change into more aggressive versions of the genre. In general, underground and spontaneous genres are more complex and avant-garde. Another pattern is that media allowed more and more local underground genres to influence the mainstream ones, ending in a combinatorial explosion of possible new genres, most of which remain underground. We suggest that we need to quantify a set of cross-genre characteristics in order to compute with data science techniques some weaker but possibly significative patterns that cannot be observed with qualitative methods. In the next section we define a quantitative methodology and we annotate a dataset to perform experiments." + ], + [ + "From the description of music genres provided above emerges that there is a limited number of super-genres and derivation lines BIBREF19, BIBREF20, as shown in figure FIGREF1.", + "From a computational perspective, genres are classes and, although can be treated by machine learning algorithms, they do not include information about the relations between them. In order to formalize the relations between genres for computing purposes, we define a continuous genre scale from the most experimental and introverted super-genre to the most euphoric and inclusive one. We selected from Wikipedia the 77 genres that we mentioned in bold in the previous paragraph and asked to two independent raters to read the Wikipedia pages of the genres, listen to samples or artists of the genres (if they did not know already) and then annotate the following dimensions:", + "genre features: genre scale (a score between 0 and 1 where 0=downtempo/industrial, 0.1=metal, 0.15=garage/punk/hardcore, 0.2=rock, 0.25=pop rock, 0.3=blues, 0.4=country, 0.5=pop/traditional, 0.55=gospel, 0.6=jazz, 0.65=latin, 0.7=RnB/soul/funk, 0.75=reggae/jamaican, 0.8=rap, 0.85=DnB, 0.9=electro/house, 0.95=EDM, 1=techno/trance) and category of the super-genre (as defined in figure FIGREF1) and influence variety 0.1=influence only from the same super-genre, 1=influences from all the supergenres", + "perceived acoustic features: sound (0=acoustic, 0.35=amplified, 0.65=sampled/manipulated, 1=synthetic), vocal melody (1=melodic vocals, 0=rhythmical vocals/spoken words), vocal scream (1=screaming, 0=soft singing), vocal emotional (1=emotional vocals, 0=monotone vocals), virtuous (0.5=normal, 0=not technical at all, 1=very technical); richbass 1=the bass is loud and clear, 0=there is no bass sound; offbeat 1=the genre has a strong offbeat, 0=the genre has not offbeat", + "time: decade (classes between 1900s and 2010s) and year representative of the time when the genre became meainstream", + "place features: origin place 0=Australia, 0.025=west USA, 0.05=south USA, 0.075=north/east USA, 0.1=UK, 0.2=jamaica, 0.3=carribean, 0.4=latin america, 0.5=africa, 0.6=south EU, 0.65=north/east EU, 0.7=middle east, 0.8=India, 0.9=China/south asia, 1=Korea/north asia; place urban (0=the origin place is rural, 1=the origin place is urban), place poor (0=the origin place is poor, 1=the origin place is rich)", + "media features: media mainstream (0=independent media, 1=mainstream media, 0.5=both), media live 0=sell recorded music, 1=sell live performance)", + "emotion features: joy/sad (1=joy, 0=sad), anticipation/surprise (1=anticipation or already known, 0=surprise), anger/calm (1=anger, 0=calm).", + "style features: novelty 0=derivative, 0.5=normal, 1=totally new characteristics and type retro 1=the genre is a revival, 0.5=normal, 0=the genre is not a revival, lyrics love/explicit 0.5=normal, 1=love lyrics, 0=explicit lyrics, style upbeat 1=extroverted and danceable, 0=introverted and depressive, style instrumental 1=totally instrumental, 0=totally singed, style eclecticism 1=includes many styles, 0=has a stereotypical style, style longsongs 0.5=radio format (3.30 minutes), 1=more than 6 minutes by average, 0=less than 1 minute by average; largebands 1=bands of 10 or more people, 0.1=just one musician; subculture 1=the audience one subculture or more, 0=the audience is the main culture; hedonism 1=the genre promotes hedonism, 0=the genre does not promote hedonism; protest 1=the genre promotes protest, 0=the genere does not promote protest; onlyblack 1=genere produced only by black communities, 0=genre produced only by white communities; ; 44beat 1=the genre has 4/4 beat, 0=the genre has other types of measures; outcasts 1=the audience is poor people, 0=the audience is rich people; dancing 1=the genre is for dancing, 0=the genre is for home listening; drugs 1=the audience use drugs, 0=the audience do not use drugs", + "MUSIC features: mellow (1=slow and romantic, 0=fast and furious), sophisticated (1=culturally complex, 0=easy to understand), intense (1=aggressive and loud, 0=soft and relaxing), contemporary (1=rhythmical and catchy, 0=not rhythmical and old-fashioned), uncomplicated (1=simple and well-known, 0=strange and disgustive)", + "We computed the agreement between the two annotators using Cronbach's alpha statistics BIBREF21. The average between all features is $\\alpha =0.793$, which is good. Among the most agreed features there are genre, place, sound and MUSIC features. In particular, the genre scale got an excellent $\\alpha =0.957$, meaning that the genre scale is a reliable measure. In the final annotation all the divergences between the two annotators were agreed upon and the scores were averaged or corrected accordingly. The final dataset is available to the scientific community." + ], + [ + "What are the tendencies that confirm or disconfirm previous findings? We noticed very interesting remarks just from the distributions of the features, reported in figure FIGREF11.", + "We can see that most of the popular music genres have a novelty score between 0.5 and 0.65, which is medium-high. This confirms the findings of previous work about the optimal level of innovation and acceptance. It is interesting to note that almost all the popular genres come from an urban context, where the connections between communities are more likely to create innovations. Moreover, we can see that the distribution of mainstream media is bi-modal: this means that an important percentage of genres are popularized by means of underground or new media. This happened many times in music history, from the the free radios to the web of the user-generated content. Crucially, popular music genres strongly tend to be perceived as technically virtuous.", + "Why the sound changed from acoustic to synthetic during the last century? To answer this question we used a correlation analysis with the sound feature as target. It emerged that the change towards sampled and synthetic sound is correlated to dancing, to intensity/aggressiveness, to a larger drug usage and to a large variety of infleunces, while it is negatively correlated to large bands and mellow tones. In summary a more synthetic sound allowed a more intense and danceable music, reducing the number of musicians (in other words reducing costs for the industry).", + "How the music taste of the audience of popular music changed in the last century? The trend lines of the MUSIC model features, reported in figure FIGREF12, reveal that audiences wanted products more and more contemporary, intense and a little bit novel or sophisticated, but less and less mellow and (surprisingly) unpretentious. In other words, the audiences of popular music are getting more demanding as the quality and variety of the music products increases.", + "Is it possible to predict future genres by means of the genre scale? To answer this question we used time series forecasting. In particular, we exploited all the features in the years from 1900 to 2010 to train a predictive model of the scores from 2011 to 2018. As the year of the genre label is arbitrary, predicted scores and labels can be not aligned, thus MAE or RSME are not suitable evaluation metrics. As evaluation metric we defined average accuracy as $a=\\frac{\\sum count(|l-h|<0.1)}{count(t)} $, where the label (l) and the prediction (h) can be anywhere within the year serie (t). Table TABREF13, shows the results of the prediction of genre scale for the years 2011 to 2018 with different algorithms: linear regression (LR), Support Vector Machine (SVM), multi layer perceptron (MPL), nearest neighbors (IBk), and a meta classifier (stacking) with SVM+MLP+IBk.", + "The results reveal that the forecasting of music genres is a non-linear problem, that IBk predicts the closest sequence to the annotated one and that a meta classifier with nearest neighborsBIBREF22 is the most accurate in the prediction. Deep Learning algorithms does not perform well in this case because the dataset is not large enough. Last remark: feature reduction (from 41 to 14) does not affect the results obtained with IBk and meta classifiers, indicating that there is no curse of dimensionality." + ], + [ + "We annotated and presented a new dataset for the computational analysis of popular music. Our preliminary studies confirm previous findings (there is an optimal level of novelty to become popular and this is more likely to happen in urban contexts) and reveal that audiences tend to like contemporary and intense music experiences. We also performed a back test for the prediction of future music genres in a time series, that turned out to be a non-linear problem. For the future we would like to update the corpus with more features about audience types and commercial success. This work has also been inspired by Music Map." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0797/instruction.md b/qasper-0797/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..3819fd3b684df505d7874fa5966da683bae4a79b --- /dev/null +++ b/qasper-0797/instruction.md @@ -0,0 +1,53 @@ +Name of Paper: The Wiki Music dataset: A tool for computational analysis of popular music + +Question: How many genres did they collect from? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Motivation, Background and Related Work", + "Brief introduction to popular music", + "Data Description", + "Experiments", + "Conclusion Acknowledgments and Future" + ], + "paragraphs": [ + [ + "Until recent times, the research in popular music was mostly bound to a non-computational approach BIBREF0 but the availability of new data, models and algorithms helped the rise of new research trends. Computational analysis of music structure BIBREF1 is focused on parsing and annotate patters in music files; computational music generation BIBREF2 trains systems able to generate songs with specific music styles; computational sociology of music analyzes databases annotated with metadata such as tempo, key, BPMs and similar (generally referred to as sonic features); even psychology of music use data to find new models.", + "Recent papers in computational sociology investigated novelty in popular music, finding that artists who are highly culturally and geographically connected are more likely to create novel songs, especially when they span multiple genres, are women, or are in the early stages of their careers BIBREF3. Using the position in Billboard charts and the sonic features of more than 20K songs, it has been demonstrated that the songs exhibiting some degree of optimal differentiation in novelty are more likely to rise to the top of the charts BIBREF4. These findings offer very interesting perspectives on how popular culture impacts the competition of novel genres in cultural markets. Another problem addressed in this research field is the distinction between what is popular and what is significative to a musical context BIBREF5. Using a user-generated set of tags collected through an online music platform, it has been possible to compute a set of metrics, such as novelty, burst or duration, from a co-occurrence tag network relative to music albums, in order to find the tags that propagate more and the albums having a significative impact. Combining sonic features and topic extraction techniques from approximately 17K tracks, scholars demonstrate quantitative trends in harmonic and timbral properties that brought changes in music sound around 1964, 1983 and 1991 BIBREF6. Beside these research fields, there is a trend in the psychology of music that studies how the musical preferences are reflected in the dimensions of personality BIBREF7. From this kind of research emerged the MUSIC model BIBREF8, which found that genre preferences can be decomposed into five factors: Mellow (relaxed, slow, and romantic), Unpretentious, (easy, soft, well-known), Sophisticated (complex, intelligent or avant-garde), Intense (loud, aggressive, and tense) and Contemporary (catchy, rhythmic or danceable).", + "Is it possible to find trends in the characteristics of the genres? And is it possible to predict the characteristics of future genres? To answer these questions, we produced a hand-crafted dataset with the intent to put together MUSIC, style and sonic features, annotated by music genre and indexed by time and decade. To do so, we collected a list of popular music genres by decade from Wikipedia and instructed annotators to score them. The paper is structured as follows: In section SECREF2 we provide a brief history of popular music, in section SECREF3 we describe the dataset and in section SECREF4 we provide the results of the experiments. In the end we draw some conclusions." + ], + [ + "We define \u201dpopular music\u201d as the music which finds appeal out of culturally closed music groups, also thanks to its commercial nature. Non-popular music can be divided into three broad groups: classical music (produced and performed by experts with a specific education), folk/world music (produced and performed by traditional cultures), and utility music (such as hymns and military marches, not primarily intended for commercial purposes). Popular music is a great mean for spreading culture, and a perfect ground where cultural practices and industry processes combine. In particular the cultural processes select novelties, broadly represented by means of underground music genres, and the industry tries to monetize, making them commercially successful. In the following description we include almost all the genres that reach commercial success and few of the underground genres that are related to them.", + "Arguably the beginning of popular music is in the USA between 1880s and 1890s with spirituals, work and shout chants BIBREF9, that we classify half-way between world music and popular music. The first real popular music genres in the 1900s were ragtime, pioneer of piano blues and jazz, and gospel, derived from religious chants of afro-american communities and pioneer of soul and RnB. The 1910s saw the birth of tin pan alley (simple pop songs for piano composed by professionals) and dixieland jazz, a spontaneous melting pot of ragtime, classical, afroamerican and haitian music BIBREF10. In the 1920s, blues and hillbilly country became popular. The former was born as a form of expression of black communities and outcasts, while the latter was a form of entertainment of the white rural communities. Tin pan alley piano composers soon commercialized tracks in the style of blues, generating boogie-woogie as a reaction, an underground and very aggressive piano blues played by black musicians. In Chicago and New York jazz became more sophisticated and spread to Europe, where gipsy jazz became popular soon after. Both in US and Europe, the 1930s were dominated by swing, the most popular form of jazz, which was at the same time danceable, melanchonic, catchy and intelligent. In the US the west swing, a mellow and easy type of country music, became popular thanks to western movies. The 1940s in the US saw a revival of dixieland jazz, the rise of be-bop (one of the most mellow and intelligent forms of jazz), the advent of crooners (male pop singers) and the establishment of back-to-the-roots types of country music such as bluegrass, a reaction against west swing, modernity and electric guitars. In the underground there was honky-tonk, a sad kind of country music that will influence folk rock. In the 1950s rock and roll was created by black communities with the electric fusion of blues, boogie-woogie and hillbilly and soon commercialized for large white audiences. Beside this, many things happened: urban blues forged its modern sound using electric guitars and harmonicas; cool jazz, played also by white people, launched a more commercial and clean style; gospel influenced both doo-wop, (a-cappella music performed by groups of black singers imitating crooners) and RnB, where black female singers played with a jazz or blues band. The 1960s saw an explosion of genres: countrypolitan, an electric and easy form of country music, became the most commercialized genre in the US; the first independent labels (in particular the Motown) turned doo-wop into well-arranged and hyper-produced soul music with a good commercial success BIBREF11; ska, a form of dance music with a very typical offbeat, became popular outside of Jamaica; garage (and also surf) rock arose as the first forms of independent commercial rock music, sometimes aggressive and sometimes easy; in the UK, beat popularized a new style of hyper-produced rock music that had a very big commercial success; blues rock emerged as the mix of the two genres; teenypop was created in order to sell records to younger audiences; independent movements like beat generation and hippies helped the rise of folk rock and psychedelic rock respectively BIBREF12; funk emerged from soul and jazz (while jazz turned into the extremely complex free jazz as a reaction against the commercial cool jazz, but remained underground). In the 1970s progressive rock turned psychedelia into a more complex form, independent radios contribute to its diffusion as well as the popularity of songwriters, an evolution of folk singers that proliferated from latin america (nueva canci\u00f3n) to western Europe. In the meanwhile, TV became a new channel for music marketing , exploited by glam rock, that emerged as a form of pop rock music with a fake trasgressive image and eclectic arrangements; fusion jazz begun to include funk and psychedelic elements; the disillusion due to the end of hippie movement left angry and frustrated masses listening to hard rock and blues rock, that included anti-religious symbols and merged into heavy metal. Then garage and independent rock, fueled by anger and frustration, was commercialized as punk rock at the end of the decade, while disco music (a catchy and hyper-danceable version of soul and RnB) was played in famous clubs and linked to sex and fun, gathering the LGBT communities. The poorest black communities, kept out from the disco clubs, begun to perform in house-parties, giving rise to old skool rap, whose sampled sounds and rhythmic vocals were a great novelty but remained underground. The real novelties popularized in this decade were ambient (a very intelligent commercial downtempo music derived from classical music), reggae (which mixed ska, rock and folk and from Jamaica conquered the UK) and above all synth electronica, a type of industrial experimental music that became popular for its new sound and style, bridging the gap between rock and electronic music. This will deeply change the sound of the following decades BIBREF13. The 1980s begun with the rise of synth pop and new wave. The former, also referred to as \u201dnew romantics\u201d, was a popular music that mixed catchy rhythms with simple melodies and synthetic sounds while the latter was an hipster mix of glam rock and post-punk with a positive view (as opposed to the depressive mood of the real post-punk), with minor influences from synth electronica and reggae. The music industry created also glam metal for the heavy metal audiences, that reacted with extreme forms like thrash metal; a similar story happened with punk audiences, that soon moved to extreme forms like hardcore, which remained underground but highlighted a serious tensions between industry and the audiences that wanted spontaneous genres BIBREF14. In the meanwhile discopop produced a very catchy, easy and danceable music mix of disco, funk and synthetic sounds, that greatly improved the quality of records, yielding to one of the best selling genres in the whole popular music history. In a similar way smooth jazz (a mix of mellow and easy melodies with synthetic rhythmical bases) and soft adult (a mellow and easy form of pop) obtained a good commercial success. Techno music emerged as a new form of danceable synthetic and funky genre and hard rap became popular both in black and white audiences, while electro (break dance at the time) and (pioneering) house music remained underground for their too much innovative sampled sounds. In the 1990s alternative/grunge rock solved the tension between commercial and spontaneous genres with a style of rock that was at the same time aggressive, intelligent and easy to listen to. The same happened with skatepunk (a fast, happy and commercial form of rock) and rap metal (a mix of the two genres) while britpop continued the tradition of pop rock initiated with beat. RnB evolved into new jack swing (a form of softer, rhythmical and easy funk) and techno split into the commercial eurodance (a mix of techno and disco music with synthetic sounds, manipulated RnB vocals and strong beats) and the subculture of rave (an extremely aggressive form of techno played in secret parties and later in clubs), which helped the creation of goa trance, that new hippie communities used for accompany drug trips BIBREF15. An intelligent and slow mix of electro and RnB became popular as trip hop while an aggressive and extremely fast form of electro with reggae influences became popular as jungle/DnB. By the end of the decade the most commercially successful genres were dancepop (a form of pop that included elements of funk, disco and eurodance in a sexy image) and gangsta rap/hip hop that reached its stereotypical form and became mainstream, while independent labels (that produced many subgenres from shoegaze/indie rock to electro and house) remained in the underground. In the underground -but in latin america- there was also reggaet\u00f3n, a latin form of rap. The rise of free download and later social networks websites in 2000s opened new channels for independent genres, that allowed the rise of grime (a type of electro mixing DnB and rap), dubstep (a very intelligent and slow mix of techno, DnB and electro low-fi samples), indietronica (a broad genre mixing intelligent indie rock, electro and a lot of minor influences) and later nu disco (a revival of stylish funk and disco updated with electro and house sounds) BIBREF16. In the meanwhile there were popular commercial genres like garage rock revival (that updated rock and punk with danceable beats), emo rock/post grunge (aggressive, easy and even more catchy), urban breaks (a form of RnB with heavy electro and rap influences) and above all electropop (the evolution of dancepop, that included elements of electro/house and consolidated the image of seductive female singers, also aimed at the youngest audiences of teens). Among those genres epic trance (an euphoric, aggressive and easy form of melodic techno) emerged from the biggest dedicated festivals and became mainstream with over-payed DJ-superstars BIBREF17. In the underground remained various forms of nu jazz, hardcore techno, metal and house music. Then in 2010s finally euro EDM house music (a form of sample-based and heavily danceable mix of house and electro) came out of underground communities and, borrowing the figure of DJ-superstar from trance, reached commercial success, but left underground communities unsatisfied (they were mostly producing complex electro, a mix of dubstep and avant-garde house). Also drumstep (a faster and aggressive version of dubstep, influenced by EDM and techno) and trap music (a form of dark and heavy techno rap) emerged from underground and had good commercial success. Genres like indiefolk (a modern and eclectic folk rock with country influences) and nu prog rock (another eclectic, experimental and aggressive form of rock with many influences from electro, metal and rap) had moderate success. The availability of websites for user-generated contents such as Youtube helped to popularize genres like electro reggaet\u00f3n (latin rap with new influences from reggae and electro), cloud rap (an eclectic and intelligent form of rap with electro influences) and JK-pop (a broad label that stands for Japanese and Korean pop, but emerged from all over the world with common features: Youtubers that produce easy and catchy pop music with heavy influences from electropop, discopop and eurodance) BIBREF18. Moreover, technologies helped the creation of mainstream genres such as tropical house (a very melodic, soft and easy form of house music singed in an modern RnB style). In the underground there are yet many minor genres, such as bro country (an easy form of country played by young and attractive guys and influenced by electro and rap), future hardstyle (a form of aggressive trance with easy vocals similar to tropical house) and afrobeat (a form of rap that is popular in western africa with influences from reggaet\u00f3n and traditional african music).", + "From this description we can highlight some general and recurrent tendencies, for example the fact that music industry converts spontaneous novelties into commercial success, but when its products leave audiences frustrated (it happened with west swing, glam metal, cool jazz, punk and many others), they generate reactions in underground cultures, that trigger a change into more aggressive versions of the genre. In general, underground and spontaneous genres are more complex and avant-garde. Another pattern is that media allowed more and more local underground genres to influence the mainstream ones, ending in a combinatorial explosion of possible new genres, most of which remain underground. We suggest that we need to quantify a set of cross-genre characteristics in order to compute with data science techniques some weaker but possibly significative patterns that cannot be observed with qualitative methods. In the next section we define a quantitative methodology and we annotate a dataset to perform experiments." + ], + [ + "From the description of music genres provided above emerges that there is a limited number of super-genres and derivation lines BIBREF19, BIBREF20, as shown in figure FIGREF1.", + "From a computational perspective, genres are classes and, although can be treated by machine learning algorithms, they do not include information about the relations between them. In order to formalize the relations between genres for computing purposes, we define a continuous genre scale from the most experimental and introverted super-genre to the most euphoric and inclusive one. We selected from Wikipedia the 77 genres that we mentioned in bold in the previous paragraph and asked to two independent raters to read the Wikipedia pages of the genres, listen to samples or artists of the genres (if they did not know already) and then annotate the following dimensions:", + "genre features: genre scale (a score between 0 and 1 where 0=downtempo/industrial, 0.1=metal, 0.15=garage/punk/hardcore, 0.2=rock, 0.25=pop rock, 0.3=blues, 0.4=country, 0.5=pop/traditional, 0.55=gospel, 0.6=jazz, 0.65=latin, 0.7=RnB/soul/funk, 0.75=reggae/jamaican, 0.8=rap, 0.85=DnB, 0.9=electro/house, 0.95=EDM, 1=techno/trance) and category of the super-genre (as defined in figure FIGREF1) and influence variety 0.1=influence only from the same super-genre, 1=influences from all the supergenres", + "perceived acoustic features: sound (0=acoustic, 0.35=amplified, 0.65=sampled/manipulated, 1=synthetic), vocal melody (1=melodic vocals, 0=rhythmical vocals/spoken words), vocal scream (1=screaming, 0=soft singing), vocal emotional (1=emotional vocals, 0=monotone vocals), virtuous (0.5=normal, 0=not technical at all, 1=very technical); richbass 1=the bass is loud and clear, 0=there is no bass sound; offbeat 1=the genre has a strong offbeat, 0=the genre has not offbeat", + "time: decade (classes between 1900s and 2010s) and year representative of the time when the genre became meainstream", + "place features: origin place 0=Australia, 0.025=west USA, 0.05=south USA, 0.075=north/east USA, 0.1=UK, 0.2=jamaica, 0.3=carribean, 0.4=latin america, 0.5=africa, 0.6=south EU, 0.65=north/east EU, 0.7=middle east, 0.8=India, 0.9=China/south asia, 1=Korea/north asia; place urban (0=the origin place is rural, 1=the origin place is urban), place poor (0=the origin place is poor, 1=the origin place is rich)", + "media features: media mainstream (0=independent media, 1=mainstream media, 0.5=both), media live 0=sell recorded music, 1=sell live performance)", + "emotion features: joy/sad (1=joy, 0=sad), anticipation/surprise (1=anticipation or already known, 0=surprise), anger/calm (1=anger, 0=calm).", + "style features: novelty 0=derivative, 0.5=normal, 1=totally new characteristics and type retro 1=the genre is a revival, 0.5=normal, 0=the genre is not a revival, lyrics love/explicit 0.5=normal, 1=love lyrics, 0=explicit lyrics, style upbeat 1=extroverted and danceable, 0=introverted and depressive, style instrumental 1=totally instrumental, 0=totally singed, style eclecticism 1=includes many styles, 0=has a stereotypical style, style longsongs 0.5=radio format (3.30 minutes), 1=more than 6 minutes by average, 0=less than 1 minute by average; largebands 1=bands of 10 or more people, 0.1=just one musician; subculture 1=the audience one subculture or more, 0=the audience is the main culture; hedonism 1=the genre promotes hedonism, 0=the genre does not promote hedonism; protest 1=the genre promotes protest, 0=the genere does not promote protest; onlyblack 1=genere produced only by black communities, 0=genre produced only by white communities; ; 44beat 1=the genre has 4/4 beat, 0=the genre has other types of measures; outcasts 1=the audience is poor people, 0=the audience is rich people; dancing 1=the genre is for dancing, 0=the genre is for home listening; drugs 1=the audience use drugs, 0=the audience do not use drugs", + "MUSIC features: mellow (1=slow and romantic, 0=fast and furious), sophisticated (1=culturally complex, 0=easy to understand), intense (1=aggressive and loud, 0=soft and relaxing), contemporary (1=rhythmical and catchy, 0=not rhythmical and old-fashioned), uncomplicated (1=simple and well-known, 0=strange and disgustive)", + "We computed the agreement between the two annotators using Cronbach's alpha statistics BIBREF21. The average between all features is $\\alpha =0.793$, which is good. Among the most agreed features there are genre, place, sound and MUSIC features. In particular, the genre scale got an excellent $\\alpha =0.957$, meaning that the genre scale is a reliable measure. In the final annotation all the divergences between the two annotators were agreed upon and the scores were averaged or corrected accordingly. The final dataset is available to the scientific community." + ], + [ + "What are the tendencies that confirm or disconfirm previous findings? We noticed very interesting remarks just from the distributions of the features, reported in figure FIGREF11.", + "We can see that most of the popular music genres have a novelty score between 0.5 and 0.65, which is medium-high. This confirms the findings of previous work about the optimal level of innovation and acceptance. It is interesting to note that almost all the popular genres come from an urban context, where the connections between communities are more likely to create innovations. Moreover, we can see that the distribution of mainstream media is bi-modal: this means that an important percentage of genres are popularized by means of underground or new media. This happened many times in music history, from the the free radios to the web of the user-generated content. Crucially, popular music genres strongly tend to be perceived as technically virtuous.", + "Why the sound changed from acoustic to synthetic during the last century? To answer this question we used a correlation analysis with the sound feature as target. It emerged that the change towards sampled and synthetic sound is correlated to dancing, to intensity/aggressiveness, to a larger drug usage and to a large variety of infleunces, while it is negatively correlated to large bands and mellow tones. In summary a more synthetic sound allowed a more intense and danceable music, reducing the number of musicians (in other words reducing costs for the industry).", + "How the music taste of the audience of popular music changed in the last century? The trend lines of the MUSIC model features, reported in figure FIGREF12, reveal that audiences wanted products more and more contemporary, intense and a little bit novel or sophisticated, but less and less mellow and (surprisingly) unpretentious. In other words, the audiences of popular music are getting more demanding as the quality and variety of the music products increases.", + "Is it possible to predict future genres by means of the genre scale? To answer this question we used time series forecasting. In particular, we exploited all the features in the years from 1900 to 2010 to train a predictive model of the scores from 2011 to 2018. As the year of the genre label is arbitrary, predicted scores and labels can be not aligned, thus MAE or RSME are not suitable evaluation metrics. As evaluation metric we defined average accuracy as $a=\\frac{\\sum count(|l-h|<0.1)}{count(t)} $, where the label (l) and the prediction (h) can be anywhere within the year serie (t). Table TABREF13, shows the results of the prediction of genre scale for the years 2011 to 2018 with different algorithms: linear regression (LR), Support Vector Machine (SVM), multi layer perceptron (MPL), nearest neighbors (IBk), and a meta classifier (stacking) with SVM+MLP+IBk.", + "The results reveal that the forecasting of music genres is a non-linear problem, that IBk predicts the closest sequence to the annotated one and that a meta classifier with nearest neighborsBIBREF22 is the most accurate in the prediction. Deep Learning algorithms does not perform well in this case because the dataset is not large enough. Last remark: feature reduction (from 41 to 14) does not affect the results obtained with IBk and meta classifiers, indicating that there is no curse of dimensionality." + ], + [ + "We annotated and presented a new dataset for the computational analysis of popular music. Our preliminary studies confirm previous findings (there is an optimal level of novelty to become popular and this is more likely to happen in urban contexts) and reveal that audiences tend to like contemporary and intense music experiences. We also performed a back test for the prediction of future music genres in a time series, that turned out to be a non-linear problem. For the future we would like to update the corpus with more features about audience types and commercial success. This work has also been inspired by Music Map." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0798/instruction.md b/qasper-0798/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..7d93dc5749975eba21bf06955d2d063b302f4d8f --- /dev/null +++ b/qasper-0798/instruction.md @@ -0,0 +1,102 @@ +Name of Paper: An Annotated Corpus of Emerging Anglicisms in Spanish Newspaper Headlines + +Question: Does the paper mention other works proposing methods to detect anglicisms in Spanish? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work", + "Anglicism: Scope of the Phenomenon", + "Corpus description and annotation ::: Corpus description", + "Corpus description and annotation ::: Corpus description ::: Main Corpus", + "Corpus description and annotation ::: Corpus description ::: Supplemental Test Set", + "Corpus description and annotation ::: Annotation guidelines", + "Baseline Model", + "Results", + "Future Work", + "Conclusions", + "Acknowledgements", + "Language Resource References" + ], + "paragraphs": [ + [ + "The study of English influence in the Spanish language has been a hot topic in Hispanic linguistics for decades, particularly concerning lexical borrowing or anglicisms BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6.", + "Lexical borrowing is a phenomenon that affects all languages and constitutes a productive mechanism for word-formation, especially in the press. chesleypaulapredicting2010 estimated that a reader of French newspapers encountered a new lexical borrowing for every 1,000 words. In Chilean newspapers, lexical borrowings account for approximately 30% of neologisms, 80% of those corresponding to English loanwords BIBREF7.", + "Detecting lexical borrowings is relevant both for lexicographic purposes and for NLP downstream tasks BIBREF8, BIBREF9. However, strategies to track and register lexical borrowings have traditionally relied on manual review of corpora.", + "In this paper we present: (1) a corpus of newspaper headlines in European Spanish annotated with emerging anglicisms and (2) a CRF baseline model for anglicism automatic extraction in Spanish newswire." + ], + [ + "Corpus-based studies of English borrowings in Spanish media have traditionally relied on manual evaluation of either previously compiled general corpora such as CREA BIBREF10, BIBREF11, BIBREF12, BIBREF13, either new tailor-made corpora designed to analyze specific genres, varieties or phenomena BIBREF14, BIBREF15, BIBREF16, BIBREF17, BIBREF18, BIBREF19, BIBREF20.", + "In terms of automatic detection of anglicisms, previous approaches in different languages have mostly depended on resource lookup (lexicon or corpus frequencies), character n-grams and pattern matching. alex-2008-comparing combined lexicon lookup and a search engine module that used the web as a corpus to detect English inclusions in a corpus of German texts and compared her results with a maxent Markov model. furiassi2007retrieval explored corpora lookup and character n-grams to extract false anglicisms from a corpus of Italian newspapers. andersen2012semi used dictionary lookup, regular expressions and lexicon-derived frequencies of character n-grams to detect anglicism candidates in the Norwegian Newspaper Corpus (NNC) BIBREF21, while losnegaard2012data explored a Machine Learning approach to anglicism detection in Norwegian by using TiMBL (Tilburg Memory-Based Learner, an implementation of a k-nearest neighbor classifier) with character trigrams as features. garley-hockenmaier-2012-beefmoves trained a maxent classifier with character n-gram and morphological features to identify anglicisms in German online communities. In Spanish, serigos2017using extracted anglicisms from a corpus of Argentinian newspapers by combining dictionary lookup (aided by TreeTagger and the NLTK lemmatizer) with automatic filtering of capitalized words and manual inspection. In serigos2017applying, a character n-gram module was added to estimate the probabilities of a word being English or Spanish. moreno2018configuracion used different pattern-matching filters and lexicon lookup to extract anglicism cadidates from a corpus of tweets in US Spanish.", + "Work within the code-switching community has also dealt with language identification on multilingual corpora. Due to the nature of code-switching, these models have primarily focused on oral copora and social media datasets BIBREF22, BIBREF23, BIBREF24. In the last shared task of language identification in code-switched data BIBREF23, approaches to English-Spanish included CRFs models BIBREF25, BIBREF26, BIBREF27, BIBREF28, logistic regression BIBREF29 and LSTMs models BIBREF30, BIBREF31.", + "The scope and nature of lexical borrowing is, however, somewhat different to that of code-switching. In fact, applying code-switching models to lexical borrowing detection has previously proved to be unsuccessful, as they tend to overestimate the number of anglicisms BIBREF32. In the next section we address the differences between both phenomena and set the scope of this project." + ], + [ + "Linguistic borrowing can be defined as the transference of linguistic elements between two languages. Borrowing and code-switching have frequently been described as a continuum BIBREF33, with a fuzzy frontier between the two. As a result, a precise definition of what borrowing is remains elusive BIBREF34 and some authors prefer to talk about code-mixing in general BIBREF35 or \u201clone other-language incorporations\" BIBREF36.", + "Lexical borrowing in particular involves the incorporation of single lexical units from one language into another language and is usually accompanied by morphological and phonological modification to conform with the patterns of the recipient language BIBREF37, BIBREF38. By definition, code-switches are not integrated into a recipient language, unlike established loanwords BIBREF39. While code-switches are usually fluent multiword interferences that normally comply with grammatical restrictions in both languages and that are produced by bilingual speakers in bilingual discourses, lexical borrowings are words used by monolingual individuals that eventually become lexicalized and assimilated as part of the recipient language lexicon until the knowledge of \u201cforeign\" origin disappears BIBREF40.", + "In terms of approaching the problem, automatic code-switching identification has been framed as a sequence modeling problem where every token receives a language ID label (as in a POS-tagging task). Borrowing detection, on the other hand, while it can also be transformed into a sequence labeling problem, is an extraction task, where only certain spans of texts will be labeled (in the fashion of a NER task).", + "Various typologies have been proposed that aim to classify borrowings according to different criteria, both with a cross-linguistic perspective and also specifically aimed to characterize English inclusions in Spanish BIBREF34, BIBREF41, BIBREF42, BIBREF5. In this work, we will be focusing on unassimilated lexical borrowings (sometimes called foreignisms), i.e. words from English origin that are introduced into Spanish without any morphological or orthographic adaptation." + ], + [ + "In this subsection we describe the characteristics of the corpus. We first introduce the main corpus, with the usual train/development/test split that was used to train, tune and evaluate the model. We then present an additional test set that was designed to assess the performance of the model on more naturalistic data." + ], + [ + "The main corpus consists of a collection of monolingual newspaper headlines written in European Spanish. The corpus contains 16,553 headlines, which amounts to 244,114 tokens. Out of those 16,553 headlines, 1,109 contain at least one anglicism. The total number of anglicisms is 1,176 (most of them are a single word, although some of them were multiword expressions). The corpus was divided into training, development and test set. The proportions of headlines, tokens and anglicisms in each corpus split can be found in Table TABREF6.", + "The headlines in this corpus come from the Spanish newspaper eldiario.es, a progressive online newspaper based in Spain. eldiario.es is one of the main national newspapers from Spain and, to the best of our knowledge, the only one that publishes its content under a Creative Commons license, which made it ideal for making the corpus publicly available.", + "The headlines were extracted from the newspaper website through web scraping and range from September 2012 to January 2020. Only the following sections were included: economy, technology, lifestyle, music, TV and opinion. These sections were chosen as they were the most likely to contain anglicisms. The proportion of headlines with anglicisms per section can be found in Table TABREF7.", + "Using headlines (instead of full articles) was beneficial for several reasons. First of all, annotating a headline is faster and easier than annotating a full article; this helps ensure that a wider variety of topics will be covered in the corpus. Secondly, anglicisms are abundant in headlines, because they are frequently used as a way of calling the attention of the reader BIBREF43. Finally, borrowings that make it to the headline are likely to be particularly salient or relevant, and therefore are good candidates for being extracted and tracked." + ], + [ + "In addition to the usual train/development/test split we have just presented, a supplemental test set of 5,017 headlines was collected. The headlines included in this additional test set also belong to eldiario.es. These headlines were retrieved daily through RSS during February 2020 and included all sections from the newspaper. The headlines in the supplemental corpus therefore do not overlap in time with the main corpus and include more sections. The number of headlines, tokens and anglicisms in the supplemental test set can be found in Table TABREF6.", + "The motivation behind this supplemental test set is to assess the model performance on more naturalistic data, as the headlines in the supplemental corpus (1) belong to the future of the main corpus and (2) come from a less borrowing-dense sample. This supplemental test set better mimics the real scenario that an actual anglicism extractor would face and can be used to assess how well the model generalizes to detect anglicisms in any section of the daily news, which is ultimately the aim of this project." + ], + [ + "The term anglicism covers a wide range of linguistic phenomena. Following the typology proposed by gomez1997towards, we focused on direct, unadapted, emerging Anglicisms, i.e. lexical borrowings from the English language into Spanish that have recently been imported and that have still not been assimilated into Spanish. Other phenomena such as semantic calques, syntactic anglicisms, acronyms and proper names were considered beyond the scope of this annotation project.", + "Lexical borrowings can be adapted (the spelling of the word is modified to comply with the phonological and orthographic patterns of the recipient language) or unadapted (the word preserves its original spelling). For this annotation task, adapted borrowings were ignored and only unadapted borrowings were annotated. Therefore, Spanish adaptations of anglicisms like f\u00fatbol (from football), mitin (from meeting) and such were not annotated as borrowings. Similarly, words derived from foreign lexemes that do not comply with Spanish orthotactics but that have been morphologically derived following the Spanish paradigm (hacktivista, hackear, shakespeariano) were not annotated either. However, pseudo-anglicisms (words that are formed as if they were English, but do not exist in English, such as footing or balconing) were annotated.", + "Words that were not adapted but whose original spelling complies with graphophonological rules of Spanish (and are therefore unlikely to be ever adapted, such as web, internet, fan, club, videoclip) were annotated or not depending on how recent or emergent they were. After all, a word like club, that has been around in Spanish language for centuries, cannot be considered emergent anymore and, for this project, would not be as interesting to retrieve as real emerging anglicisms. The notion of emergent is, however, time-dependent and quite subjective: in order to determine which unadapted, graphophonologically acceptable borrowings were to be annotated, the online version of the Diccionario de la lengua espa\u00f1ola dle was consulted. This dictionary is compiled by the Royal Spanish Academy, a prescriptive institution on Spanish language. This decision was motivated by the fact that, if a borrowing was already registered by this dictionary (that has conservative approach to language change) and is considered assimilated (that is, the institution recommended no italics or quotation marks to write that word) then it could be inferred that the word was not emergent anymore.", + "Although the previous guidelines covered most cases, they proved insufficient. Some anglicisms were unadapted (they preserved their original spelling), unacceptable according to the Spanish graphophonological rules, and yet did not satisfy the condition of being emergent. That was the case of words like jazz or whisky, words that do not comply with Spanish graphophonological rules but that were imported decades ago, cannot be considered emergent anymore and are unlikely to ever be adapted into the Spanish spelling system. To adjudicate these examples on those cases, the criterion of pragmatic markedness proposed by winter2012proposing (that distinguishes between catachrestic and non-catachrestic borrowing) was applied: if a borrowing was not adapted (i.e. its form remained exactly as it came from English) but referred to a particular invention or innovation that came via the English language, that was not perceived as new anymore and that had never competed with a Spanish equivalent, then it was ignored. This criteria proved to be extremely useful to deal with old unadapted anglicisms in the fields of music and food. Figure 1 summarizes the decision steps followed during the annotation process.", + "The corpus was annotated by a native speaker of Spanish using Doccano doccano. The annotation tagset includes two labels: ENG, to annotate the English borrowings just described, and OTHER. This OTHER tag was used to tag lexical borrowings from languages other than English. After all, although English is today by far the most prevalent donor of borrowings, there are other languages that also provide new borrowings to Spanish. Furthermore, the tag OTHER allows to annotate borrowings such as premi\u00e8re or tempeh, borrowings that etymologically do not come from English but that have entered the Spanish language via English influence, even when their spelling is very different to English borrowings. In general, we considered that having such a tag could also help assess how successful a classifier is detecting foreign borrowings in general in Spanish newswire (without having to create a label for every possible donor language, as the number of examples would be too sparse). In total, the training set contained 40 entities labeled as OTHER, the development set contained 14 and the test set contained 13. The supplemental test set contained 35 OTHER entities." + ], + [ + "A baseline model for automatic extraction of anglicisms was created using the annotated corpus we just presented as training material. As mentioned in Section 3, the task of detecting anglicisms can be approached as a sequence labeling problem where only certain spans of texts will be labeled as anglicism (in a similar way to an NER task). The chosen model was conditional random field model (CRF), which was also the most popular model in both Shared Tasks on Language Identification for Code-Switched Data BIBREF23, BIBREF24.", + "The model was built using pycrfsuite korobov2014python, the Python wrapper for crfsuite CRFsuite that implements CRF for labeling sequential data. It also used the Token and Span utilities from spaCy library honnibal2017spacy.", + "The following handcrafted features were used for the model:", + "Bias feature", + "Token feature", + "Uppercase feature (y/n)", + "Titlecase feature (y/n)", + "Character trigram feature", + "Quotation feature (y/n)", + "Word suffix feature (last three characters)", + "POS tag (provided by spaCy utilities)", + "Word shape (provided by spaCy utilities)", + "Word embedding (see Table TABREF26)", + "Given that anglicisms can be multiword expressions (such as best seller, big data) and that those units should be treated as one borrowing and not as two independent borrowings, we used multi-token BIO encoding to denote the boundaries of each span BIBREF44. A window of two tokens in each direction was set for the feature extractor. The algorithm used was gradient descent with the L-BFGS method.", + "The model was tuned on the development set doing grid search; the hyperparameters considered were c1 (L1 regularization coefficient: $0.01$, $0.05$, $0.1$, $0.5$, $1.0$), c2 (L2 regularization coefficient: $0.01$, $0.05$, $0.1$, $0.5$, $1.0$), embedding scaling ($0.5$, $1.0$, $2.0$, $4.0$), and embedding type bojanowski2017enriching,josecanete20193255001,cardellinoSBWCE,grave2018learning,honnibal2017spacy,perezfasttext,perezglove (see Table TABREF26). The best results were obtained with c1 = $0.05$, c2 = $0.01$, scaling = $0.5$ and word2vec Spanish embeddings by cardellinoSBWCE. The threshold for the stopping criterion delta was selected through observing the loss during preliminary experiments (delta = $1\\mathrm {e}-3$).", + "In order to assess the significance of the the handcrafted features, a feature ablation study was done on the tuned model, ablating one feature at a time and testing on the development set. Due to the scarcity of spans labeled with the OTHER tag on the development set (only 14) and given that the main purpose of the model is to detect anglicisms, the baseline model was run ignoring the OTHER tag both during tuning and the feature ablation experiments. Table TABREF27 displays the results on the development set with all features and for the different feature ablation runs. The results show that all features proposed for the baseline model contribute to the results, with the character trigram feature being the one that has the biggest impact on the feature ablation study." + ], + [ + "The baseline model was then run on the test set and the supplemental test set with the set of features and hyperparameters mentioned on Section SECREF5 Table TABREF28 displays the results obtained. The model was run both with and without the OTHER tag. The metrics for ENG display the results obtained only for the spans labeled as anglicisms; the metrics for OTHER display the results obtained for any borrowing other than anglicisms. The metrics for BORROWING discard the type of label and consider correct any labeled span that has correct boundaries, regardless of the label type (so any type of borrowing, regardless if it is ENG or OTHER). In all cases, only full matches were considered correct and no credit was given to partial matching, i.e. if only fake in fake news was retrieved, it was considered wrong and no partial score was given.", + "Results on all sets show an important difference between precision and recall, precision being significantly higher than recall. There is also a significant difference between the results obtained on development and test set (F1 = 89.60, F1 = 87.82) and the results on the supplemental test set (F1 = 71.49). The time difference between the supplemental test set and the development and test set (the headlines from the the supplemental test set being from a different time period to the training set) can probably explain these differences.", + "Comparing the results with and without the OTHER tag, it seems that including it on the development and test set produces worse results (or they remain roughly the same, at best). However, the best precision result on the supplemental test was obtained when including the OTHER tag and considering both ENG and OTHER spans as BORROWING (precision = 87.62). This is caused by the fact that, while the development and test set were compiled from anglicism-rich newspaper sections (similar to the training set), the supplemental test set contained headlines from all the sections in the newspaper, and therefore included borrowings from other languages such as Catalan, Basque or French. When running the model without the OTHER tag on the supplemental test set, these non-English borrowings were labeled as anglicisms by the model (after all, their spelling does not resemble Spanish spelling), damaging the precision score. When the OTHER tag was included, these non-English borrowings got correctly labeled as OTHER, improving the precision score. This proves that, although the OTHER tag might be irrelevant or even damaging when testing on the development or test set, it can be useful when testing on more naturalistic data, such as the one in the supplemental test set.", + "Concerning errors, two types of errors were recurrent among all sets: long titles of songs, films or series written in English were a source of false positives, as the model tended to mistake some of the uncapitalized words in the title for anglicisms (for example, it darker in \u201c`You want it darker', la oscura y brillante despedida de Leonard Cohen\"). On the other hand, anglicisms that appear on the first position of the sentence (and were, therefore, capitalized) were consistently ignored (as the model probably assumed they were named entities) and produced a high number of false negatives (for example, vamping in \u201cVamping: la recurrente leyenda urbana de la luz azul `asesina'\").", + "The results on Table TABREF28 cannot, however, be compared to the ones reported by previous work: the metric that we report is span F-measure, as the evaluation was done on span level (instead of token level) and credit was only given to full matches. Secondly, there was no Spanish tag assigned to non-borrowings, that means that no credit was given if a Spanish token was identified as such." + ], + [ + "This is an on-going project. The corpus we have just presented is a first step towards the development of an extractor of emerging anglicisms in the Spanish press. Future work includes: assessing whether to keep the OTHER tag, improving the baseline model (particularly to improve recall), assessing the suitability and contribution of different sets of features and exploring different models. In terms of the corpus development, the training set is now closed and stable, but the test set could potentially be increased in order to have more and more diverse anglicisms." + ], + [ + "In this paper we have presented a new corpus of 21,570 newspaper headlines written in European Spanish. The corpus is annotated with emergent anglicisms and, up to our very best knowledge, is the first corpus of this type to be released publicly. We have presented the annotation scope, tagset and guidelines, and we have introduced a CRF baseline model for anglicism extraction trained with the described corpus. The results obtained show that the the corpus and baseline model are appropriate for automatic anglicism extraction." + ], + [ + "The author would like to thank Constantine Lignos for his feedback and advice on this project." + ], + [ + "lrec" + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0810/instruction.md b/qasper-0810/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..4ce101f1982a121a91972a648c207defbcbb48e7 --- /dev/null +++ b/qasper-0810/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Duality Regularization for Unsupervised Bilingual Lexicon Induction + +Question: What are new best results on standard benchmark? \ No newline at end of file diff --git a/qasper-0816/instruction.md b/qasper-0816/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..d397bf679d63ba6584964e40b07bd3d51ffc8b13 --- /dev/null +++ b/qasper-0816/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Team Papelo: Transformer Networks at FEVER + +Question: What baseline do they compare to? \ No newline at end of file diff --git a/qasper-0817/instruction.md b/qasper-0817/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..c94e78a141ff5869e7ab8efec549dc9b37d62b15 --- /dev/null +++ b/qasper-0817/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Team Papelo: Transformer Networks at FEVER + +Question: Which pre-trained transformer do they use? \ No newline at end of file diff --git a/qasper-0819/instruction.md b/qasper-0819/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..bb7e1800dc44ec32318210625d8011f7a3823c1b --- /dev/null +++ b/qasper-0819/instruction.md @@ -0,0 +1,97 @@ +Name of Paper: Automatic Differentiation in ROOT + +Question: How is correctness of automatic derivation proved? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Background", + "Background ::: AD and its Modes", + "Background ::: AD Implementations", + "Architecture and Implementation", + "Results", + "Results ::: Accuracy", + "Results ::: Performance", + "Results ::: Performance in TFormula", + "Conclusion", + "Acknowledgments" + ], + "paragraphs": [ + [ + "Accurate and efficient computation of derivatives is vital for a wide variety of computing applications, including numerical optimization, solution of nonlinear equations, sensitivity analysis, and nonlinear inverse problems. Virtually every process could be described with a mathematical function, which can be thought of as an association between elements from different sets. Derivatives track how a varying quantity depends on another quantity, for example how the position of a planet varies as time varies.", + "Derivatives and gradients (vectors of partial derivatives of multivariable functions) allow us to explore the properties of a function and thus the described process as a whole. Gradients are an essential component in gradient-based optimization methods, which have become more and more important in recent years, in particular with its application training of (deep) neural networks BIBREF0.", + "Several different techniques are commonly used to compute the derivatives of a given function, either exactly or approximately BIBREF1, BIBREF0, BIBREF2. The most prevalent techniques are:", + "Numerical differentiation, based on the finite difference method, provides a way to evaluate derivatives approximately. While simple, numerical differentiation can be slow (the run-time complexity grows linearly with the number of input variables) and may have problems with accuracy due to round-off and truncation errors.", + "Symbolic differentiation, based on transformations of symbolic expressions of functions, provides exact closed-form expressions for the derivatives. It faces difficulties when the function to be differentiated is not available in a closed form, which is often the case for computer programs which may contain control flow. Symbolic differentiation can produce derivative expressions that are computationally expensive to evaluate due to difficulties in exploiting common subexpressions.", + "Automatic differentiation (AD) computes derivatives accurately to the precision of the original function, supports control flow and uses at most a small constant factor more time and space than it takes to evaluate the original function, at the expense of increased implementation complexity and introducing more software dependencies.", + "Numerical and symbolic differentiation methods are slow at computing gradients of functions with many input variables, as is often needed for gradient-based optimization algorithms. Both methods have problems calculating higher-order derivatives, where the complexity and errors due to numerical precision increase. Automatic differentiation largely avoids the problems of numerical and symbolic differentiation.", + "In this paper, we describe the implementation of automatic differentiation techniques in ROOT, which is the data analysis framework broadly used High-Energy Physics BIBREF3. This implementation is based on Clad BIBREF4, BIBREF5, which is an automatic differentiation plugin for computation expressed in C/C++." + ], + [ + "Here, we briefly discuss main algorithmic and implementation principles behind AD. An in-depth overview and more formal description can be found in BIBREF1 and BIBREF2, respectively." + ], + [ + "AD is based on the decomposition of the procedure (e.g. a source code that computes the original function) into a sequence of simple mathematical operations (e.g. $+, -, *, /, \\sin , \\cos , \\exp $) that can be expressed using a series of intermediate results. Subsequently, derivatives of every intermediate result are evaluated and combined via the chain rule of calculus to obtain the derivatives of the whole sequence. The control flow (e.g. branches, loops) can be incorporated by differentiating the control flow of the original function during the derivative evaluation. Two main modes of AD, which differ in the order of application of the chain rule, are used:", + "Forward mode operates in a top-down approach and computes the derivative of every intermediate result with respect to a single selected input variable of the function. As soon as a final result of the function is reached, the partial derivative with respect to the selected input is available. A single evaluation of the forward mode can only compute partial derivatives with respect to a single input variable. Thus, when the whole gradient is required, forward mode must be invoked once per every input variable, leading to $m \\cdot c_{F} \\cdot n$ runtime complexity, where $m$ is the number of input variables, $n$ is the algorithmic complexity of the original function and $c_{F} < 3 $ is a small constant factor overhead of a single invocation of the forward mode BIBREF2.", + "Reverse mode operates in a bottom-up approach and computes the derivative of a function's output with respect to every intermediate result. Once every input variable of the function is reached, the whole gradient of an output is available. Note that, independently on the number of input variables $N$, a single evaluation of the reverse mode is sufficient to get the whole gradient of a function's output, leading to $c_{R} \\cdot n$ runtime complexity, where $n$ is the complexity of the original function and $c_{R} \\le 4$ is a small constant factor overhead BIBREF2. This is a huge advantage in settings with a single scalar output and many inputs, which is often the case in machine-learning problems where $N >> 10^6$ that makes the forward mode infeasible. As a disadvantage, reverse mode implementations are more complicated, and dynamic memory allocations may be required when dynamic control flow is involved. Depending on the original function, this may cause a single evaluation of the reverse mode to be somewhat slower compared to a single evaluation of the forward mode." + ], + [ + "AD techniques have been implemented in a variety of programming languages and paradigms, ranging from classical tools for Fortran BIBREF6 and C BIBREF7, to recent active work on tools specific to machine-learning applications BIBREF8, BIBREF9, and modern general-purpose programming languages BIBREF10, BIBREF11. We refer the reader to www.autodiff.org for a comprehensive list of available AD implementations for various languages.", + "In particular, several implementations exist for C++, e.g. BIBREF12, BIBREF13, BIBREF14. Majority of implementations of AD fall into one of the two categories of implementation techniques:", + "Tools based on operator overloading utilize features of programming languages like C++ and Python to define custom types and overload mathematical operators (e.g. +, -, *, /) and functions (e.g. $\\exp , \\sin , \\cos $) on them. Such implementations are often based on custom AD-enabled types that wrap values of both the original and derivative functions and redefine operators to simultaneously act on original and derivative values. In C++, such tools are often implemented as a library that introduces templated differentiable types and corresponding mathematical operations. Then, functions called on the custom type return both original and derivative values. This is a powerful technique but has two primary limitations: legacy code and performance. Functions must be either polymorphic (templated) or explicitly defined on AD-enabled type to be differentiated. Differentiation of pre-existing source code using builtin types such as double and float is not possible. Users are required to use additional level of abstraction in the form of library-specific types instead of first-class language features. Moreover, the performance of the derivative generation can be suboptimal due to the C++ metaprogramming system which usually constructs deep template instantiation chains. Performance can be even more problematic when creating a higher order derivatives.", + "Tools based on source transformation analyze the source code of the original function and build another source code for the derivative function. Such techniques typically accept and generate any code using built-in features of the original language and do not require custom libraries. On the other hand, they require an additional pass over the source file to analyze and generate derivative code. Source transformation can fully utilize source-level optimizations and has reasonably good performance. Implementation is more complicated and it is problematic to achieve full coverage of C++ language features. While full integration with a compiler can make AD a first-class language feature that is transparent for the user, most current implementations for C++ are based on custom parsers that do not have full coverage of the vast variety of C++ language constructs and require a separate step before compilation." + ], + [ + "Automatic differentiation in ROOT is based on Clad BIBREF4, BIBREF5. Clad is a source transformation AD tool for C++. It is based on LLVM compiler infrastructure BIBREF15 and is implemented as a plugin for C++ compiler Clang, which allows Clad to be transparently integrated into the compilation phase and to utilize large parts of the compiler. Clad relies on Clang's parsing and code generation functionality and can differentiate complicated C++ constructs. Clad supports both forward and reverse mode. It is available as a standalone Clang plugin that, when attached to the compiler, produces derivatives in the compilation phase.", + "On top of that, Clad is integrated directly into ROOT to provide AD functionality as an integral part of the framework. ROOT has a C++ interpreter Cling BIBREF16 which is built on the top of LLVM and Clang. This allows Clad to be attached to Cling as a plugin in a similar way as it can be attached to Clang. In this section, we discuss 1) architecture of Clad and its interaction with Cling; and 2) details of its integration into ROOT.", + "Clad operates on Clang AST (abstract syntax tree) by analyzing the AST of the original function and generating the AST of the derivative. Clad provides two API functions: clad::differentiate for forward mode and clad::gradient for reverse mode, which can be used directly in the source code to mark a function for differentiation (see BIBREF5 for more details on usage and code examples).", + "The information flow of interactions with Cling during differentiation (Figure FIGREF13) is:", + "A function is marked for differentiation with the C++ construct clad::differentiate or clad::gradient (step 1).", + "Cling in ROOT performs incremental compilation and receives an abstract syntax tree (AST) representation of the code (step 2).", + "Cling detects the differentiation marker and sends the AST of the original function to Clad, which transforms the AST to produce the AST of the derivative (step 3).", + "Clad returns the derivative AST to Cling for code generation and execution by the low level LLVM primitives (steps 4, 5, 6, 7). Alternatively, if Clad was configured for non-interactive use, the generated AST can be converted to a C++ source code and written to a text file. The generated code then can be compiled with any C++ compiler (steps 8, 9).", + "Inside of ROOT, interface functions clad::differentiate and clad::gradient are accessible via include . Clad is also directly integrated into the TFormula class that encapsulates the concept of multidimensional mathematical functions in ROOT. TFormula is a primitive in ROOT's math package which is connected to the Cling interpreter. In the context of TFormula, Clad can differentiate functions available in the interpreter. The TFormula::GenerateGradientPar method uses Clad to differentiate the underlying code of the formula with respect to its parameters and generate the code for the gradient. TFormula::GradientPar method then evaluates the gradient at a specified point." + ], + [ + "In this section, we empirically compare automatic differentiation (AD, our implementation based on Clad) and numerical differentiation (ND, based on finite difference method) in ROOT. We show that AD can drastically improve accuracy and performance of derivative evaluation, compared to ND." + ], + [ + "As stated in Section SECREF1, numerical differentiation may give imprecise results while AD computes the derivatives exactly. We show an example of a function where this difference is apparent: AD provides exact result while ND suffers from the loss of accuracy.", + "2", + "", + "The function is the PDF of Breit-Wigner distribution (Eq. DISPLAY_FORM19), whose derivative with respect to $\\Gamma $ (Eq. DISPLAY_FORM20) has critical points at $\\Gamma =\\pm {2x}$. In ROOT, the function is implemented as in (Listing SECREF18).", + "linenos=false inline double breitwignerpdf(double x, double gamma, double x0 = 0) double gammahalf = gamma/2.0; return gammahalf/(MPI * ((x-x0)*(x-x0) + gammahalf*gammahalf));", + "listingBreit-Wigner PDF implementation in ROOT", + "", + "When evaluating the derivative of breitwignerpdf with respect to gamma at x=1, gamma=2, ND in ROOT the yields a result close to 0 with an absolute error of $10^{-13}$ despite the fact that the function is smooth and well-conditioned at this point. The approximation error becomes larger when the derivative is evaluated further from the critical point. In contrast, the automatic differentiation (in both modes) yields the exact result of 0." + ], + [ + "Section SECREF2 showed that reverse mode AD computes gradients in a single pass with a runtime complexity of at most $4 \\cdot n$, which depends only on the complexity $n$ and not the dimensionality $dim$ of the original function. On the other hand, numerical differentiation requires a separate evaluation of the original function for every dimension to compute the entire gradient, making the overall the run-time complexity of gradient evaluation via central finite difference method $2 \\cdot dim \\cdot n$. Hence, in theory, reverse mode achieves an asymptotic speedup of $O(dim)$ over the numerical differentiation and can be up to $dim / 2$ times faster.", + "We experimentally verify this by comparing the performance of gradient evaluation produced by reverse mode AD against our an implementation of numerical differentiation via the central finite difference method. We use the two functions in Listing SECREF21: sum, which computes the sum of all values in a vector; and mvn, which implements the PDF of a multivariate normal distribution. Both functions have a parameter dim which defines the dimension, and gradients are taken with respect to dim-dimensional vector p. While closed-form expressions of these gradients are well-known, these functions make a good basis of a benchmark as they perform typical operations that are commonly found inside more complicated functions (e.g. +, *, pow, exp inside loop).", + "", + "linenos=false double sum(double* p, int dim) double r = 0.0; for (int i = 0; i < dim; i++) r += p[i]; return r; linenos=false double mvn(double* x, double* p /*means*/, double sigma, int dim) double t = 0; for (int i = 0; i < dim; i++) t += (x[i] - p[i])*(x[i] - p[i]); t = -t / (2*sigma*sigma); return std::pow(2*MPI, -n/2.0) * std::pow(sigma, -0.5) * std::exp(t); listingImplementations of sum and mvn functions", + "Gradients of sum produced by numerical differentiation and Clad are shown in Listing SECREF21.", + "", + "linenos=false double* sumnumgrad(double* p, int dim, double eps = 1e-8) double result = new double[dim]; for (int i = 0; i < dim; i++) double pi = p[i]; p[i] = pi + eps; double v1 = sum(p, dim); p[i] = pi - eps; double v2 = sum(p, dim); result[i] = (v1 - v2)/(2 * eps); p[i] = pi; return result;", + "linenos=false void sumadgrad(double *p, int dim, double *result) double dr = 0; unsigned long t0; int di = 0; clad::tape t1 = ; double r = 0.; t0 = 0; for (int i = 0; i < dim; i++) t0++; r += p[clad::push(t1, i)]; double sumreturn = r; dr += 1; for (; t0; t0\u2013) double rd0 = dr; dr += rd0; result[clad::pop(t1)] += rd0; dr -= rd0; listingGradient of sum: (left) using finite differences, (right) generated by Clad", + "We perform the evaluation for values of dim between 5 and 20480. Figure FIGREF22 shows the comparison for (a) sum; (b) mvn and confirms the expected theoretical speedup of $O(dim)$, with AD-generated gradient being $~dim/4$ times faster for sum and $~dim/25$ times faster for mvn (slowdown is due to more expensive operations like pow, exp).", + "", + "" + ], + [ + "Figure FIGREF26 shows the performance comparisons of reverse-mode AD and ND for the task of evaluating gradients of TFormula's builtin primitive probability density functions. The functions are gaus ($dim=3$), expo ($dim=2$), crystalball ($dim=5$), breitwigner ($dim=5$) and cheb2 ($dim=4$). Despite the low dimensionality ($dim \\le 5$), AD gives significant (approx. 10x) speedups. The speedups are even larger than expected factor of $dim/2$ that follows from theoretical results, apparently due to additional overhead of the implementation of numerical differentiation in ROOT, which tries to find the optimal step size for its finite difference method to improve accuracy.", + "In Figure FIGREF26, we perform fitting of a Gaussian distribution to a histogram of random samples via gradient-based optimization. In ROOT, this functionality is implemented in TFormula-based TF1 class. We can therefore use AD due to the integration of Clad into TFormula. Figure FIGREF26 compares the performance of the AD-based TF1 fitting with the numerical fitting in the Hist package. As in previous experiments, we show that AD scales better with problem dimensionality (number of histogram bins) on this task. The integration of Clad into TFormula makes it straightforward to use AD for fitting in ROOT." + ], + [ + "We discussed our implementation of automatic differentiation in ROOT based on Clad. We demonstrated that Clad is integrated into ROOT and can be easily used in various contexts inside ROOT (e.g. histogram fitting). Furthermore, we showed that automatic differentiation in ROOT achieves significant improvements in accuracy and performance over numerical differentiation. The performance and accuracy are promising and encourage further work in the development of Clad and its integration in ROOT.", + "Possible further improvements for Clad include optimizations to code transformation and design of a consistent interface for derivatives and gradients computation. This functionality can be further extended, including the computation of Jacobians and higher-order derivatives. In order to achieve optimal performance, the evaluation of individual derivatives could be executed in parallel. Besides, the Clad API should enable a flexible execution method based on the needs of its user." + ], + [ + "This work has been supported by U.S. NSF grants PHY-1450377 and 1450323." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0821/instruction.md b/qasper-0821/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..326a0c26ed374a941e931955d8500b8df0087feb --- /dev/null +++ b/qasper-0821/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Controlling the Output Length of Neural Machine Translation + +Question: Do they conduct any human evaluation? \ No newline at end of file diff --git a/qasper-0826/instruction.md b/qasper-0826/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..f650e0720621210173b8b852558338e0f306d241 --- /dev/null +++ b/qasper-0826/instruction.md @@ -0,0 +1,109 @@ +Name of Paper: Controlling the Output Length of Neural Machine Translation + +Question: What dataset do they use? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Background", + "Background ::: Transformer", + "Background ::: Length encoding in summarization", + "Methods", + "Methods ::: Length Token Method", + "Methods ::: Length Encoding Method", + "Methods ::: Combining the two methods", + "Methods ::: Fine-Tuning for length control", + "Experiments ::: Data and Settings", + "Experiments ::: Models", + "Experiments ::: Evaluation", + "Results", + "Results ::: Small Data condition", + "Results ::: Large data condition", + "Results ::: Human Evaluation and Analysis", + "Related works", + "Conclusion" + ], + "paragraphs": [ + [ + "The sequence to sequence BIBREF0, BIBREF1 approach to Neural Machine Translation (NMT) has shown to improve quality in various translation tasks BIBREF2, BIBREF3, BIBREF4. While translation quality is normally measured in terms of correct transfer of meaning and of fluency, there are several applications of NMT that would benefit from optimizing the output length, such as the translation of document elements that have to fit a given layout \u2013 e.g. entries of tables or bullet points of a presentation \u2013 or subtitles, which have to fit visual constraints and readability goals, as well as speech dubbing, for which the length of the translation should be as close as possible to the length of the original sentence.", + "Current NMT models do not model explicitly sentence lengths of input and output, and the decoding methods do not allow to specify desired number of tokens to be generated. Instead, they implicitly rely on the observed length of the training examples BIBREF5, BIBREF6.", + "Sequence-to-sequence models have been also applied to text summarization BIBREF7 to map the relevant information found in a long text into a limited-length summary. Such models have shown promising results by directly controlling the output length BIBREF8, BIBREF9, BIBREF10, BIBREF11. However, differently from MT, text summarization (besides being a monolingual task) is characterized by target sentences that are always much shorter than the corresponding source sentences. While in MT, the distribution of the relative lengths of source and target depends on the two languages and can significantly vary from one sentence pair to another due to stylistic decisions of the translator and linguistic constraints (e.g. idiomatic expressions).", + "In this work, we propose two approaches to control the output length of a transformer NMT model. In the first approach, we augment the source side with a token representing a specific length-ratio class, i.e. short, normal, and long, which at training time corresponds to the observed ratio and at inference time to the desired ratio. In the second approach, inspired by recent work in text summarization BIBREF11, we enrich the position encoding used by the transformer model with information representing the position of words with respect to the end of the target string.", + "We investigate both methods, either in isolation or combined, on two translation directions (En-It and En-De) for which the length of the target is on average longer than the length of the source. Our ultimate goal is to generate translations whose length is not longer than that of the source string (see example in Table FIGREF1). While generating translations that are just a few words shorter might appear as a simple task, it actually implies good control of the target language. As the reported examples show, the network has to implicitly apply strategies such as choosing shorter rephrasing, avoiding redundant adverbs and adjectives, using different verb tenses, etc. We report MT performance results under two training data conditions, small and large, which show limited degradation in BLEU score and n-gram precision as we vary the target length ratio of our models. We also run a manual evaluation which shows for the En-It task a slight quality degradation in exchange of a statistically significant reduction in the average length ratio, from 1.05 to 1.01." + ], + [ + "Our proposal is based on the transformer architecture and a recently proposed extension of its positional encoding aimed to control the length of generated sentences in text summarization." + ], + [ + "Transformer BIBREF12 is a sequence-to-sequence architecture that processes sequences using only attention and feed forward layers. Its core component is the so-called multi-head attention, which computes attention BIBREF0, BIBREF13 between two sequences in a multi-branch fashion BIBREF14. Within the encoder or the decoder, each layer first computes attention between two copies of the same sequence (self-attention). In the decoder, this step is followed by an attention over the encoder output sequence. The last step in each layer is a two-layered time-distributed feed-forward network, with a hidden size larger than its input and output. Attention and feed-forward layers are characterized by a position-invariant processing of their input. Thus, in order to enrich input embeddings in source and target with positional information, they are summed with positional vectors of the same dimension $d$, which are computed with the following trigonometric encoding ($\\text{PE}$):", + "for $i=1,\\ldots ,d/2$." + ], + [ + "Recently, an extension of the positional encoding BIBREF11 was proposed to model the output length for text summarization. The goal is achieved by computing the distance from every position to the end of the sentence. The new length encoding is present only in the decoder network as an additional vector summed to the input embedding. The authors proposed two different variants. The first variant replaces the variable pos in equations (1-2) with the difference $len - pos$, where len is the sentence length. The second variant attempts to model the proportion of the sentence that has been covered at a given position by replacing the constant 10000 in the denominator of equations (1-2) with $len$. As decoding is performed at the character level, len and pos are given in number of characters. At training time, len is the observed length of the reference summary, while at inference time it is the desired length." + ], + [ + "We propose two methods to control the output length in NMT. In the first method we partition the training set in three groups according to the observed length ratio of the reference over the source text. The idea is to let the model learn translation variants by observing them jointly with an extra input token. The second method extends the Transformer positional encoding to give information about the remaining sentence length. With this second method the network can leverage fine-grained information about the sentence length." + ], + [ + "Our first approach to control the length is inspired by target forcing in multilingual NMT BIBREF15, BIBREF16. We first split the training sentence pairs into three groups according to the target/source length ratio (in terms of characters). Ideally, we want a group where the target is shorter than the source (short), one where they are equally-sized (normal) and a last group where the target is longer than the source (long). In practice, we select two thresholds $t_\\text{min}$ and $t_\\text{max}$ according to the length ratio distribution. All the sentence pairs with length ratio between $t_\\text{min}$ and $t_\\text{max}$ are in the normal group, the ones with ratio below $t_\\text{min}$ in short and the remaining in long. At training time we prepend a length token to each source sentence according to its group ($<$short$>$, $<$normal$>$, or $<$long$>$), in order to let a single network to discriminate between the groups (see Figure FIGREF2). At inference time, the length token is used to bias the network to generate a translation that belongs to the desired length group." + ], + [ + "Inspired by BIBREF11, we use length encoding to provide the network with information about the remaining sentence length during decoding. We propose two types of length encoding: absolute and relative. Let pos and len be, respectively, a token position and the end of the sequence, both expressed in terms of number characters. Then, the absolute approach encodes the remaining length:", + "where $i=1,\\ldots ,d/2$.", + "Similarly, the relative difference encodes the relative position to the end. This representation is made consistent with the absolute encoding by quantizing the space of the relative positions into a finite set of $N$ integers:", + "where $q_N: [0, 1] \\rightarrow \\lbrace 0, 1, .., N\\rbrace $ is simply defined as $q_N(x) = \\lfloor {x \\times N}\\rfloor $. As we are interested in the character length of the target sequence, len and pos are given in terms of characters, but we represent the sequence as a sequence of BPE-segmented subwords BIBREF17. To solve the ambiguity, len is the character length of the sequence, while pos is the character count of all the preceding tokens. We prefer a representation based on BPE, unlike BIBREF11, as it leads to better translations with less training time BIBREF18, BIBREF19. During training, len is the observed length of the target sentence, while at inference time it is the length of the source sentence, as it is the length that we aim to match. The process is exemplified in Figure FIGREF9." + ], + [ + "We further propose to use the two methods together to combine their strengths. In fact, while the length token acts as a soft constraint to bias NMT to produce short or long translation with respect to the source, actually no length information is given to the network. On the other side, length encoding leverages information about the target length, but it is agnostic of the source length." + ], + [ + "Training an NMT model from scratch is a compute intensive and time consuming task. Alternatively, fine-tuning a pre-trained network shows to improve performance in several NMT scenarios BIBREF20, BIBREF21, BIBREF22, BIBREF23, BIBREF24. For our length control approaches, we further propose to use fine-tuning an NMT model with length information, instead of training it from scratch. By adopting a fine-tuning strategy, we specifically aim; i) to decouple the performance of the baseline NMT model from that of the additional length information, ii) control the level of aggressiveness that can come from the data (length token) and the model (length encoding), and iii) make the approaches versatile to any pre-trained model. More importantly, it will allow to transform any NMT model to an output length aware version, while getting better improvements on the quality of the generated sequences." + ], + [ + "Our experiments are run using the English$\\rightarrow $Italian/German portions of the MuST-C corpus BIBREF25, which is extracted from TED talks, using the same train/validation/test split as provided with the corpus (see Table TABREF18). As additional data, we use a mix of public and proprietary data for about 16 million sentence pairs for English-Italian (En-It) and $4.4$ million WMT14 sentence pairs for the English-German (En-De). While our main goal is to verify our hypotheses on a large data condition, thus the need to include proprietary data, for the sake of reproducibility in both languages we also provide results with systems only trained on TED Talks (small data condition). When training on large scale data we use Transformer with layer size of 1024, hidden size of 4096 on feed forward layers, 16 heads in the multi-head attention, and 6 layers in both encoder and decoder. When training only on TED talks, we set layer size of 512, hidden size of 2048 for the feed forward layers, multi-head attention with 8 heads and again 6 layers in both encoder and decoder.", + "In all the experiments, we use the Adam BIBREF26 optimizer with an initial learning rate of $1\\times 10^{-7}$ that increases linearly up to $0.001$ for 4000 warm-up steps, and decreases afterwards with the inverse square root of the training step. The dropout is set to $0.3$ in all layers but the attention, where it is $0.1$. The models are trained with label smoothed cross-entropy with a smoothing factor of $0.1$. Training is performed on 8 Nvidia V100 GPUs, with batches of 4500 tokens per GPU. Gradients are accumulated for 16 batches in each GPU BIBREF27. We select the models for evaluation by applying early stopping based on the validation loss. All texts are tokenized with scripts from the Moses toolkit BIBREF28, and then words are segmented with BPE BIBREF17 with 32K joint merge rules.", + "For evaluation we take the best performing checkpoint on the dev set according to the loss. The size of the data clusters used for the length token method and their corresponding target-source length ratios are reported in Table TABREF19. The value of $N$ of the relative encoding is set to a small value (5), as in preliminary experiments we observed that a high value (100) produces results similar to the absolute encoding." + ], + [ + "We evaluate our Baseline Transformer using two decoding strategies: i) a standard beam search inference (standard), and ii) beam search with length penalty (penalty) set to $0.5$ to favor shorter translations BIBREF29.", + "Length token models are evaluated with three strategies that correspond to the tokens prepended to the source test set at a time (short, normal, and long), and reported as Len-Tok. Length encoding (Len-Enc) models are evaluated in a length matching condition, i.e. output length has to match input length. We report the relative (Rel) and absolute (Abs) strategies of the approach as discussed in Section SECREF10. In the small data condition, we additionally evaluated how the fine-tuning strategy compares with a model trained from scratch. In the large data condition, we added a setting that combines both the length-token and length-encoding strategies." + ], + [ + "To evaluate all models' performance we compute BLEU BIBREF30 with the multi-bleu.perl implementation on the single-reference test sets of the En-It and En-De pairs. Given the absence of multiple references covering different length ratios, we also report n-gram precision scores (BLEU$^*$), by multiplying the BLEU score by the inverse of the brevity penalty BIBREF30. BLEU$^*$ scores is meant to measure to what extent shorter translations are subset of longer translations.", + "The impact on translation lengths is evaluated with the mean sentence-level length ratios between MT output and source (LR$^{src}$) and between MT output and reference (LR$^{ref}$)." + ], + [ + "We performed experiments in two conditions: small data and larger data. In the small data condition we only use the MuST-C training set. In the large data condition, a baseline model is first trained on large data, then it is fine-tuned on the MuST-C training set using the proposed methods. Tables TABREF23 and TABREF26 lists the results for the small and large data conditions. For the two language directions they show BLEU and BLEU* scores, as well as the average length ratios." + ], + [ + "The baselines generate translations longer than the source sentence side, with a length ratio of 1.05 for Italian and 1.11 for German. Decoding with length penalty (penalty) slightly decreases the length ratios but they are still far from our goal of LR$^{src}$=1.00.", + "Fine-tuning. A comparison of the models trained from scratch (central portion of Table TABREF23) with their counterparts fine-tuned from the baseline (last portion of Table TABREF23) shows that the models in the first group generally generate shorter translations, but of worse quality. Additionally, the results with fine-tuning are not much different from the baseline. Existing models can be enhanced to produce shorter sentences, and little variation is observed in their translation quality.", + "Length tokens. Fine-tuning with Len-Tok (Fourth section in Table TABREF23) gives a coarse-grained control over the length, while keeping BLEU scores similar to the baseline or slightly better. Decoding with the token normal leads to translations slightly shorter than the baseline for En-It (LR$^{src}$=1.05 and LR$^{ref}$=1.02), while the token small strongly reduces the translation lengths up to almost the source length (LR$^{src}$=1.01). In the opposite side, the token long generates longer translations which are slightly worse than the others (32.00). A similar behavior is observed for En-De, where the LR$^{src}$ goes from 1.12 to 1.07 when changing normal with short, and to 1.15 with long. The results with the token long are not interesting for our task and are given only for the sake of completeness.", + "Length Encoding. The last section of Table TABREF23 lists the results of using length encoding (Len-Enc) relative (Rel) and absolute (Abs). The two encodings lead to different generated lengths, with Abs being always shorter than Rel. Unfortunately, these improvements in the lengths correspond to a significant degradation in translation quality, mostly due to truncated sentences." + ], + [ + "Our Baselines for the large data condition generate sentences with length ratios over the source comparable to the small data condition (LR$^\\text{src}$ and LR$^\\text{ref}$), but with better translation quality: 35.46 BLEU points for En-It and 33.96 for En-De. Length penalty slightly reduces the length ratios, which results in a 0.3 BLEU points improvement in Italian and -0.3 in German because of the brevity penalty. In the latter case, the BLEU* is slightly better than the standard baseline output. Also for the large data condition, while the length penalty slightly helps to shorten the translations, its effect is minimal and insufficient for our goal.", + "Length tokens. In En-It there is no noticeable difference in translation quality between the tokens normal and short, while there is a degradation of $\\sim 0.7$ points when using long. This last result is consistent with the ones observed before. Also in this case the token short does not degrade the BLEU score, and obtains the highest precision BLEU* with 36.22. In En-De we obtain the best results with token normal (34.46), which matches the length distribution of the references. The token short generates much shorter outputs (LR$^\\text{src}$=1.05), which are also much shorter than the reference (LR$^\\text{ref}=0.93$). Consequently the BLEU score degrades significantly (30.61), and also the BLEU* is 1 point lower than with the token normal. Longer translations can be generated with the token long, but they always come at the expense of lower quality.", + "Length encoding. For En-It, Len-Enc Rel in Table TABREF26 achieves a LR$^\\text{src}$ of 1.01 with a slight degradation of $0.3$ BLEU points over the baseline, while in the case of Abs the degradation is higher (-1.6) and LR$^\\text{src}$ is similar (1.02). Also in En-De the degradation of Rel over the baseline is only -0.3, but the reduction in terms of LR$^\\text{src}$ is very small (1.11 vs 1.13). On the other side, Abs produces much shorter translations (1.03 LR$^\\text{src}$) at the expense of a significantly lower BLEU score (30.79). When computing the BLEU* score, the absolute encoding is only 0.45 points lower than the relative encoding (33.29 vs 33.74), but -0.8 lower than the baseline.", + "Token + Encoding. So far, we have observed generally good results using the token method and translating with the tokens short and normal. while the length encoding generally produces a more predictable output length, in particular for the absolute variant. In the last experiment, we combine the two methods in order to have a system that can capture different styles (short, normal, long), as well as explicitly leveraging length information. The results listed in the last portion of Table TABREF26 (Tok+Enc) show that the relative encoding Rel produces better translations than Abs, but again it has less predictability in output length. For instance, in En-It the LR$^\\text{src}$ of Rel is 0.96 with token short and 1.02 with normal, while for En-De it is 1.01 with short and 1.08 with normal. On the other side, the Abs produces LR$^\\text{src}$ of 1.01 with both tokens in En-It and also with short in En-De, and it increases to only 1.03 with normal.", + "Controlling output length. In order to achieve LR$^\\text{src}$ as close as possible to 1.0, we set the target length during generation equal to the source length when using the length encoding methods. However, one advantage of length encoding is the possibility to set the target length to modify the average output length. We illustrate this option by using the Tok+Enc Rel system for En-It, and translating with the tokens normal or short and different scaling factors for the target length. The results, listed in Table TABREF27, show that we are able to approach an LR$^{src}$ of 1.0 with both tokens and the BLEU score is not affected with token normal (35.45) or improves with token short (35.11).", + "Discussion. Length token is an effective approach to generate translations of different lengths, but it does not allow a fine-grained control of the output lengths and its results depend on the partition of the training set into groups, which is a manual process. Length encoding allows to change the output length, but the two variants have different effects. Absolute encoding is more accurate but generates sentences with missing information. The relative encoding produces better translations than the absolute encoding, but its control over the translation length is more loose. The increased length stability is captured by the standard deviation of the length ratio with the source, which is $0.14$ for length tokens, $\\sim 0.11$ for relative encoding and $\\sim 0.07$ for absolute encoding. The advantage of the combined approach is that it can generate sentences with different style to fit different length groups, and the output length can also be tuned by modifying the target length, while no important quality degradation is observed. Additionally, the standard deviation of the lengths is the same as for the length encoding used." + ], + [ + "After manually inspecting the outputs of the best performing models under the large data condition, we decided to run a human evaluation only for the En-It Len-Tok model. As our ultimate goal is to be able to generate shorter translations and as close as possible to the length of the source sentences, we focused the manual evaluation on the Short output class and aimed to verify possible losses in quality with respect to the baseline system. We ran a head-to-head evaluation on the first 10 sentences of each test talk, for a total of 270 sentences, by asking annotators to blindly rank the two system outputs (ties were also permitted) in terms of quality with respect to a reference translation. We collected three judgments for each output, from 19 annotators, for a total of 807 scores (one sentence had to be discarded). Inter-annotator agreement measured with Fleiss' kappa was 0.35 (= fair agreement). Results reported in Table TABREF32 confirm the small differences observed in BLEU scores: there are only a 4% more wins for the Baseline and almost 60% of ties. The small degradation in quality of the shorter translations is statistically significant ($p<0.05$), as well as their difference in length ($p<0.001$).", + "Notice that the evaluation was quite severe towards the shorter translations, as even small changes of the meaning could affect the ranking. After the manual evaluation, we analyzed sentences in which shorter translations were unanimously judged equal or better than the standard translations. We hence tried to identify the linguistic skills involved in the generation of shorter translations, namely: (i) use of abbreviations, (ii) preference of simple verb tenses over compound tenses, (iii) avoidance of not relevant adjective, adverbs, pronouns and articles, (iv) use of paraphrases. Table TABREF33 shows examples of the application of the above strategies as found in the test set." + ], + [ + "As an integration of Section 2, we try to provide a more complete picture on previous work with seq-to-seq models to control the output length for text summarization, and on the use of tokens to bias in different ways the output of NMT.", + "In text summarization, BIBREF8 proposed methods to control output length either by modifying the search process or the seq-to-seq model itself, showing that the latter being more promising. BIBREF9 addressed the problem similarly to our token approach, by training the model on data bins of homogeneous output length and conditioning the output on a length token. They reported better performance than BIBREF8. Finally, BIBREF11 proposed the extension of the positional encoding of the transformer (cf. Section 2), reporting better performance than BIBREF8 and BIBREF9.", + "The use of tokens to condition the output of NMT started with the multilingual models BIBREF15, BIBREF16, and was then further applied to control the use of the politeness form in English-German NMT BIBREF32, in the translation from English into different varieties of the same language BIBREF33, for personalizing NMT to user gender and vocabulary BIBREF34, and finally to perform NMT across different translation styles BIBREF35." + ], + [ + "In this paper, we have proposed two solutions for the problem of controlling the output length of NMT. A first approach, inspired by multilingual NMT, allows a coarse-grained control over the length and no degradation in translation quality. A second approach, inspired by positional encoding, enables a fine-grained control with only a small error in the token count, but at the cost of a lower translation quality. A manual evaluation confirms the translation quality observed with BLEU score. In future work, we plan to design more flexible and context-aware evaluations which allow us to account for short translations that are not equivalent to the original but at the same time do not affect the overall meaning of the discourse." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0828/instruction.md b/qasper-0828/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..a97e7c86b86e7330717647dcf881ae57d873c797 --- /dev/null +++ b/qasper-0828/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Spectral decomposition method of dialog state tracking via collective matrix factorization + +Question: What state-of-the-art models are compared against? \ No newline at end of file diff --git a/qasper-0843/instruction.md b/qasper-0843/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..e961cf930fcb140618f981b3b1ce2344a1c13c9e --- /dev/null +++ b/qasper-0843/instruction.md @@ -0,0 +1,123 @@ +Name of Paper: CRWIZ: A Framework for Crowdsourcing Real-Time Wizard-of-Oz Dialogues + +Question: How does framework made sure that dialogue will not breach procedures? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work", + "System Overview", + "Data Collection", + "Data Collection ::: Implementation", + "Data Collection ::: Deployment", + "Data Analysis", + "Data Analysis ::: Subjective Data", + "Data Analysis ::: Single vs Multiple Wizards", + "Data Analysis ::: Limitations", + "Data Analysis ::: Future Work", + "Conclusion", + "Acknowledgements" + ], + "paragraphs": [ + [ + "Recent machine learning breakthroughs in dialogue systems and their respective components have been made possible by training on publicly available large scale datasets, such as ConvAI BIBREF0, bAbI BIBREF1 and MultiWoZ BIBREF2, many of which are collected on crowdsourcing services, such as Amazon Mechanical Turk and Figure-eight. These data collection methods have the benefits of being cost-effective, time-efficient to collect and scalable, enabling the collection of large numbers of dialogues.", + "Where this crowdsourcing method has its limitations is when specific domain expert knowledge is required, rather than general conversation. These tasks include, for example, call centre agents BIBREF3 or clerks with access to a database, as is required for tourism information and booking BIBREF2. In the near future, there will be a demand to extend this to workplace-specific tasks and procedures. Therefore, a method of gathering crowdsourced dialogue data is needed that ensures compliance with such procedures, whilst providing coverage of a wide variety of dialogue phenomena that could be observed in deployment of a trained dialogue system.", + "Wizard-of-Oz data collections in the past have provided such a mechanism. However, these have traditionally not been scalable because of the scarcity of Wizard experts or the expense to train up workers. This was the situation with an initial study reported in BIBREF4, which was conducted in a traditional lab setting and where the Wizard (an academic researcher) had to learn, through training and reading manuals, how best to perform operations in our domain of emergency response.", + "We present the CRWIZ Intelligent Wizard Interface that enables a crowdsourced Wizard to make intelligent, relevant choices without such intensive training by providing a restricted list of valid and relevant dialogue task actions, which changes dynamically based on the context, as the interaction evolves.", + "Prior crowdsourced wizarded data collections have divided the dialogue up into turns and each worker's job consists of one turn utterance generation given a static dialogue context, as in the MultiWoZ dataset BIBREF2. However, this can limit naturalness of the dialogues by restricting forward planning, collaboration and use of memory that humans use for complex multi-stage tasks in a shared dynamic environment/context.", + "Our scenario is such a complex task. Specifically, our scenario relates to using robotics and autonomous systems on an offshore energy platform to resolve an emergency and is part of the EPSRC ORCA Hub project BIBREF5. The ORCA Hub vision is to use teams of robots and autonomous intelligent systems to work on offshore energy platforms to enable cheaper, safer and more efficient working practices. An important part of this is ensuring safety of robots in complex, dynamic and cluttered environments, co-operating with remote operators. With this data collection method reported here, we aim to automate a conversational Intelligent Assistant (Fred), who acts as an intermediary between the operator and the multiple robotic systems BIBREF6, BIBREF7. Emergency response is clearly a high-stakes situation, which is difficult to emulate in a lab or crowdsourced data collection environment. Therefore, in order to foster engagement and collaboration, the scenario was gamified with a monetary reward given for task success.", + "In this paper, we provide a brief survey of existing datasets and describe the CRWIZ framework for pairing crowdworkers and having half of them acting as Wizards by limiting their dialogue options only to relevant and plausible ones, at any one point in the interaction. We then perform a data collection and compare our dataset to a similar dataset collected in a more controlled lab setting with a single Wizard BIBREF4 and discuss the advantages/disadvantages of both approaches. Finally, we present future work. Our contributions are as follows:", + "The release of a platform for the CRWIZ Intelligent Wizard Interface to allow for the collection of dialogue data for longer complex tasks, by providing a dynamic selection of relevant dialogue acts.", + "A survey of existing datasets and data collection platforms, with a comparison to the CRWIZ data collection for Wizarded crowdsourced data in task-based interactions." + ], + [ + "Table TABREF3 gives an overview of prior work and datasets. We report various factors to compare to the CRWIZ dataset corresponding to columns in Table TABREF3: whether or not the person was aware they were talking to a bot; whether each dialogue had a single or multiple participants per role; whether the data collection was crowdsourced; and the modality of the interaction and the domain. As we see from the bottom row, none of the datasets reported in the table meet all the criteria we are aiming for, exemplifying the need for a new and novel approach.", + "Collecting large amounts of dialogue data can be very challenging as two interlocutors are required to create a conversation. If one of the partners in the conversation is a machine as in BIBREF0, the challenge becomes slightly easier since only one partner is lacking. However, in most cases these datasets are aimed at creating resources to train the conversational system itself. Self-authoring the dialogues BIBREF16 or artificially creating data BIBREF1 could be a solution to rapidly collect data, but this solution has been shown to produce low quality unnatural data BIBREF17.", + "One way to mitigate the necessity of pairing two users simultaneously is to allow several participants to contribute to the dialogue, one turn at the time. This approach has been used both in task-oriented BIBREF10, BIBREF2, BIBREF9 and chitchat BIBREF17. This means that the same dialogue can be authored by several participants. However, this raises issues in terms of coherence and forward-planning. These can be addressed by carefully designing the data collection to provide the maximum amount of information to the participants (e.g. providing the task, personality traits of the bot, goals, etc.) but then this adds to cognitive load, time, cost and participant fatigue.", + "Pairing is a valid option, which has been used in a number of recent data collections in various domains, such as navigating in a city BIBREF13, playing a negotiation game BIBREF14, talking about a person BIBREF18, playing an image game BIBREF8 or having a chat about a particular image that is shown to both participants BIBREF21, BIBREF22. Pairing frameworks exist such as Slurk BIBREF23. Besides its pairing management feature, Slurk is designed in order to allow researchers to modify it and implement their own data collection rapidly.", + "The scenarios for the above-mentioned data collections are mostly intuitive tasks that humans do quite regularly, unlike our use-case scenario of emergency response. Role playing is one option. For example, recent work has tried to create datasets for non-collaborative scenarios BIBREF24, BIBREF25, requesting participants to incarnate a particular role during the data collection. This is particularly challenging when the recruitment is done via a crowdsourcing platform. In BIBREF25, the motivation for the workers to play the role is intrinsic to the scenario. In this data collection, one of the participants tries to persuade their partner to contribute to a charity with a certain amount of money. As a result of their dialogue, the money that the persuadee committed to donate was actually donated to a charity organising. However, for scenarios such as ours, the role playing requires a certain expertise and it is questionable whether the desired behaviour would be achieved simply by letting two non-experts converse with free text.", + "Therefore, in recent data collections, there have been a number of attempts to control the data quality in order to produce a desired behaviour. For example, in BIBREF15, the data collection was done with a limited number of subjects who performed the task several days in a row, behaving both as the Wizard and the customer of a travel agency. The same idea was followed in BIBREF12, where a number of participants took part in the data collection over a period of 6 months and, in BIBREF3, BIBREF19 where a limited number of subjects were trained to be the Wizard. This quality control, however, naturally comes with the cost of recruiting and paying these subjects accordingly.", + "The solution we propose in this paper tries to minimise these costs by increasing the pool of Wizards to anyone wanting to collaborate in the data collection, by providing them the necessary guidance to generate the desired dialogue behaviour. This is a valuable solution for collecting dialogues in domains where specific expertise is required and the cost of training capable Wizards is high. We required fine-grained control over the Wizard interface so as to be able to generate more directed dialogues for specialised domains, such as emergency response for offshore facilities. By providing the Wizard with several dialogue options (aside from free text), we guided the conversation and could introduce actions that change an internal system state. This proposes several advantages:", + "A guided dialogue allows for set procedures to be learned and reduces the amount of data needed for a machine learning model for dialogue management to converge.", + "Providing several dialogue options to the Wizard increases the pace of the interaction and allows them to understand and navigate more complex scenarios." + ], + [ + "The CRWIZ Intelligent Wizard Interface resides on Slurk BIBREF23, an interaction server built for conducting dialogue experiments and data collections. Slurk handles the pairing of participants and provides a basic chat layout amongst other features. Refer to BIBREF23 for more information on the pairing of participants and the original chat layout. Our chat layout remains similar to Slurk with an important difference. In our scenario, we assign each new participant a role (Operator or Wizard) and, depending on this role, the participant sees different game instructions and chat layout schemes. These are illustrated in Figures FIGREF8 and FIGREF11, for the Operator and Wizard respectively. The main components are described in turn below: 1) The Intelligent Wizard Interface; 2) dialogue structure; and 3) system-changing actions.", + "Wizard interface: the interface shown to participants with the Wizard role provides possible actions on the right-hand side of the browser window. These actions could be verbal, such as sending a message, or non-verbal, such as switching on/off a button to activate a robot. Figure FIGREF11 shows this interface with several actions available to be used in our data collection.", + "Dialogue structure: we introduced structured dialogues through a Finite State Machine (FSM) that controls the current dialogue state and offers multiple suitable and relevant state transitions (actions) to the Wizard depending on the point in the interaction, the state of the world and the history. A graph of dialogue states, transitions and utterances is loaded when the system is initialised, and each chat room has its own dialogue state, which changes through actions.", + "The CRWIZ framework is domain-agnostic, but the data collected with it corresponds to the emergency response domain.", + "System-changing actions: actions trigger transitions between the states in the FSM. We differentiate two types of actions:", + "Verbal actions, such as the dialogue options available at that moment. The Wizard can select one of several predefined messages to send, or type their own message if needed. Free text messages do not change the dialogue state in the FSM, so it is important to minimise their use by providing enough dialogue options to the Wizard. Predefined messages can also trigger other associated events such as pop-ups or follow-up non-verbal actions.", + "Non-verbal actions, such as commands to trigger events. These can take any form, but we used buttons to control robots in our data collection.", + "Submitting an action would change the dialogue state in the FSM, altering the set of actions available in the subsequent turn visible to the Wizard. Some dialogue options are only possible at certain states, in a similar way as to how non-verbal actions are enabled or disabled depending on the state. This is reflected in the Wizard interface.", + "The advantage of the CRWIZ framework is that it can easily be adapted to different domains and procedures by simply modifying the dialogue states loaded at initialisation. These files are in YAML format and have a simple structure that defines their NLG templates (the FSM will pick one template at random if there is more than one) and the states that it can transition to. Note, that some further modifications may be necessary if the scenario is a slot-filling dialogue requiring specific information at various stages.", + "Once the dialogue between the participants finishes, they receive a code in the chat, which can then be submitted to the crowdsourcing platform for payment. The CRWIZ framework generates a JSON file in its log folder with all the information regarding the dialogue, including messages sent, FSM transitions, world state at each action, etc. Automatic evaluation metrics and annotations are also appended such as number of turns per participant, time taken or if one of the participants disconnected. Paying the crowdworkers can be done by just checking that there is a dialogue file with the token that they entered." + ], + [ + "We set up a crowdsourced data collection through Amazon Mechanical Turk, in which two participants chatted with each other in a setting involving an emergency at an offshore facility. As mentioned above, participants had different roles during the interaction: one of them was an Operator of the offshore facility whereas the other one acted as an Intelligent Emergency Assistant. Both of them had the same goal of resolving the emergency and avoiding evacuation at all costs, but they had different functions in the task:", + "The Operator was responsible for the facility and had to give instructions to the Emergency Assistant to perform certain actions, such as deploying emergency robots. Participants in the role of Operator were able to chat freely with no restrictions and were additionally given a map of the facility and a list of available robots (see Figure FIGREF8).", + "The Emergency Assistant had to help the Operator handle the emergency by providing guidance and executing actions. Participants in the role of Emergency Assistant had predefined messages depending on the task progress. They had to choose between one of the options available, depending on which made sense at the time, but they also had the option to write their own message if necessary. The Emergency Assistant role mimics that of the Wizard in a Wizard-of-Oz experiment (see Figure FIGREF11).", + "The participants had a limited time of 6 minutes to resolve the emergency, which consisted of the following sub-tasks: 1) identify and locate the emergency; 2) resolve the emergency; and 3) assess the damage caused. They had four robots available to use with different capabilities: two ground robots with wheels (Husky) and two Quadcopter UAVs (Unmanned Aerial Vehicles). For images of these robots, see Figure FIGREF8. Some robots could inspect areas whereas others were capable of activating hoses, sprinklers or opening valves. Both participants, regardless of their role, had a list with the robots available and their capabilities, but only the Emergency Assistant could control them. This control was through high-level actions (e.g. moving a robot to an area, or ordering the robot to inspect it) that the Emergency Assistant had available as buttons in their interface, as shown in Figure FIGREF11. For safety reasons that might occur in the real world, only one robot could be active doing an action at any time. The combinations of robots and capabilities meant that there was not a robot that could do all three steps of the task mentioned earlier (inspect, resolve and assess damage), but the robots could be used in any order allowing for a variety of ways to resolve the emergency.", + "Participants would progress through the task when certain events were triggered by the Emergency Assistant. For instance, inspecting the area affected by an alarm would trigger the detection of the emergency. After locating the emergency, other dialogue options and commands would open up for the Emergency Assistant. In order to give importance to the milestones in the dialogue, these events were also signalled by GIFs (short animated video snippets) in the chat that both participants could see (e.g. a robot finding a fire), as in Figure FIGREF12. The GIFs were added for several reasons: to increase participant engagement and situation awareness, to aid in the game and to show progress visually. Note that there was no visual stimuli in the original WoZ study BIBREF4 but they were deemed necessary here to help the remote participants contextualise the scenario. These GIFs were produced using a Digital Twin simulation of the offshore facility with the various types of robots. See BIBREF26 for details on the Digital Twin." + ], + [ + "The dialogue structure for the Emergency Assistant (the Wizard) followed a dialogue flow previously used for the original lab-based Wizard-of-Oz study BIBREF4 but which was slightly modified and simplified for this crowdsourced data collection. In addition to the transitions that the FSM provides, there are other fixed dialogue options always available such as \u201cHold on, 2 seconds\u201d, \u201cOkay\u201d or \u201cSorry, can you repeat that?\u201d as a shortcut for commonly used dialogue acts, as well as the option to type a message freely.", + "The dialogue has several paths to reach the same states with varying levels of Operator control or engagement that enriched the heterogeneity of conversations. The Emergency Assistant dialogue options show various speaking styles, with a more assertive tone (\u201cI am sending Husky 1 to east tower\u201d) or others with more collaborative connotations (\u201cWhich robot do you want to send?\u201d or \u201cHusky 1 is available to send to east tower\u201d). Refer to BIBREF4 for more details. Furthermore, neither participants were restricted in the number of messages that they could send and we did not require a balanced number of turns between them. However, there were several dialogue transitions that required an answer or authorisation from the Operator, so the FSM would lock the dialogue state until the condition was met. As mentioned earlier, the commands to control the robots are also transitions of the FSM, so they were not always available.", + "The Emergency Assistant interface contains a button to get a hint if they get stuck at any point of the conversation. This hint mechanism, when activated, highlights one of the possible dialogue options or robot buttons. This highlighted transition was based on the observed probability distribution of transitions from BIBREF4 to encourage more collaborative interaction than a single straight answer.", + "As in the real world, robot actions during the task were simulated to take a certain period of time, depending on the robot executing it and the action. The Emergency Assistant had the option to give status updates and progress reports during this period. Several dialogue options were available for the Emergency Assistant whilst waiting. The time that robots would take to perform actions was based on simulations run on a Digital Twin of the offshore facility implemented in Gazebo BIBREF26. Specifically, we pre-simulated typical robot actions, with the robot's progress and position reflected in the Wizard interface with up-to-date dialogue options for the Emergency Assistant. Once the robot signals the end of their action, additional updated dialogue options and actions are available for the Emergency Assistant. This simulation allowed us to collect dialogues with a realistic embedded world state." + ], + [ + "We used Amazon Mechanical Turk (AMT) for the data collection. We framed the task as a game to encourage engagement and interaction. The whole task, (a Human Intelligence Task (HIT) in AMT) consisted of the following:", + "Reading an initial brief set of instructions for the overall task.", + "Waiting for a partner for a few seconds before being able to start the dialogue.", + "When a partner was found, they were shown the instructions for their assigned role. As these were different, we ensured that they both took around the same time. The instructions had both a text component and a video explaining how to play, select dialogues, robots, etc.", + "Playing the game to resolve the emergency. This part was limited to 6 minutes.", + "Filling a post-task questionnaire about partner collaboration and task ease.", + "The participants received a game token after finishing the game that would allow them to complete the questionnaire and submit the task. This token helped us link their dialogue to the responses from the questionnaire.", + "Several initial pilots helped to define the total time required as 10 minutes for all the steps above. We set the HIT in AMT to last 20 minutes to allow additional time should any issues arise. The pilots also helped setting the payment for the workers. Initially, participants were paid a flat amount of $1.4 per dialogue. However, we found that offering a tiered payment tied to the length of the dialogue and bonus for completing the task was the most successful and cost-effective method to foster engagement and conversation:", + "$0.5 as base for attempting the HIT, reading the instructions and completing the questionnaire.", + "$0.15 per minute during the game, for a maximum of $0.9 for the 6 minutes.", + "$0.2 additional bonus if the participants were able to successfully avoid the evacuation of the offshore facility.", + "The pay per worker was therefore $1.4 for completing a whole dialogue and $1.6 for those who resolved the emergency for a 10-minute HIT. This pay is above the Federal minimum wage in the US ($7.25/hr or $0.12/min) at the time of the experiment.", + "The post-task questionnaire had four questions rated in 7-point rating scales that are loosely based on the PARADISE BIBREF27 questions for spoken dialogue systems:", + "Partner collaboration: \u201cHow helpful was your partner?\u201d on a scale of 1 (not helpful at all) to 7 (very helpful).", + "Information ease: \u201cIn this conversation, was it easy to get the information that I needed?\u201d on a scale of 1 (no, not at all) to 7 (yes, completely).", + "Task ease: \u201cHow easy was the task?\u201d on a scale of 1 (very easy) to 7 (very difficult).", + "User expertise: \u201cIn this conversation, did you know what you could say or do at each point of the dialog?\u201d on a scale of 1 (no, not at all) to 7 (yes, completely).", + "At the end, there was also an optional entry to give free text feedback about the task and/or their partner." + ], + [ + "For the intitial data collection using the CRWIZ platform, 145 unique dialogues were collected (each dialogue consists of a conversation between two participants). All the dialogues were manually checked by one of the authors and those where the workers were clearly not partaking in the task or collaborating were removed from the dataset. The average time per assignment was 10 minutes 47 seconds, very close to our initial estimate of 10 minutes, and the task was available for 5 days in AMT. Out of the 145 dialogues, 14 (9.66%) obtained the bonus of $0.2 for resolving the emergency. We predicted that only a small portion of the participants would be able to resolve the emergency in less than 6 minutes, thus it was framed as a bonus challenge rather than a requirement to get paid. The fastest time recorded to resolve the emergency was 4 minutes 13 seconds with a mean of 5 minutes 8 seconds. Table TABREF28 shows several interaction statistics for the data collected compared to the single lab-based WoZ study BIBREF4." + ], + [ + "Table TABREF33 gives the results from the post-task survey. We observe, that subjective and objective task success are similar in that the dialogues that resolved the emergency were rated consistently higher than the rest.", + "Mann-Whitney-U one-tailed tests show that the scores of the Emergency Resolved Dialogues for Q1 and Q2 were significantly higher than the scores of the Emergency Not Resolved Dialogues at the 95% confidence level (Q1: $U = 1654.5$, $p < 0.0001$; Q2: $U = 2195$, $p = 0.009$, both $p < 0.05$). This indicates that effective collaboration and information ease are key to task completion in this setting.", + "Regarding the qualitative data, one of the objectives of the Wizard-of-Oz technique was to make the participant believe that they are interacting with an automated agent and the qualitative feedback seemed to reflect this: \u201cThe AI in the game was not helpful at all [...]\u201d or \u201cI was talking to Fred a bot assistant, I had no other partner in the game\u201c." + ], + [ + "In Table TABREF28, we compare various metrics from the dialogues collected with crowdsourcing with the dialogues previously collected in a lab environment for a similar task. Most figures are comparable, except the number of emergency assistant turns (and consequently the total number of turns). To further understand these differences, we have first grouped the dialogue acts in four different broader types: Updates, Actions, Interactions and Requests, and computed the relative frequency of each of these types in both data collections. In addition, Figures FIGREF29 and FIGREF30 show the distribution of the most frequent dialogue acts in the different settings. It is visible that in the lab setting where the interaction was face-to-face with a robot, the Wizard used more Interaction dialogue acts (Table TABREF32). These were often used in context where the Wizard needed to hold the turn while looking for the appropriate prompt or waiting for the robot to arrive at the specified goal in the environment. On the other hand, in the crowdsourced data collection utterances, the situation updates were a more common choice while the assistant was waiting for the robot to travel to the specified goal in the environment.", + "Perhaps not surprisingly, the data shows a medium strong positive correlation between task success and the number of Action type dialogue acts the Wizard performs, triggering events in the world leading to success ($R=0.475$). There is also a positive correlation between task success and the number of Request dialogue acts requesting confirmation before actions ($R=0.421$), e.g., \u201cWhich robot do you want to send?\u201d. As Table 3 shows, these are relatively rare but perhaps reflect a level of collaboration needed to further the task to completion. Table TABREF40 shows one of the dialogues collected where the Emergency Assistant continuously engaged with the Operator through these types of dialogue acts.", + "The task success rate was also very different between the two set-ups. In experiments reported in BIBREF4, 96% of the dialogues led to the extinction of the fire whereas in the crowdsourcing setting only 9.66% achieved the same goal. In the crowdsourced setting, the robots were slower moving at realistic speeds unlike the lab setting. A higher bonus and more time for the task might lead to a higher task success rate." + ], + [ + "It is important to consider the number of available participants ready and willing to perform the task at any one time. This type of crowdsourcing requires two participants to connect within a few minutes of each other to be partnered together. As mentioned above, there were some issues with participants not collaborating and these dialogues had to be discarded as they were not of use." + ], + [ + "In future work, we want to expand and improve the platform. Dialogue system development can greatly benefit from better ways of obtaining data for rich task-oriented domains such as ours. Part of fully exploiting the potential of crowdsourcing services lies in having readily available tools that help in the generation and gathering of data. One such tool would be a method to take a set of rules, procedures or business processes and automatically convert to a FSM, in a similar way to BIBREF28, ready to be uploaded to the Wizard interface.", + "Regarding quality and coherence, dialogues are particularly challenging to automatically rate. In our data collection, there was not a correct or wrong dialogue option for the messages that the Emergency Assistant sent during the conversation, but some were better than others depending on the context with the Operator. This context is not easily measurable for complex tasks that depend on a dynamic world state. Therefore, we leave to future work automatically measuring dialogue quality through the use of context.", + "The introduction of Instructional Manipulation Checks BIBREF29 before the game to filter out inattentive participants could improve the quality of the data (Crowdworkers are known for performing multiple tasks at once). Goodman2013 also recommend including screening questions that check both attention and language comprehension for AMT participants. Here, there is a balance that needs to be investigated between experience and quality of crowdworkers and the need for large numbers of participants in order to be quickly paired.", + "We are currently exploring using the data collected to train dialogue models for the emergency response domain using Hybrid Code Networks BIBREF30." + ], + [ + "In conclusion, this paper described a new, freely available tool to collect crowdsourced dialogues in rich task-oriented settings. By exploiting the advantages of both the Wizard-of-Oz technique and crowdsourcing services, we can effortlessly obtain dialogues for complex scenarios. The predefined dialogue options available to the Wizard intuitively guide the conversation and allow the domain to be deeply explored without the need for expert training. These predefined options also reinforce the feeling of a true Wizard-of-Oz experiment, where the participant who is not the Wizard thinks that they are interacting with a non-human agent.", + "As the applications for task-based dialogue systems keep growing, we will see the need for systematic ways of generating dialogue corpora in varied, richer scenarios. This platform aims to be the first step towards the simplification of crowdsourcing data collections for task-oriented collaborative dialogues where the participants are working towards a shared common goal. The code for the platform and the data are also released with this publication." + ], + [ + "This work was supported by the EPSRC funded ORCA Hub (EP/R026173/1, 2017-2021). Chiyah Garcia's PhD is funded under the EPSRC iCase EP/T517471/1 with Siemens." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0844/instruction.md b/qasper-0844/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..507a3f9569db12addcdcbb8550166282139f6338 --- /dev/null +++ b/qasper-0844/instruction.md @@ -0,0 +1,123 @@ +Name of Paper: Detecting Online Hate Speech Using Context Aware Models + +Question: How do they combine the models? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Works", + "Corpus Overview", + "Annotation Guidelines", + "Annotation Procedure", + "Characteristics in Fox News User Comments corpus", + "Logistic Regression Models", + "Neural Network Models", + "Ensemble Models", + "Evaluation", + "Experimental Results", + "Conclusion" + ], + "paragraphs": [ + [ + "Following a turbulent election season, 2016's cyber world is awash with hate speech. Automatic detection of hate speech has become an urgent need since human supervision is unable to deal with large quantities of emerging texts.", + "Context information, by our definition, is the text, symbols or any other kind of information related to the original text. While intuitively, context accompanying hate speech is useful for detecting hate speech, context information of hate speech has been overlooked in existing datasets and automatic detection models.", + "Online hate speech tends to be subtle and creative, which makes context especially important for automatic hate speech detection. For instance,", + "", + "(1) barryswallows: Merkel would never say NO", + "", + "This comment is posted for the News titled by \"German lawmakers approve 'no means no' rape law after Cologne assaults\". With context, it becomes clear that this comment is a vicious insult towards female politician. However, almost all the publicly available hate speech annotated datasets do not contain context information. BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 .", + "We have created a new dataset consisting of 1528 Fox News user comments, which were taken from 10 complete discussion threads for 10 widely read Fox News articles. It is different from previous datasets from the following two perspectives. First, it preserves rich context information for each comment, including its user screen name, all comments in the same thread and the news article the comment is written for. Second, there is no biased data selection and all comments in each news comment thread were annotated.", + "In this paper, we explored two types of models, feature-based logistic regression models and neural network models, in order to incorporate context information in automatic hate speech detection. First, logistic regression models have been used in several prior hate speech detection studies BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF0 , BIBREF2 , BIBREF9 and various features have been tried including character-level and word-level n-gram features, syntactic features, linguistic features, and comment embedding features. However, all the features were derived from the to-be-classified text itself. In contrast, we experiment with logistic regression models using features extracted from context text as well. Second, neural network models BIBREF10 , BIBREF11 , BIBREF12 have the potential to capture compositional meanings of text, but they have not been well explored for online hate speech detection until recently BIBREF13 . We experiment with neural net models containing separate learning components that model compositional meanings of context information. Furthermore, recognizing unique strengths of each type of models, we build ensemble models of the two types of models. Evaluation shows that context-aware logistic regression models and neural net models outperform their counterparts that are blind with context information. Especially, the final ensemble models outperform a strong baseline system by around 10% in F1-score." + ], + [ + "Recently, a few datasets with human labeled hate speech have been created, however, most of existing datasets do not contain context information. Due to the sparsity of hate speech in everyday posts, researchers tend to sample candidates from bootstrapping instead of random sampling, in order to increase the chance of seeing hate speech. Therefore, the collected data instances are likely to be from distinct contexts.", + "For instance, in the Primary Data Set described in BIBREF14 and later used by BIBREF9 , 10% of the dataset is randomly selected while the remaining consists of comments tagged by users and editors. BIBREF15 built a balanced data set of 24.5k tweets by selecting from Twitter accounts that claimed to be racist or were deemed racist using their followed news sources. BIBREF5 collected hateful tweets related to the murder of Drummer Lee Rigby in 2013. BIBREF0 provided a corpus of 16k annotated tweets in which 3.3k are labeled as sexist and 1.9k are labeled as racist. They created this corpus by bootstrapping from certain key words ,specific hashtags and certain prolific users. BIBREF16 created a dataset of 9000 human labeled paragraphs that were collected using regular expression matching in order to find hate speech targeting Judaism and Israel. BIBREF7 extracted data instances from instagram that were associated with certain user accounts. BIBREF2 presented a very large corpus containing over 115k Wikipedia comments that include around 37k randomly sampled comments and the remaining 78k comments were selected from Wikipedia blocked comments.", + "Most of existing hate speech detection models are feature-based and use features derived from the target text itself. BIBREF5 experimented with different classification methods including Bayesian Logistic Regression, Random Forest Decision Trees and SVMs, using features such as n-grams, reduced n-grams, dependency paths, and hateful terms. BIBREF0 proposed a logistic regression model using character n-gram features. BIBREF14 used the paragraph2vec for joint modeling of comments and words, then the generated embeddings were used as feature in a logistic regression model. BIBREF9 experimented with various syntactic, linguistic and distributional semantic features including word length, sentence length, part of speech tags, and embedding features, in order to improve performance of logistic regression classifiers. Recently, BIBREF17 surveyed current approaches for hate speech detection, which interestingly also called to attention on modeling context information for resolving difficult hate speech instances." + ], + [ + "The Fox News User Comments corpus consists of 1528 annotated comments (435 labeled as hateful) that were posted by 678 different users in 10 complete news discussion threads in the Fox News website. The 10 threads were manually selected and represent popular discussion threads during August 2016. All of the comments included in these 10 threads were annotated. The number of comments in each of the 10 threads is roughly equal. Rich context information was kept for each comment, including its user screen name, the comments and their nested structure and the original news article. The data corpus along with annotation guidelines is posted on github." + ], + [ + "Our annotation guidelines are similar to the guidelines used by BIBREF9 . We define hateful speech to be the language which explicitly or implicitly threatens or demeans a person or a group based upon a facet of their identity such as gender, ethnicity, or sexual orientation. The labeling of hateful speech in our corpus is binary. A comment will be labeled as hateful or non-hateful." + ], + [ + "We identified two native English speakers for annotating online user comments. The two annotators first discussed and practices before they started annotation. They achieved a surprisingly high Kappa score BIBREF18 of 0.98 on 648 comments from 4 threads. We think that thorough discussions in the training stage is the key for achieving this high inter-agreement. For those comments which annotators disagreed on, we label them as hateful as long as one annotator labeled them as hateful. Then one annotator continued to annotate the remaining 880 comments from the remaining six discussion threads." + ], + [ + "Hateful comments in the Fox News User Comments Corpus is often subtle, creative and implicit. Therefore, context information is necessary in order to accurately identify such hate speech.", + "The hatefulness of many comments depended on understanding their contexts. For instance,", + "", + "(3) mastersundholm: Just remember no trabjo no cervesa", + "", + "This comment is posted for the news \"States moving to restore work requirements for food stamp recipients\". This comment implies that Latino immigrants abuse the usage of food stamp policy, which is clearly a stereotyping.", + "Many hateful comments use implicit and subtle language, which contain no clear hate indicating word or phrase. In order to recognize such hard cases, we hypothesize that neural net models are more suitable by capturing overall composite meanings of a comment. For instance, the following comment is a typical implicit stereotyping against women.", + "", + "(4) MarineAssassin: Hey Brianne - get in the kitchen and make me a samich. Chop Chop", + "", + "11% of our annotated comments have more than 50 words each. In such long comments, the hateful indicators usually appear in a small region of a comment while the majority of the comment is neutral. For example,", + "", + "(5) TMmckay: I thought ...115 words... Too many blacks winning, must be racist and needs affirmative action to make whites equally win! ", + "", + "Certain user screen names indicate hatefulness, which imply that comments posted by these users are likely to contain hate speech. In the following example, commie is a slur for communists.", + "", + "(6)nocommie11: Blah blah blah. Israel is the only civilized nation in the region to keep the unwashed masses at bay.", + "" + ], + [ + "In logistic regression models, we extract four types of features, word-level and character-level n-gram features as well as two types of lexicon derived features. We extract these four types of features from the target comment first. Then we extract these features from two sources of context texts, specifically the title of the news article that the comment was posted for and the screen name of the user who posted the comment.", + "For logistic regression model implementation, we use l2 loss. We adopt the balanced class weight as described in Scikit learn. Logistic regression model with character-level n-gram features is presented as a strong baseline for comparison since it was shown very effective. BIBREF0 , BIBREF9 ", + "", + "", + "For character level n-grams, we extract character level bigrams, tri-grams and four-grams. For word level n-grams, we extract unigrams and bigrams.", + "Linguistic Inquiry and Word Count, also called LIWC, has been proven useful for text analysis and classification BIBREF19 . In the LIWC dictionary, each word is labeled with several semantic labels. In our experiment, we use the LIWC 2015 dictionary which contain 125 semantic categories. Each word is converted into a 125 dimension LIWC vector, one dimension per semantic category. The LIWC feature vector for a comment or its context is a 125 dimension vector as well, which is the sum of all its words' LIWC vectors.", + "NRC emotion lexicon contains a list of English words that were labeled with eight basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) and sentiment polarities (negative and positive) BIBREF20 . We use NRC emotion lexicon to capture emotion clues in text. Each word is converted into a 10 dimension emotion vector, corresponding to eight emotion types and two polarity labels. The emotion vector for a comment or its context is a 10 dimension vector as well, which is the sum of all its words' emotion vectors.", + "As shown in table TABREF20 , given comment as the only input content, the combination of character n-grams, word n-grams, LIWC feature and NRC feature achieves the best performance. It shows that in addition to character level features, adding more features can improve hate speech detection performance. However, the improvement is limited. Compared with baseline model, the F1 score only improves 1.3%.", + "In contrast, when context information was taken into account, the performance greatly improved. Specifically, after incorporating features extracted from the news title and username, the model performance was improved by around 4% in both F1 score and AUC score. This shows that using additional context based features in logistic regression models is useful for hate speech detection." + ], + [ + "Our neural network model mainly consists of three parallel LSTM BIBREF21 layers. It has three different inputs, including the target comment, its news title and its username. Comment and news title are encoded into a sequence of word embeddings. We use pre-trained word embeddings in word2vec. Username is encoded into a sequence of characters. We use one-hot encoding of characters.", + "Comment is sent into a bi-directional LSTM with attention mechanism. BIBREF22 . News title and username are sent into a bi-directional LSTM. Note that we did not apply attention mechanism to the neural network models for username and news title because both types of context are relatively short and attention mechanism tends to be useful when text input is long. The three LSTM output layers are concatenated, then connected to a sigmoid layer, which outputs predictions.", + "The number of hidden units in each LSTM used in our model is set to be 100. The recurrent dropout rate of LSTMs is set to 0.2. In addition, we use binary cross entropy as the loss function and a batch size of 128. The neural network models are trained for 30 epochs.", + "As shown in table TABREF21 , given comment as the only input content, the bi-directional LSTM model with attention mechanism achieves the best performance. Note that the attention mechanism significantly improves the hate speech detection performance of the bi-directional LSTM model. We hypothesize that this is because hate indicator phrases are often concentrated in a small region of a comment, which is especially the case for long comments." + ], + [ + "To study the difference of logistic regression model and neural network model and potentially get performance improvement, we will build and evaluate ensemble models.", + "As shown in table TABREF24 , both ensemble models significantly improved hate speech detection performance. Figure FIGREF28 shows the system prediction results of comments that were labeled as hateful in the dataset. It can be seen that the two models perform differently. We further examined predicted comments and find that both types of models have unique strengths in identifying certain types of hateful comments.", + "The feature-based logistic regression models are capable of making good use of character-level n-gram features, which are powerful in identifying hateful comments that contains OOV words, capitalized words or misspelled words. We provide two examples from the hateful comments that were only labeled by the logistic regression model:", + "", + "(7)kmawhmf:FBLM.", + "", + "Here FBLM means fuck Black Lives Matter. This hateful comment contains only character information which can exactly be made use of by our logistic regression model.", + "", + "(8)SFgunrmn: what a efen loon, but most femanazis are.", + "", + "This comment deliberately misspelled feminazi for femanazis, which is a derogatory term for feminists. It shows that logistic regression model is capable in dealing with misspelling.", + "The LSTM with attention mechanism are suitable for identifying specific small regions indicating hatefulness in long comments. In addition, the neural net models are powerful in capturing implicit hateful language as well. The following are two hateful comment examples that were only identified by the neural net model:", + "", + "(9)freedomscout: @LarJass Many religions are poisonous to logic and truth, that much is true...and human beings still remain fallen human beings even they are Redeemed by the Sacrifice of Jesus Christ. So there's that. But the fallacies of thinking cannot be limited or attributed to religion but to error inherent in human motivation, the motivation to utter self-centeredness as fallen sinful human beings. Nearly all of the world's many religions are expressions of that utter sinful nature...Christianity and Judaism being the sole exceptions.", + "", + "This comment is expressing the stereotyping against religions which are not Christian or Judaism. The hatefulness is concentrated within the two bolded segments.", + "", + "(10)mamahattheridge: blacks Love being victims.", + "In this comment, the four words themselves are not hateful at all. But when combined together, it is clearly hateful against black people." + ], + [ + "We evaluate our model by 10 fold cross validation using our newly created Fox News User Comments Corpus. Both types of models use the exact same 10 folds of training data and test data. We report experimental results using multiple metrics, including accuracy, precision/recall/F1-score, and accuracy area under curve (AUC)." + ], + [ + "Table TABREF20 shows the performance of logistic regression models. The first section of table TABREF20 shows the performance of logistic regression models using features extracted from a target comment only. The result shows that the logistic regression model was improved in every metric after adding both word-level n-gram features and lexicon derived features. However, the improvements are moderate.", + "The second section shows the performance of logistic regression models using the four types of features extracted from both a target comment and its contextsThe result shows that the logistic regression model using features extracted from a comment and both types of context achieved the best performance and obtained improvements of 2.8% and 2.5% in AUC score and F1-score respectively.", + "Table TABREF21 shows the performance of neural network models. The first section of table TABREF21 shows the performance of several neural network models that use comments as the only input. The model names are self-explanatory. We can see that the attention mechanism coupled with the bi-directional LSTM neural net greatly improved the online hate speech detection, by 5.7% in AUC score.", + "The second section of table TABREF21 shows performance of the best neural net model (bi-directional LSTM with attention) after adding additional learning components that take context as input. The results show that adding username and news title can both improve model performance. Using news title gives the best F1 score while using both news title and username gives the best AUC score.", + "Table TABREF24 shows performance of ensemble models by combining prediction results of the best context-aware logistic regression model and the best context-aware neural network model. We used two strategies in combining prediction results of two types of models. Specifically, the Max Score Ensemble model made the final decisions based on the maximum of two scores assigned by the two separate models; instead, the Average Score Ensemble model used the average score to make final decisions.", + "We can see that both ensemble models further improved hate speech detection performance compared with using one model only and achieved the best classification performance. Compared with the logistic regression baseline, the Max Score Ensemble model improved the recall by more than 20% with a comparable precision and improved the F1 score by around 10%, in addition, the Average Score Ensemble model improved the AUC score by around 7%." + ], + [ + "We demonstrated the importance of utilizing context information for online hate speech detection. We first presented a corpus of hateful speech consisting of full threads of online discussion posts. In addition, we presented two types of models, feature-based logistic regression models and neural network models, in order to incorporate context information for improving hate speech detection performance. Furthermore, we show that ensemble models leveraging strengths of both types of models achieve the best performance for automatic online hate speech detection." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0872/instruction.md b/qasper-0872/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..17324e3f9bf6b4d50f10021cd42aba8658c76614 --- /dev/null +++ b/qasper-0872/instruction.md @@ -0,0 +1,69 @@ +Name of Paper: Binary and Multitask Classification Model for Dutch Anaphora Resolution: Die/Dat Prediction + +Question: What are the sizes of both datasets? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work", + "Dataset", + "Preprocessing", + "Binary Classification Model ::: Model Architecture", + "Binary Classification Model ::: Experimental Set-Up", + "Binary Classification Model ::: Results", + "Multitask Classification Model ::: Model Architecture", + "Multitask Classification Model ::: Experimental Set-up", + "Multitask Classification Model ::: Results", + "Discussion", + "Conclusion" + ], + "paragraphs": [ + [ + "Following previous research on automatic detection and correction of dt-mistakes in Dutch BIBREF0, this paper investigates another stumbling block for both native and non-native speakers of Dutch: the correct use of die and dat. The multiplicity of syntactic functions and the dependency on the antecedent's gender and number make this a challenging task for both human and computer. The grammar concerning die and dat is threefold. Firstly, they can be used as dependent or independent demonstrative pronouns (aanwijzend voornaamwoord), with the first replacing the article before the noun it modifies and the latter being a noun phrase that refers to a preceding/following noun phrase or sentence. The choice between the two pronouns depends on the gender and number of the antecedent: dat refers to neuter, singular nouns and sentences, while die refers to masculine, singular nouns and plural nouns independent of their gender. Secondly, die and dat can be used as relative pronouns introducing relative clauses (betrekkelijk voornaamwoord), which provide additional information about the directly preceding antecedent it modifies. Similar rules as for demonstrative pronouns apply: masculine, singular nouns and plural nouns are followed by relative pronoun die, neuter singular nouns by dat. Lastly, dat can be used as a subordinating conjunction (onderschikkend voegwoord) introducing a subordinating clause. An brief overview of the grammar is given in Table TABREF1.", + "The aim is to develop (1) a binary classification model that automatically detects, predicts and corrects die and dat instances in texts. Furthermore, the correct die/dat instance and the syntactic function of the predicted die and dat are jointly predicted in (2) a multitask classification model. Whereas research on neural-based, machine learning approaches for Dutch demonstrative and relative pronoun resolution - especially for die and dat - is to our knowledge non-existing, this project can serve as a starting point for further research on machine learning applications concerning Dutch subordinating conjunctions, demonstrative pronouns and relative pronouns." + ], + [ + "The incentive for this research project is the detection and correction system for dt-mistakes in Dutch BIBREF0. For that task, a system with a context encoder - a bidirectional LSTM with attention mechanism - and verb encoder - of which the outputs are then fed to a feedforward neural network - has been developed to predict different verb suffixes. As mentioned above, this project explores the possibility of constructing a neural network system for correcting Dutch demonstrative and relative pronouns die and dat. The task is also called pronoun resolution or anaphora resolution. Anaphora resolution and pronoun prediction has been major research subjects in machine translation research. In BIBREF3, for example, the effect of multiple English coreference resolvers on the pronoun translation in English-Dutch machine translation system with deep transfer has been investigated. Niton, Morawiecki and Ogrodnizuk (2018) developed a fully connected network with three layers in combination with a sieve-based architecture for Polish coreference resolution BIBREF4. Not only in machine translation, but also in general much research has been conducted on machine learning approaches towards coreference resolution BIBREF5BIBREF6BIBREF7 and pronoun resolution BIBREF8, BIBREF9. However, little to no research has been conducted specifically on die/dat correction." + ], + [ + "The datasets used for training, validation and testing contain sentences extracted from the Europarl corpus BIBREF1 and SoNaR corpus BIBREF2. The Europarl corpus is an open-source parallel corpus containing proceedings of the European Parliament. The Dutch section consists of 2,333,816 sentences and 53,487,257 words. The SoNaR corpus comprises two corpora: SONAR500 and SONAR1. The SONAR500 corpus consists of more than 500 million words obtained from different domains. Examples of text types are newsletters, newspaper articles, legal texts, subtitles and blog posts. All texts except for texts from social media have been automatically tokenized, POS tagged and lemmatized. It contains significantly more data and more varied data than the Europarl corpus. Due to the high amount of data in the corpus, only three subparts are used: Wikipedia texts, reports and newspaper articles. These subparts are chosen because the number of wrongly used die and dat is expected to be low." + ], + [ + "The sentences in the Europarl corpus are tokenized and parsed using the Dutch version of TreeTagger BIBREF10. Only sentences which contain at least one die or dat are extracted from the corpora. Subsequently, each single occurrence of die and dat is detected and replaced by a unique token ('PREDICT'). When there are multiple occurrences in one sentence, only one occurrence is replaced at a time. Consequently, a sentence can appear multiple times in the training and test dataset with the unique token for die and dat at a different place in the sentence. Each sentence is paired with its automatically assigned ground truth label for die and dat. The Europarl dataset, on the one hand, contains 70,057 dat-labeled and 33,814 die-labeled sentences. The resulting train and test sets consist of 103,871 (Europarl) and 1,269,091 (SoNaR) sentences. The SoNaR dataset, on the other hand, has more than ten times the number of labeled sentences with 736,987 dat-labeled and 532,104 die-labeled. Considering the imbalance in both datasets, it may be argued that dat occurs more frequently than die due to its syntactic function as subordinating conjunction and not to its use as demonstrative pronoun whereas it can only refer to singular, neutral nouns. As for the multitask classification model, the POS tags for die and dat present in the SoNaR corpus are extracted and stored as ground truth labels: 407,848 subordinating conjunction, 387,292 relative pronoun and 473,951 demonstrative pronoun. From a brief qualitative assessment on the POS tags for die and dat in both corpora, the POS tags in the SoNaR corpus appear to be more reliable than the POS tags generated by TreeTagger in the Europarl corpus. Therefore, only the SoNaR dataset is used for the multitask classification. An overview of the datasets after preprocessing is given in Table TABREF2." + ], + [ + "For the binary classification model that predicts the correct die or dat for each sentence, a Bidirectional Long-Short Term Memory (BiLSTM) neural network is computed. Whereas the antecedent can be rather distant from the relative or demonstrative pronoun due to adjectives and sentence boundaries, an LSTM architecture is chosen over a regular Recurrent Neural Network while the latter does not cope well with learning non-trivial long-distance dependencies BIBREF11. Furthermore, a bidirectional LSTM is chosen over a single left-to-right LSTM, whereas the antecedent can be either before or after the die or dat. The architecture of the binary classification model is provided in Fig. FIGREF7. The input sentence is first sent through an embedding layer where each token is transformed to a 100-dimensional word embedding which have been initially trained on the dataset of sentences containing at least one die or dat using the Word2Vec Skip-gram model BIBREF12. The weights of the embedding layer are trainable. The word embeddings are then sent through a BiLSTM layer. The bidirectional LSTM concatenates the outputs of two LSTMs: the left-to-right $LSTM_{forward}$ computes the states $\\overrightarrow{h_1}..\\overrightarrow{h_N}$ and the right-to-left $LSTM_{backward}$ computes the states $\\overleftarrow{h_N}..\\overleftarrow{h_1}$. This means that at time $t$ for input $x$, represented by its word embedding $E(x)$, the bidirectional LSTM outputs the following:", + "The concatenated output is then sent through a maxpooling layer, linear layer and, eventually, a softmax layer to get a probability distribution over the two classes. In order to prevent the model from overfitting and co-adapting too much, dropout regularization is implemented in the embedding layer and the linear layer. In both layers, dropout is set to $p = 0.5$ which randomly zeroes out nodes in the layer using samples from a Bernoulli distribution." + ], + [ + "Each dataset is randomly divided into a training (70%), validation (15%) and test set (15%). The data is fed to the model in batches of 128 samples and reshuffled at every epoch. The objective function that is minimized is Binary Cross-Entropy:", + "where $y_i$ is the ground truth label (0 for dat and 1 for die) and $p(\\hat{y}_i)$ is the probability of the predicted label for all $N$ input sentences of the train set. The weights are optimized by Stochastic Gradient Descent with learning rate = 0.01 and momentum = 0.9. The data is fed to the model in 24 epochs." + ], + [ + "An overview of the performance results is given in Table TABREF11. We compare model performance when trained and tested on the two corpora individually and experiment with different settings of the two corpora in order to investigate the effect of dataset changes on model performance. There are three settings: full in which the datasets contain full sentences, windowed in which sentences are windowed around the unique prediction token without exceeding sentence boundaries (five tokens before and after the token, including token), and windowed no_boundaries in which the windows can exceed sentence boundaries. When limiting the input sentences to windowed sentences in the Europarl corpus(2), model performance increases significantly on all metrics, especially for die prediction performance. The difference in model performance when trained and tested on the Europarl (2) and SoNaR (3) windowed datasets is particularly noticeable in the precision, recall and F1 scores. Model performance for dat prediction is better for the Europarl dataset than for the SoNaR dataset, while model performance for die prediction is notably better for the SoNaR dataset than for the Europarl dataset. Lastly, a change in windowing seems to have a positive impact on the overall model performance: the model trained and tested on the SoNaR dataset with windows exceeding sentence boundaries (3) outperforms that on the SoNaR dataset with windows within sentence boundaries (4) on every metric." + ], + [ + "The second model performs two prediction tasks. The first prediction task remains the binary classification of die and dat. The second prediction task concerns the prediction of three parts-of-speech (POS) or word classes, namely subordinating conjunction, relative pronoun and demonstrative pronoun. An overview of the model architectures is given in Fig. FIGREF13. For the BiLSTM model, the first layer is the embedding layer where the weights are initialized by means of the 200-dimensional pre-trained embedding matrix. The weights are updated after every epoch. The second layer consists of two bidirectional LSTMs where the output of the first bidirectional LSTM serves as input to the second bidirectional LSTM. The layer has dropout regularization equal to 0.2. The two-layer bidirectional LSTM concatenates the outputs at time $t$ into a 64-dimensional vector and sends it through a maxpooling layer. Until this point, the two task share the same parameters. The model than splits into two separate linear layers. The left linear layer transforms the 64-dimensional vector to a two-dimensional vector on which the softmax is computed. The softmax outputs the probability distribution over the dat and die labels. The right linear layer transforms the 64-dimensional vector to a three-dimensional vector on which the softmax is computed as well. The softmax outputs the probability distribution over the subordinating conjunction, relative pronoun and demonstrative pronoun labels. The second multitask classification model takes the immediate context around the 'PREDICT' token as additional input. Both the windowed sentence and context are first transformed into their word embedding representations. They are, then, separately sent through a sentence encoder and context encoder, respectively. The sentence encoder has the same architecture as the second and third layer of the BiLSTM model, namely a two-layer bidirectional LSTM and a maxpooling layer. For the context encoder, we experiment with two different architectures: a feedforward neural network and a one-layer bidirectional LSTM with dropout = 0.2 with a maxpooling layer on top. Both sentence and context encoder output a 64-dimensional vector which are, consequently, concatenated to a 128-dimensional vector. As in the BiLSTM model, the resulting vector is sent through two separate linear layers to output probability distributions for both the die/dat and POS prediction task." + ], + [ + "As discussed in Section SECREF4, the POS ground truth labels in SoNaR-based datasets are more reliable than the POS labels in the Europarl-based datasets that are generated by TreeTagger. Consequently, only the SoNaR dataset is used for training and testing. The dataset is randomly divided into a training (70%), validation (15%) and test (15%) set. The data is fed into the model in batches of 516 samples and the data is reshuffled at every epoch. For die/dat prediction, the Binary Cross-Entropy loss function is minimized. The weights are optimized by Stochastic Gradient Descent with learning rate = 0.01 and momentum = 0.9. For POS prediction, Cross-Entropy is minimized:", + "where $C$ is the number of classes, in this case three, $y_{i,c}$ is the binary indicator (0 or 1) if class label $c$ is the correct predicted classification for input sentence $i$ and $p$ is the probability of sentence $i$ having class label $c$. The weights are optimized using Adam optimization with learning rate = 0.0001. The data is fed to the model in 35 epochs." + ], + [ + "An overview of the performance results for die/dat prediction is given in Table TABREF19. The same dataset settings as for the binary classification model are used: full in which the datasets contain full sentences, windowed in which sentences are windowed around the unique prediction token without exceeding sentence boundaries (five tokens before and after the token, including token), and windowed no_boundaries in which the windows can exceed sentence boundaries. As mentioned in section SECREF4, we only use the SoNaR dataset. The multitask classification models generally perform better with the windowed no_boundaries dataset setting. Concerning the model architectures, it can be concluded that altering the model architecture has no large impact on model performance for die/dat prediction. However, altering the model architecture from an architecture with merely a sentence encoder to an architecture with both sentence and context encoder does have a more significant positive impact on model performance for POS prediction (Table TABREF20). For that prediction task, the multitask classification model with a bidirectional LSTM context encoder trained and tested on windowed SoNaR sentences reaches best performance results on almost all evaluation metrics." + ], + [ + "In Section SECREF5, a first classification model based on neural networks is computed to predict die and dat labels. The binary classification model consists of an embedding layer, a bidirectional LSTM, a maxpooling layer and a linear layer. The softmax is taken over the output of the last layer and provides a probability distribution over die and dat prediction labels. The sentences receive the prediction label with the highest probability. It is trained, validated and tested four times using four different database settings. From an analysis of the performance metric results, several conclusions can be drawn. Firstly, in all cases, the model appears to predict the dat label more precisely than the die label. This may be caused by the higher number of dat than die instances in training, validation and test datasets extracted from the Europarl and SoNaR corpus. Secondly, when the dataset is more balanced, as in the SoNaR corpus, the difference in performance between die and dat labels decreases as expected. Thirdly, die/dat prediction performance increases when the window over the sentences is not limited to sentence boundaries (SoNaR windowed, no_boundaries). A probable reason for that higher performance is that the model's ability to detect antecedents in the preceeding or following sentence, while it is not able to do so when it is trained and tested on boundary-constraint windowed sentences (SoNaR windowed). Lastly, it appears that performance of the model drops significantly when the binary classification model is trained and tested on full sentences (Europarl full). In conclusion, the binary classification model performs best when it is trained on the larger, more evenly balanced SoNaR corpus that consists of windowed sentences that are not limited to sentence boundaries. A clear performance overview of the best performing binary classification and multitask classification models for die/dat prediction can be found in Table TABREF21.", + "In Section SECREF6, several multitask classification models are constructed to jointly execute two prediction tasks: die/dat prediction and POS prediction. The BiLSTM multitask classification model consists of an embedding layer, two consecutive bidirectional LSTMs and a maxpooling layer. The output of the maxpooling layer is used as input to two separate linear layers followed by a softmax layer. The two softmax layers yield a probability distribution for die/dat and POS labels. The model trained and tested on windowed SoNaR sentences that exceed sentence boundaries performs better than the model on boundary-constraint windowed sentences and full sentences. The best performing BiLSTM multitask classification model (Model 2) outperforms the best binary classification model (Model 1) on every evaluation metric for die/dat prediction. This could arguably be due to the increased batch size, the doubled embedding dimension, the extra bidirectional LSTM layer, the influence of the second prediction task and/or the split in sentence and context encoder. Firstly, the data is divided into batch sizes of 512 instead of 128. Table TABREF22 shows, however, that there is little consistent difference in performance when batch size is 512 or 128. Therefore, it can be suggested that an increased batch size has no directly positive influence on model performance. Secondly, the input data is transformed to 200-dimensional word embeddings instead of 100-dimensional word embeddings. From the results displayed in Table TABREF22, it appears that a change in word embedding dimension could be causing an slight increase in model performance. Thirdly, the multitask model contains two bidirectional LSTM layers opposed to the binary model that has only one layer. Table TABREF23 shows the influence of the number of layers on the performance of the binary classification model. When the binary classification model has an additional bidirectional LSTM layer, all the evaluation metrics rise with approximately 2%. However, when the binary classification model has three bidirectional LSTM layers, model performance drops significantly. It appears that the doubled number of layers is indeed one of the reasons why the multitask classification models perform better than the binary classification model. However, not every rise in number of layers necessarily influences a model's performance in a positive manner. Concerning the influence of the POS prediction task on die/dat prediction performance and syntactic knowledge in general, a comparison between a two-layer bidirectional LSTM binary classification model and the two-layer bidirectional LSTM multitask classification model is made and displayed in Table TABREF24. It seems that the integration of POS knowledge positively influences die/dat prediction performance, while all evaluation metrics have increased. When examining the influence of a context encoder on die/dat prediction performance of Model 3 and Model 4, the evaluation metrics of Model 2, 3 and 4 are compared. The metric scores are fairly similar which leads to the conclusion that the addition of a context encoder has little to no further influence on die/dat prediction performance. Moreover, the encoder architecture does not cause a considerable difference in die/dat prediction performance between the model with a feedforward context encoder (Model 3) and the model with a bidirectional LSTM context encoder (Model 4). It can thus be suggested that a model does not necessarily profit from a different architecture and that an extra focus on immediate context is not additionally advantageous for the die/dat prediction task.", + "Contrary to the little to no impact on die/dat prediction performance, the context encoder - especially the bidirectional LSTM context encoder - does have a direct positive impact on POS prediction performance. The difference in POS prediction performance between the three multitask prediction models can be found in Table TABREF25. The model with the bidirectional LSTM context encoder outperforms the other two multitask classification models on every evaluation metric. Considering its highest POS prediction performance and high die/dat prediction performance, it is suggested that the multitask prediction model with bidirectional LSTM context encoder (Model 4) is the overall best model." + ], + [ + "Deciding which pronoun to use in various contexts can be a complicated task. The correct use of die and dat as Dutch pronouns entails knowing the antecedent and - if the antecedent is a noun - its grammatical gender and number. We experimented with neural network models to examine whether die and dat instances in sentences can be computationally predicted and, if necessary, corrected. Our binary classification model reaches a promising 84.56 % accuracy. In addition, we extended that model to three multitask classification models that not only predict die and dat, but also predicts the POS (demonstrative pronoun, relative pronoun and subordinating conjunction). By increasing the word embedding dimension, doubling the number of bidirectional LSTM layers and integrating POS knowledge in the model, the multitask classification models raise die/dat prediction performance by approximately 4 %. Concerning POS prediction performance, the multitask classification model consisting of a sentence and context encoder performs best on all evaluation metrics and reaches a accuracy of 87.78 %.", + "There are ample opportunities to further analyze, enhance and/or extend the die/dat prediction model. A qualitative study of the learned model weights, for example, could provide more insight in the prediction mechanism of the models. We already obtain excellent results with a simple neural architecture comprising relatively few parameters. We believe that more complex architectures such as a transformer architecture BIBREF13 with multihead attention will improve results. It might also be interesting to look at the possibility of integrating a language model such as BERT BIBREF14 in the classification model (e.g., as pretrained embeddings). Moreover, the binary classification task could be extended to a multiclass classification task to predict not only die and dat labels, but also respectively equivalent deze and dit labels. The difference between die/dat and deze/dat, however, entails a difference in temporal and spatial information: while die/dat indicates a physically near or earlier mentioned antecedent, deze/dit implies that the antecedent is physically distant or later mentioned in the text. That difference may possibly cause a prediction model to base its predictions on other tokens in a text." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0875/instruction.md b/qasper-0875/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..7f3bb8791fbbdd288d19068d6180d8849fa4492f --- /dev/null +++ b/qasper-0875/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: 'Warriors of the Word' -- Deciphering Lyrical Topics in Music and Their Connection to Audio Feature Dimensions Based on a Corpus of Over 100,000 Metal Songs + +Question: What are lyrical topics present in the metal genre? \ No newline at end of file diff --git a/qasper-0881/instruction.md b/qasper-0881/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..53b1f336f79c9bf39bc589bbdd7c11336114d135 --- /dev/null +++ b/qasper-0881/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Abstractive Dialog Summarization with Semantic Scaffolds + +Question: What are previous state-of-the-art document summarization methods used? \ No newline at end of file diff --git a/qasper-0886/instruction.md b/qasper-0886/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..091b364b079fd9f0dbac27161d9e7e4d9e002124 --- /dev/null +++ b/qasper-0886/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: CommonGen: A Constrained Text Generation Dataset Towards Generative Commonsense Reasoning + +Question: Are the models required to also generate rationales? \ No newline at end of file diff --git a/qasper-0888/instruction.md b/qasper-0888/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..8c1be0bc86dac22019e1ad84ccb1e3082b067df2 --- /dev/null +++ b/qasper-0888/instruction.md @@ -0,0 +1,116 @@ +Name of Paper: CommonGen: A Constrained Text Generation Dataset Towards Generative Commonsense Reasoning + +Question: Are the sentences in the dataset written by humans who were shown the concept-sets? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Problem Formulation", + "The CommonGen Dataset", + "The CommonGen Dataset ::: Collecting Concept-Sets with Captions", + "The CommonGen Dataset ::: Crowd-Sourcing via AMT", + "The CommonGen Dataset ::: Statistics", + "Methods", + "Methods ::: Seq-to-Seq Learning", + "Methods ::: A BERT-based Method: UniLM", + "Methods ::: Other methods", + "Methods ::: Incorporating Commonsense Rationales", + "Evaluation", + "Evaluation ::: Setup", + "Evaluation ::: Automatic Metrics", + "Evaluation ::: Experimental Results", + "Evaluation ::: Human Evaluation", + "Evaluation ::: Qualitative Analysis", + "Related Work ::: Machine Common Sense", + "Related Work ::: Constrained Text Generation", + "Conclusion" + ], + "paragraphs": [ + [ + "Commonsense reasoning has long been acknowledged as a critical bottleneck of artificial intelligence and especially in natural language processing. It is an ability of combining commonsense facts and logic rules to make new presumptions about ordinary scenes in our daily life. A distinct property of commonsense reasoning problems is that they are generally trivial for human-beings while challenging for machine reasoners.", + "There have been a few recent tasks and datasets for testing machine commonsense, while most of them frame their problems as multi-choice question answering, such as CSQA BIBREF0 and SWAG BIBREF1. We name this kind of tasks as deterministic commonsense reasoning because they focus on modeling the plausibility of given complete scenes. The systems for these tasks thus have to work with biased selection of distractors, and thus are less practical or challenging. Simply fine-tuning such large pre-trained language encoders can yield near or exceeding human performance BIBREF2. On the other hand, few work has been done so far in testing machine commonsense in a generative reasoning setting, where a reasoner is expected to complete scenes with several given concepts.", + "Specifically, we would like to investigate if machine-reasoning models can generate a sentence that contains a required set of concepts (i.e. nouns or verbs) while describing a common scene in our daily life. For example, as shown in Figure FIGREF1, given an unordered collection of concepts \u201c{apple (noun), bag (noun), pick (verb), place (verb), tree (noun)}\u201d, a rational reasoner should be able to generate a sentence like \u201cA boy picks some apples from a tree and places them into a bag.\u201d, which describes a natural scene and contains all given concepts. The creation of this sentence is easy for humans while non-trivial for even state-of-the-art conditional language generation models. We argue that such an ability of recovering natural scenes of daily life can benefit a wide range of natural language generation (NLG) tasks including image/video captioning BIBREF3, BIBREF4, scene-based visual reasoning and VQA BIBREF5, storytelling BIBREF6, and dialogue systems BIBREF7, BIBREF8.", + "Towards empowering machines with the generative commonsense reasoning ability, we create a large-scale dataset, named CommonGen, for the constrained text generation task. We collect $37,263$ concept-sets as the inputs, each of which contains three to five common concepts. These concept-sets are sampled from several large corpora of image/video captions, such that the concepts inside them are more likely to co-occur in natural scenes. Through crowd-sourcing via Amazon Mechanical Turk (AMT), we finally obtain $89,028$ human-written sentences as expected outputs. We investigate the performance of sophisticated sequence generation methods for the proposed task with both automatic metrics and human evaluation. The experiments show that all methods are far from human performance in generative commonsense reasoning. Our main contributions are as follows: 1) We introduce the first large-scale constrained text generation dataset targeting at generative commonsense reasoning; 2) We systematically compare methods for this (lexically) constrained text generation with extensive experiments and evaluation. 3) Our code and data are publicly available (w/ the URL in the abstract), so future research in this direction can be directly developed in a unified framework." + ], + [ + "In this section, we formulate our task with mathematical notations and discuss its inherent challenges. The input to the task is a set of $n$ concepts $x=\\lbrace c_1,c_2,\\dots ,c_n\\rbrace \\in \\mathcal {X}$, where $c_i\\in \\mathcal {C}$ is a common noun or verb. $\\mathcal {X}$ denotes the space of concept-sets and $\\mathcal {C}$ stands for the concept vocabulary. The expected output of this task is a simple, grammatical sentence $y\\in \\mathcal {Y}$, describing a natural scene in our daily-life that covers all given concepts in $x$. Note that other forms of given concepts are also accepted, such as plural forms of nouns and verbs. In addition, we also provide rationales as an optional resource to model the generation process. For each pair of $(x, y)$, a rationale $r$ is a list of sentences that explains the background commonsense knowledge used in the scene recovering process.", + "The task is to learn a structured predictive function $f:\\mathcal {X} \\rightarrow \\mathcal {Y}$, which maps a concept-set to a sentence. Thus, it can be seen as a special case of constrained text generation BIBREF9. The unique challenges of our proposed task come from two main aspects as follows.", + "Constrained Decoding. Lexically constrained decoding for sentence generation has been an important and challenging research topic in machine translation community BIBREF10, where they focus on how to decode sentences when some words/phrases (e.g. terminology) must present in target sentences (Section SECREF6). However, it is still an open problem how to efficiently generate sentences given an unordered set of multiple keywords with potential morphological changes (e.g. \u201cpick\u201d $\\rightarrow $ \u201cpicks\u201d in the previous case). Apart from that, the part-of-speech constraints brings even more difficulties (e.g. \u201cplace\u201d can be verb/noun).", + "Commonsense Reasoning. Apart from the challenge in constrained decoding, a generative commonsense reasoner also has to compositionally use (latent) commonsense knowledge for generating most plausible scenes. Recall the illustrative example in Figure FIGREF1, even such a simple scene generation process needs pretty much commonsense knowledge like: 1) \u201capples grow in trees\u201d; 2) \u201cbags are containers that you can put something in\u201d; 3) \u201cyou usually pick something and then place it in a container\u201d. Expected reasoners have to prioritize target scenes over an infinity number of less plausible scenes like \u201cA boy picks an apple tree and places it into bags.\u201d or \u201cA boy places some bags on a tree and picks an apple.\u201d." + ], + [ + "In this section, we present how we build the CommonGen dataset for testing machine commonsense with generative reasoning. The overall data collection process is as follows. 1) We first collect a large amount of high-quality image/video caption sentences from several existing corpora, 2) Then, we compute co-occurrence statistics about concept-sets of different sizes ($3\\sim 5$), such that we can find the concept-sets that are more likely to be present in the same scene. 3) Finally, we ask human crowd-workers from AMT to write scenes with rationales for every given concept-set, which serve as our development and test sets. The training set consists of carefully post-processed human-written caption sentences, which have little overlap with dev/test sets. We present the statistics and show its inherent challenges at the end of this section." + ], + [ + "Following the general definition in the largest commonsense knowledge graph, ConceptNet BIBREF11, we understand a concept as a common noun or verb. We aim to test the ability of generating natural scenes with a given set of concepts. The expected concept-sets in our task are supposed to be likely co-occur in natural, daily-life scenes . The concepts in images/videos captions, which usually describe scenes in our daily life, thus possess the desired property. We therefore collect a large amount of caption sentences from a variety of datasets, including VATEX BIBREF4, LSMDC BIBREF12, ActivityNet BIBREF13, and SNLI BIBREF15, forming 1,040,330 sentences in total.", + "We assume if a set of concepts are all mentioned together in more caption sentences, then this concept-set is more like to co-occur. Thus, we compute the co-occurrence frequency of all possible concept-sets that have $3\\sim 5$ concepts, named as three/four/five-concept-sets respectively. Each concept-set is associated with at least one caption sentences. We carefully post-process them and take the shortest ones with minimal overlaps as the final data. These initial concept-sets are further divided into three parts: train/dev/test. We then iterate all training concept-sets and remove the ones that have more than two overlapping concepts with any concept-set in the dev or test set. Thus, the dev/test set can better measure the generalization ability of models on unseen combinations of concepts." + ], + [ + "It is true that the above-mentioned associated caption sentences for each concept-set are human-written and do describe scenes that cover all given concepts. However, they are created under specific contexts (i.e. an image or a video) and thus might be less representative for common sense. To better measure the quality and interpretability of generative reasoners, we need to evaluate them with scenes and rationales created by using concept-sets only as the signals for annotators.", + "We collect more human-written scenes for each concept-set in dev and test set through crowd-sourcing via the Amazon Mechanical Turk platform. Each input concept-set is annotated by at least three different humans. The annotators are also required to give sentences as the rationales, which further encourage them to use common sense in creating their scenes. The crowd-sourced sentences correlate well with the associated captions, meaning that it is reasonable to use caption sentences as training data although they can be partly noisy. Additionally, we utilize a search engine over the OMCS corpus BIBREF16 for retrieving relevant propositions as distant rationales in training data." + ], + [ + "We present the statistical information of our final dataset. Firstly, we summarize the basic statistics in Table TABREF9, such as the number of unique concept-sets, scene sentences, and sentence lengths. In total, there are 3,706 unique concepts among all concept-sets, and 3,614/1,018/1,207 in the train/dev/test parts respectively. Note that there are 4% of the dev and 6% of the test concepts never appear in the training data, so we can better understand how well trained models can perform with unseen concepts.", + "We analyze the overlap between training concept-sets and dev/test concept-sets. By average, we find that 98.8% of the training instances share no common concept at all with dev/test data, such that the dev/test can help us analyze model performance on new combinations of concepts.", + "We also visualize the frequency distribution of our test concept-sets in Figure FIGREF7 by showing the frequency of top 50 single concepts and co-occurred concept pairs." + ], + [ + "In this section, we introduce the methods that we adopt for the proposed constrained text generation task. We group these methods into several types as follows. Basically, we have different kinds of encoder-decoder architectures with copy attention mechanism, including both classic and recently proposed methods. Apart from that, we utilize the state-of-the-art pre-trained sentence generation model for our task. Moreover, we include three typical models for abstractive summarization, story generation respectively, and keywords-based decoding of language models." + ], + [ + "One very straightforward way is to form this problem as a \u201csequence\u201d-to-sequence task, where input sequences are randomly sorted sets of given concepts. In this way, encoder-decoder seq2seq architectures based on bidirectional RNN (bRNN) BIBREF17 or Transformer (Trans.) BIBREF18 can be directly adopted to the task, just like many other conditional sequence generation problems (translation, summarization, etc.).", + "Order-insensitive processing. However, these encoders may degrade because our inputs are actually order-insensitive. We thus try to use multi-layer perceptrons (MLP) with mean-pooling as the encoder (\u201cmean encoder\u201d) over sequences of word vectors to completely eliminate the order sensitivity. Similarly, we consider removing the positional embeddings in Transformers (Trans. w/o Pos).", + "Copying mechanism. The above-mentioned architectures with vanilla attention can miss the words in input sequences and thus produce either unknown tokens or synonyms. To force the decoder to produce target sentences with a constraint on input sentence, we utilize the copying mechanism BIBREF19 for all these models. We follow the implementation of these methods by OpenNMT-py BIBREF20.", + "Non-autoregressive generation. Recent advances in conditional sentence generation have a focus on edit-based models, which iteratively refine generated sequences (usually bounded by a fixed length). These models potentially get better performance than auto-regressive methods because of their explicit modeling on iterative refinements. We study typical models including iNAT BIBREF21, Insertion Transformer (InsertTrans) BIBREF22, and Levenshtein Transformer (LevenTrans) BIBREF23." + ], + [ + "We employ a new unified pre-trained language model, UniLM BIBREF24, which uses BERT BIBREF25 as the encoder and then fine-tunes the whole architecture with different generation-based objective. To the best of our knowledge, the UniLM model is the state-of-the-art method for a wide range of conditional text generation tasks including summarization, question generation, and dialogue responding." + ], + [ + "Based on the similarity between our task and abstractive summarization and story generation (with given topic words), we also apply Pointer Generator Networks (\u201cPointerGen\u201d) BIBREF26 and Multi-scale Fusion Attention (\u201cFusion Attn.\u201d) BIBREF27 model respectively for our task." + ], + [ + "We explore how to utilize additional commonsense knowledge (i.e. rationales) as the input to the task. Like we mentioned in Section SECREF6, we search relevant sentences from the OMCS corpus as the additional distant rationales, and ground truth rationale sentences for dev/test data. The inputs are no longer the concept-sets themselves, but in a form of \u201c[rationales$|$concept-set]\u201d (i.e. concatenating the rationale sentences and original concept-set strings)." + ], + [ + "Herein, we present the experimental results for comparing different baseline methods in the proposed setting. We first introduce the setup and automatic metrics, and then we present the results and analysis. Finally, we show human evaluation results and qualitative analysis." + ], + [ + "We use the proposed CommonGen dataset in two setting: knowledge-agnostic and knowledge-aware. For the knowledge-agnostic setting, we simply apply the methods in Section SECREF4 while we concatenate rationales and input concept-sets together as the knowledge-aware inputs (\u201c$+r$\u201d)." + ], + [ + "For automatically evaluating our methods, we propose to use widely used metric for image/video captioning. This is because the proposed CommonGen task can be regarded as also a caption task where the context are incomplete scenes with given concept-sets. Therefore, we choose BLEU-3/4 BIBREF28, ROUGE-2/L BIBREF29, CIDEr BIBREF30, and SPICE BIBREF31 as the main metrics. Apart from these classic metrics, we also include a novel embedding-based metric named BERTScore BIBREF32. To make the comparisons more clear, we show the delta of BERTScore results by subtracting the score of merely using input concept-sets as target sentences, named $\\triangle $BERTS.", + "To have an estimation about human performance in each metric, we iteratively treat every reference sentence in dev/test data as the prediction to be compared with all references (including itself). That is, if a model has the same reasoning ability with average performance of our crowd workers, its results should exceed this \u201chuman bound\u201d." + ], + [ + "We present the experimental results of five groups of methods that are introduced in Section SECREF4. We find that the model UniLM outperforms all other baseline methods by a large margin, which is expected due to it is pre-trained with the BERT encoder towards generation objectives. However, its performance is still way far from the human bound, and this margin is even larger in test data.", + "We notice that the most recent edit-based model named LevenTrans archives the best performance among models without pre-training at all. This shows that edit-based sequence generation models can better deal with the cases where target sentences share similar vocabulary with source ones. Nonetheless, the other two models within the same sequence modeling framework (i.e. fairseq) are much worse, which might because of their specialty designed for machine translation.", + "An order-insensitive sequence/set encoder, \u201cmean encoder\u201d, outperform order-sensitive counterparts like \u201cbRNN\u201d. However, such a marginal improvement is not seen in the comparison between \u201cTrans.\u201d vs \u201cTrans. w/o Pos\u201d. We assume that for short sequences the order sensitivity does not harm sequential encoders, while positional embeddings in Transformers can better improve the self-attention mechanism. Also, we find that Transformer-based seq2seq architectures are not outperforming simpler models like bRNN.", + "As for the use of additional retrieved sentences form OMCS corpus and human-written associated rationales, we find that they are not generally helpful in investigated architectures. Although they increase the BLEU and ROUGE scores, the metrics specially designed for captioning like CIDEr and SPICE are dropping down. We argue that it might because the OMCS sentences are actually not aligned with training data, and more sophisticated methods for encoding such non-sequential facts in a more compositional way." + ], + [ + "From the automatic evaluation results with multiple metrics, we have a rough idea of the performance of all models. However, no automatic metric is perfect, especially for a newly proposed generation task like the CommonGen. We thus ask humans to rank 100 outputs of 6 selected typical models as well as one randomly picked reference sentence, forming seven systems in total. Annotators are educated to rank results by their coverage, fluency, and plausibility in daily life. Then, we compute the cumulative gains of each system in all 100 cases:", + "$S^{(k)}_i$ is the final score of the $i$-th system by the $k$-th annotator. $G^{k}_{i, j}$ is the rank position of the $i$-th system output for $j$-th example. In our case, $N=100$, $K = 5$, $G^{k}_{i, j}\\in [1,7]$.", + "As shown in Table TABREF22, we compare different systems including human bound for both the above-introduced cumulative ranking scores and the average hit@top3 rates with standard deviations. We find that the correlation between human evaluation and CIDEr and SPICE are better than the other metrics (see Table TABREF15)." + ], + [ + "For more clearly observe the performance of interested models, we present several real system outputs on the test set in Table TABREF24. We find that models usually cannot cover all given concepts, and also can produce repetitions of given concepts (e.g. \u201ca dog catches a dog\u201d, \u201ca couple of couples\u201d, and \u201cat an object and an object .\u201d). Moreover, we find that the order of actions may be mot natural. For example, the model output \u201ca man pulls a sword out of his mouth and swallows it\u201d makes less sense because a man usually swallow a sword first before he pull it out in such performances." + ], + [ + "Machine common sense (MCS) has long been considered as one of the most significant area in artificial intelligence. Recently, there are various emerging datasets for testing machine commonsense from different angles, such as commonsense extraction BIBREF33, BIBREF34, next situation prediction (SWAG BIBREF1, CODAH BIBREF35, HellaSWAG BIBREF36), cultural/social understanding BIBREF37, BIBREF38, BIBREF39, visual scene comprehension BIBREF40, and general commonsense question answering BIBREF0, BIBREF41. Most of them are in a multi-choice QA setting for discriminative commonsense reasoning, among which CSQA BIBREF0 and SWAG BIBREF1 are two typical examples. The input of the CSQA task is a question that needs commonsense reasoning and there are five candidate answers (words/phrases). The SWAG task asks models to select which situation is the most plausible next situation, given a sentence describing an event.", + "The two tasks share very similar objectives with large pre-trained language encoders like BERT BIBREF42: Masked-LM can predict the missing words in an incomplete sentence, which is similar to the CSQA setting; NextSentPrediction classifies whether a sentence is the next sentence of the given sentence in the corpora, which can be seen as using distant supervision for the SWAG task. Thus, simply fine-tuning such large pre-trained language encoders can yield near or exceeding human performance BIBREF43, BIBREF2, but it does not necessarily mean machine reasoners can really produce new assumptions in an open and generative setting. The proposed CommonGen, to the best of our knowledge, is the first dataset and task for generative commonsense reasoning." + ], + [ + "Constrained or controllable text generation aims to decode realistic sentences that have expected attributes such as sentiment BIBREF44, BIBREF9, tense BIBREF9, template BIBREF45, style BIBREF46, BIBREF47, BIBREF48, etc. The most similar scenario with our task is lexically constrained sentence encoding, which has been studied mainly in the machine translation community BIBREF49, BIBREF50 for dealing with terminology and additional bilingual dictionaries.", + "Classic methods usually modify the (beam) searching algorithms to accommodate lexical constraints like Grid Beam Search BIBREF10. The most recent work in this line is the CGMH BIBREF51 model, which works in the inference stage to sample sentences with a sequence of multiple keywords from language models. However, our task brings more challenges: 1) we do not assume there is a fixed order of keywords in target sentences; 2) we allow morphological changes of the keywords; 3) the decoded sentences must describe highly plausible scenes in our daily life. Current methods cannot well address these issues and also work extremely slow to generate grammatical sentences. We instead mainly investigate sequence-to-sequence architectures, especially models that are based on editing operations and non-autoregressive. Pre-trained seq2seq generation models like UniLM BIBREF24 and BRAT BIBREF52 are usually initialized with pre-trained language encoder and then further fine-tuned with multiple NLG tasks. The UniLM archives the best performance on our proposed CommonGen task, while being far from human-level performance and hardly interpretable." + ], + [ + "In this paper, we purpose a novel constrained text generation task for generative commonsense reasoning. We introduce a new large-scale dataset named CommonGen and investigate various methods on them. Through our extensive experiments and human evaluation, we demonstrate that the inherent difficulties of the new task cannot be addressed by even the state-of-the-art pre-trained language generation model.", + "For the future research, we believe the following directions are highly valuable to explore: 1) specially designed metrics for automatic evaluation that focus on commonsense plausibility; 2) better mechanisms for retrieving and imposing useful commonsense knowledge into sentence generation processes; 3) explicitly modeling keyword-centric edits (e.g. insertion, deletion, morphological changes) such that relevant commonsense knowledge can be well utilized. We also believe that models performed well on CommonGen can be easily transferred to other commonsense-required reasoning tasks with few annotations, including image/video captioning, visual question answering, and discriminative multi-choice commonsense question answering." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0901/instruction.md b/qasper-0901/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..3698318f44c0734582ab1e423f1ec25d07535032 --- /dev/null +++ b/qasper-0901/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Modeling Event Background for If-Then Commonsense Reasoning Using Context-aware Variational Autoencoder + +Question: How do they measure the diversity of inferences? \ No newline at end of file diff --git a/qasper-0906/instruction.md b/qasper-0906/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..a34e556d908daee2febdbc110a807193f8ef424d --- /dev/null +++ b/qasper-0906/instruction.md @@ -0,0 +1,59 @@ +Name of Paper: Comparing Human and Machine Errors in Conversational Speech Transcription + +Question: what standard speech transcription pipeline was used? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Measuring Human Error", + "Machine Transcription System", + "Error Distribution and Correlation", + "Error types", + "A Turing-like Experiment", + "Conclusions", + "Acknowledgments" + ], + "paragraphs": [ + [ + "Automatic speech recognition (ASR) systems have seen remarkable advances over the last half-decade from the use of deep, convolutional and recurrent neural network architectures, enabled by a combination of modeling advances, available training data, and increased computational resources. Given these advances, our research group recently embarked on an effort to reach human-level transcription accuracy using state-of-the-art ASR techniques on one of the genres of speech that has historically served as a difficult benchmark task: conversational telephone speech (CTS). About a decade ago, CTS recognition had served as an evaluation task for government-sponsored work in speech recognition, predating the take-over of deep learning approaches and still largely in the GMM-HMM modeling framework BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 . It had proven to be a hard problem, due to the variable nature of conversational pronunciations, speaking styles, and regional accents. Seide at al. BIBREF6 demonstrated that deep networks as acoustic models could achieve significant improvements over GMM-HMM models on CTS data, and more recently researchers at IBM had achieved results on this task that represented a further significant advance BIBREF7 , BIBREF8 over those from a decade ago.", + "The goal of reaching \u201chuman parity\u201d in automatic CTS transcription raises the question of what should be considered human accuracy on this task. We operationalized the question by submitting the chosen test data to the same vendor-based transcription pipeline that is used at Microsoft for production data (for model training and internal evaluation purposes), and then comparing the results to ASR system output under the NIST scoring protocol. Using this methodology, and incorporating state-of-the-art convolutional and recurrent network architectures for both acoustic modeling BIBREF9 , BIBREF10 , BIBREF7 , BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 and language modeling BIBREF15 , BIBREF16 , BIBREF17 , BIBREF18 with extensive use of model combination, we obtained a machine error rate that was very slightly below that of the human transcription process (5.8% versus 5.9% on Switchboard data, and 11.0% versus 11.3% on CallHome English data) BIBREF19 . Since then, Saon et al. have reported even better results, along with a separate transcription experiment that puts the human error rate, on the same test data, at a lower point than measured by us (5.1% for Switchboard, 6.8% for CallHome) BIBREF20 .", + "In this paper, we address the question whether there are major qualitative differences between the results of human transcriptions of conversational speech and those obtained by ASR systems, based on a detailed analysis of the data and system output from our human parity experiment BIBREF19 . The question becomes important if ASR is to replace humans as the first step in fully automatic speech understanding systems\u2014if machine transcription errors are qualitatively different from humans then we would have to worry about the possible effects on downstream processing, and mitigation techniques so as to still achieve an overall \u201cnatural\u201d user experience (e.g., in real-time conversational speech translation, such as in the Skype application).", + "We start by discussing why human error rate on this task must themselves be considered a moving target. Next we ask whether speech that is difficult for ASR also tends to be hard for humans to transcribe (and vice-versa), and whether the speaker overlap with the training data that is found in a portion of the test data has a noticeable effect on the result, as was suggested in BIBREF20 . We then look at the most frequent word error types exhibited by the two transcription systems (human and machine), and finally report on a very preliminary but still informative experiment to see if humans could tell apart the transcription source (again, human versus machine), based on the errors they make." + ], + [ + "The assessment of human transcription error on conversational speech has been somewhat murky. A widely cited figure is 4% word error rate (WER), based on BIBREF21 . However, the reference therein is only a \u201cpersonal communication\u201d without further data. The Linguistics Data Consortium quantified inter-transcriber disagreement for the NIST 2003 CTS evaluation data at between 4.1% and 4.5% with very careful multiple transcriptions BIBREF22 . For \u201cquick transcription\u201d, the disagreement increased to 9.6%. The CTS data in the NIST study is from the Switchboard (SWB) and Fisher corpora, and is therefore comparable to the SWB portion of our data, i.e., coming from telephone conversations between strangers discussing a general-interest topic. Still, the exact dataset is different, which may account for some of the discrepancy with error rates measured on the NIST 2000 set used by us (5.9%) and IBM (5.1%), although the numbers are remarkably close.", + "As briefly described in the introduction, we measured human performance by leveraging an existing pipeline in which Microsoft data is transcribed on a weekly basis. This pipeline uses a large commercial vendor to perform two-pass transcription. In the first pass, a transcriber works from scratch to transcribe the data. In the second pass, a second listener monitors the data to do error correction. Dozens of hours of test data are processed in each batch, with no special instructions to the transcribers. The waveform segments, roughly corresponding to utterances, making up the test set are processed separately. This makes the task easier since the speakers are more clearly separated, but also more difficult since the two sides of the conversation are not interleaved and context may be missing. We performed that text normalization on the human transcripts to remove systematic discrepancies with the NIST scoring references. (Since this was done with some amount of trial and error it effectively was \u201ccheating\u201d for the benefit of the human transcribers.) We then applied the NIST scoring tools to obtain word error rates of 5.9% on the SWB portion, and 11.3% on the CallHome (CH) portion of the NIST 2000 test set. The latter corpus, unlike Switchboard, consists of conversations between friends and family, without seed topic, which would account for the much higher overall error rate. Clearly our method was not designed to achieve the highest possible human transcription accuracy; instead, as pointed out in BIBREF19 , our goal was to establish a benchmark corresponding to industry-standard (i.e. high-volume) professional transcript production.", + "The authors in BIBREF20 undertook to measure human error on the same dataset, but using a more involved process. The major differences were: (1) The transcription vendor was cognizant of the experiment and actively involved. (2) Transcribers were chosen based on past performance and familiarized with the conventions used by LDC in generating the reference transcripts. (3) Three independent, parallel transcribers were used, plus a fourth one for 2nd-pass quality control (QC) of the 1st-pass output. All in all, the transcribers performed roughly 12 to 18 listening passes. (4) The final output was obtained by choosing the transcriber (with QC) who had obtained the lowest WER on the test data. As noted earlier, the resulting WERs were 5.1% and 6.8%, respectively. The considerably lower estimate for CH could be a result of the transcribers having access to the entire conversation (as per personal communication with the authors). This would be especially helpful in transcribing unfamiliar vocabulary and speaking styles (allowing the transcriber to \u201cadapt\u201d to the data more effectively).", + "Clearly the IBM experiment made a much more thorough effort to probe the boundaries of human accuracy, and may in fact have come close to the inter-transcriber agreement previously measured by LDC on a different data set. However, it is important to realize that further improvements on the human side are no doubt achievable. For example, the number of transcribers could be scaled up further, or they could be allowed to confer with each other, to resolve disagreements. This raises the question of where to draw the line on human effort.", + "Finally, it is important to realize that conversational speech has a high degree of inherent ambiguity. For example, conversational pronunciations are highly variable and often reduced BIBREF23 . Another source of ambiguity is the lack of context and knowledge shared by the speakers (especially in the case of CH). In the presence of inherent ambiguity, inter-transcriber agreement can be improved by agreed-upon disambiguation rules, although this would not necessarily reflect true agreement based on speech understanding." + ], + [ + "The details of our conversational speech recognition system are described elsewhere BIBREF19 , so we only give a brief summary here. The system employs independent decodings by diverse acoustic models, including convolutional neural net (CNN) and bidirectional long short-term memory (BLSTM) models that differ by model architecture, number of senones, amount of training data, and other metaparameters. Decoding uses a pruned 4-gram N-gram language model (LM) to generate lattices, which are then expanded into 500-best lists using a larger N-gram LM. The N-best lists are rescored with multiple LSTM-LMs operating in forward and backward directions. Model scores are combined log-linearly at the utterance level and converted to posterior probabilities represented as word confusion networks. The various subsystems making up the final system are selected in a greedy search, and their weights are optimized via an expectation-maximization algorithm, on development data. The acoustic training data comprises all the publicly available CTS data (about 2000 hours), while the LMs are additionally trained on Broadcast News and Web data from U. Washington. The individual subsystems (based on different acoustic models) achieve word error rates between 6.4% and 7.7% on the Switchboard evaluation set, and between 12.2% and 17.0% on the CallHome portion. Combined, the system achieves 5.8% and 11.0% WER, respectively." + ], + [ + "We note in passing that machine and human transcription WERs do not differ significantly according the Wilcoxon and Matched Pairs Sentence Segment Word Error tests as applied by NIST, nor do they differ according to a Sign test comparing error counts at the utterance level.", + "A first high-level question regarding the relation between word errors by machine and human transcribers is whether difficulty in one predicts difficulty in the other. Figure FIGREF1 shows scatter plots of speaker-level error rates (machine vs. human), separated by corpus. Each corpus subset has 40 conversation sides.", + "Clearly the errors at that level are correlated, with INLINEFORM0 for SWB and INLINEFORM1 for CH. This suggests that properties of the speech, either as a function of the content, the speaker, or the channel (each speaker occurs in exactly one test conversation), cause errors for both machine and human transcription.", + "We observe that the CH data has two speakers with outlier machine error rates (37.5% and 64.7% WER, solid red dots in Figure FIGREF1 ). These correspond to secondary speakers in their respective conversation sides, each with only a fraction of the speech of the dominant speaker. Note that the ASR system processes each conversation assuming only a single speaker per side. If we remove these outliers, the machine-human error correlation on CH increases to INLINEFORM0 . With secondary speakers excluded, we can also observe that the machine error rates cluster tighter than the human ones in both corpora (SWB: machine INLINEFORM1 vs. human INLINEFORM2 ; CH: machine INLINEFORM3 vs. human INLINEFORM4 ).", + "In BIBREF20 it was sugggested that one of the reasons for the much higher error rate on CH compared to SWB was that 36 of the 40 SWB test speakers occur in the portion of the SWB corpus that is used in training (due to what we surmise to be an oversight in the selection of the NIST 2000 test set). To assess this hypothesis we singled out the four speakers in the SWB portion that are not found in the training set; these are shown as solid black circles in Figure FIGREF1 . At first, it seems that the speaker-averaged WER for the \u201cseen\u201d speakers (machine WER 5.9%) is indeed much lower than for the speakers not found in training (7.5%). However, we can safely attribute this to bad luck and small sample size. The average machine WER of 7.5% for \u201cunseen\u201d speakers is well within one standard deviation of the \u201cseen\u201d speakers' WER distribution ( INLINEFORM0 ), and more tellingly, almost exactly the same relative difference in WERs between \u201cseen\u201d and \u201cunseen\u201d speakers is observed for human transcriptions (6.0% versus 7.7%). Clearly the human transcribers did not have the benefit of training on the \u201cseen\u201d speakers, so the difference must be due to the intrinsic difficulty of the speakers, which affects both transcription systems." + ], + [ + "Tables TABREF3 \u2013 TABREF5 show the top ten types of substitutions, deletions and insertions for both ASR and human transcripts. Inspections reveals that the same short function words, discourse markers and filled pauses appear in the top ten errors for both systems. There is one notable exception, however. The top substitution error for the ASR system involves misrecognition of filled pauses (\u201c%hesitation\u201d, a word class label covering \u201cuh\u201d and \u201cum\u201d in various spellings) as backchannel acknowledgments (\u201c%bcack\u201d, standing for \u201duhhuh\u201d, \u201cmhm\u201d, etc.). The same substitution error is much less frequent in human transcripts.", + "A possible explanation for this asymmetry lies in the discourse functions of filled pauses and backchannels. Filled pauses serve to either claim or retain the floor, signaling that the speaker wants to either start or continue speaking. Backchannels, on the other hand, acknowledge that the speaker is listening, and that the other speaker should carry on. Since the two classes of words thus have exactly opposite functions in turn management, it stands to reason that humans are keenly aware of their differences and use all available phonetic, prosodic, and contextual cues to distinguish then. Our ASR system, by contrast, uses only its standard acoustic-phonetic and language models. Modeling dialog context in particular would be expected to improve this shortcoming." + ], + [ + "Having established that human and machine transcriptions are quite similar in several aspects, including the word token types involved, we were wondering if higher-level error patterns could distinguish the two systems. For example, one might expect that human misrecognitions are guided by a strong \u201chuman\u201d language and understanding model, whereas machine errors might be more likely to generate syntactic and semantic nonsense. To get at this question we designed a specialized version of the classic Turing test, in the sense that a human judge is asked to interact with a system with the goal of estimating whether it is underpinned by human or artificial \u201cintelligence.\u201d In our case, the task involved inspecting one randomly chosen utterance from the test set at a time, with a side-by-side display of the reference transcript, the human transcript, and the ASR output (after the text normalizations that are part of the scoring protocol). Only utterances having at least one transcription error and a discrepancy between the two versions are presented. Discrepancies between the transcript versions are highlighted, and the error type (substitution, insertion, deletion) is visually coded as well, as shown in Figure FIGREF7 .", + "We ran this informal experiment during four days on the exhibitor floor of the 2017 IEEE ICASSP conference in New Orleans. The players were not formally recruited or characterized, but consisted of conference attendees who for the most part had some background or experience in speech processing. Subjects were introduced to the test by explaining the research background, and were allowed to play as many trials as they wanted. Out of a total of 353 trials, subjects identified the human transcript correctly 188 times, for an overall success rate of 53%. The successes included occasional gimmes like human misspellings or the asymmetry in the filled pause/backchannel substitution (which we pointed out to the subjects). According to a binomial test, this success rate does not differ signficantly from the 50% chance rate ( INLINEFORM0 , one-tailed). While this result is obviously quite preliminary, it was a good demonstration that it is not easy distinguishing machine from human errors, even for technically sophisticated observers." + ], + [ + "We have discussed methodological issues and reported first findings when comparing automatic conversational speech transcriptions to human performance, using data generated by our recent efforts to reach human parity in CTS recognition. While an exact characterization of the human benchmark remains a moving target that is subject to debate, our results so far have shown that machine transcription errors track those made by humans in several important aspects. At the speaker (as well as corpus) level the two error rates are strongly correlated, suggesting that common underlying factors in the speech data determine transcription difficulty for both humans and ASR systems. (A detailed characterization of those factors has precedent in ASR research and should be revisited while also considering human performance.) A partial overlap of Switchboard training and test speakers seems to have no major effect on error rates. We also find that the most frequent error patterns involve the same short function words and discourse particles for both humans and machines. The one notable exception is that ASR tends to confuse filled pauses and backchannels, a functional distinction that humans need to be very good at pragmatically. An informal Turing-like test also demonstrated that error patterns in the two types of transcriptions are not obviously distinguishable. Overall, we conclude that recent advances in ASR technology have not only achieved remarkable levels of accuracy, but also generate results that are qualitatively surprisingly similar to professional human transcriber output." + ], + [ + "We thank our coauthors and collaborators on the Human Parity project: X. Huang, F. Seide, M. Seltzer, W. Xiong, D. Yu, and G. Zweig. Thanks to K. Riedhammer for sharing metadata on train/test speaker overlap." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0908/instruction.md b/qasper-0908/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..b82873e10286efe5f993b723d4439a3d90dbc17f --- /dev/null +++ b/qasper-0908/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: An Empirical Comparison of Simple Domain Adaptation Methods for Neural Machine Translation + +Question: What kinds of neural networks did they use in this paper? \ No newline at end of file diff --git a/qasper-0909/instruction.md b/qasper-0909/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..56f931142d3a9d5db902ca33d43e217f92c9ee05 --- /dev/null +++ b/qasper-0909/instruction.md @@ -0,0 +1,78 @@ +Name of Paper: An Empirical Comparison of Simple Domain Adaptation Methods for Neural Machine Translation + +Question: How did they use the domain tags? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related Work", + "Methods for Comparison", + "Fine Tuning", + "Multi Domain", + "Mixed Fine Tuning", + "Experimental Settings", + "High Quality In-domain Corpus Setting", + "Low Quality In-domain Corpus Setting", + "MT Systems", + "Results", + "Conclusion" + ], + "paragraphs": [ + [ + "One of the most attractive features of neural machine translation (NMT) BIBREF0 , BIBREF1 , BIBREF2 is that it is possible to train an end to end system without the need to deal with word alignments, translation rules and complicated decoding algorithms, which are a characteristic of statistical machine translation (SMT) systems. However, it is reported that NMT works better than SMT only when there is an abundance of parallel corpora. In the case of low resource domains, vanilla NMT is either worse than or comparable to SMT BIBREF3 .", + "Domain adaptation has been shown to be effective for low resource NMT. The conventional domain adaptation method is fine tuning, in which an out-of-domain model is further trained on in-domain data BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 . However, fine tuning tends to overfit quickly due to the small size of the in-domain data. On the other hand, multi domain NMT BIBREF8 involves training a single NMT model for multiple domains. This method adds tags \u201c<2domain>\" by modifying the parallel corpora to indicate domains without any modifications to the NMT system architecture. However, this method has not been studied for domain adaptation in particular.", + "Motivated by these two lines of studies, we propose a new domain adaptation method called \u201cmixed fine tuning,\" where we first train an NMT model on an out-of-domain parallel corpus, and then fine tune it on a parallel corpus that is a mix of the in-domain and out-of-domain corpora. Fine tuning on the mixed corpus instead of the in-domain corpus can address the overfitting problem. All corpora are augmented with artificial tags to indicate specific domains. We tried two different corpora settings:", + "We observed that \u201cmixed fine tuning\" works significantly better than methods that use fine tuning and domain tag based approaches separately. Our contributions are twofold:" + ], + [ + "Besides fine tuning and multi domian NMT using tags, another direction for domain adaptation is using in-domain monolingual data. Either training an in-domain recurrent neural language (RNN) language model for the NMT decoder BIBREF13 or generating synthetic data by back translating target in-domain monolingual data BIBREF5 have been studied." + ], + [ + "All the methods that we compare are simple and do not need any modifications to the NMT system." + ], + [ + "Fine tuning is the conventional way for domain adaptation, and thus serves as a baseline in this study. In this method, we first train an NMT system on a resource rich out-of-domain corpus till convergence, and then fine tune its parameters on a resource poor in-domain corpus (Figure 1 )." + ], + [ + "The multi domain method is originally motivated by BIBREF14 , which uses tags to control the politeness of NMT translations. The overview of this method is shown in the dotted section in Figure 2 . In this method, we simply concatenate the corpora of multiple domains with two small modifications: a. Appending the domain tag \u201c<2domain>\" to the source sentences of the respective corpora. This primes the NMT decoder to generate sentences for the specific domain. b. Oversampling the smaller corpus so that the training procedure pays equal attention to each domain.", + "We can further fine tune the multi domain model on the in-domain data, which is named as \u201cmulti domain + fine tuning.\u201d" + ], + [ + "The proposed mixed fine tuning method is a combination of the above methods (shown in Figure 2 ). The training procedure is as follows:", + "Train an NMT model on out-of-domain data till convergence.", + "Resume training the NMT model from step 1 on a mix of in-domain and out-of-domain data (by oversampling the in-domain data) till convergence.", + "By default, we utilize domain tags, but we also consider settings where we do not use them (i.e., \u201cw/o tags\u201d). We can further fine tune the model from step 2 on the in-domain data, which is named as \u201cmixed fine tuning + fine tuning.\u201d", + "Note that in the \u201cfine tuning\u201d method, the vocabulary obtained from the out-of-domain data is used for the in-domain data; while for the \u201cmulti domain\u201d and \u201cmixed fine tuning\u201d methods, we use a vocabulary obtained from the mixed in-domain and out-of-domain data for all the training stages." + ], + [ + "We conducted NMT domain adaptation experiments in two different settings as follows:" + ], + [ + "Chinese-to-English translation was the focus of the high quality in-domain corpus setting. We utilized the resource rich patent out-of-domain data to augment the resource poor spoken language in-domain data. The patent domain MT was conducted on the Chinese-English subtask (NTCIR-CE) of the patent MT task at the NTCIR-10 workshop BIBREF9 . The NTCIR-CE task uses 1000000, 2000, and 2000 sentences for training, development, and testing, respectively. The spoken domain MT was conducted on the Chinese-English subtask (IWSLT-CE) of the TED talk MT task at the IWSLT 2015 workshop BIBREF10 . The IWSLT-CE task contains 209,491 sentences for training. We used the dev 2010 set for development, containing 887 sentences. We evaluated all methods on the 2010, 2011, 2012, and 2013 test sets, containing 1570, 1245, 1397, and 1261 sentences, respectively." + ], + [ + "Chinese-to-Japanese translation was the focus of the low quality in-domain corpus setting. We utilized the resource rich scientific out-of-domain data to augment the resource poor Wikipedia (essentially open) in-domain data. The scientific domain MT was conducted on the Chinese-Japanese paper excerpt corpus (ASPEC-CJ) BIBREF11 , which is one subtask of the workshop on Asian translation (WAT) BIBREF15 . The ASPEC-CJ task uses 672315, 2090, and 2107 sentences for training, development, and testing, respectively. The Wikipedia domain task was conducted on a Chinese-Japanese corpus automatically extracted from Wikipedia (WIKI-CJ) BIBREF12 using the ASPEC-CJ corpus as a seed. The WIKI-CJ task contains 136013, 198, and 198 sentences for training, development, and testing, respectively." + ], + [ + "For NMT, we used the KyotoNMT system BIBREF16 . The NMT training settings are the same as those of the best systems that participated in WAT 2016. The sizes of the source and target vocabularies, the source and target side embeddings, the hidden states, the attention mechanism hidden states, and the deep softmax output with a 2-maxout layer were set to 32,000, 620, 1000, 1000, and 500, respectively. We used 2-layer LSTMs for both the source and target sides. ADAM was used as the learning algorithm, with a dropout rate of 20% for the inter-layer dropout, and L2 regularization with a weight decay coefficient of 1e-6. The mini batch size was 64, and sentences longer than 80 tokens were discarded. We early stopped the training process when we observed that the BLEU score of the development set converges. For testing, we self ensembled the three parameters of the best development loss, the best development BLEU, and the final parameters. Beam size was set to 100.", + "For performance comparison, we also conducted experiments on phrase based SMT (PBSMT). We used the Moses PBSMT system BIBREF17 for all of our MT experiments. For the respective tasks, we trained 5-gram language models on the target side of the training data using the KenLM toolkit with interpolated Kneser-Ney discounting, respectively. In all of our experiments, we used the GIZA++ toolkit for word alignment; tuning was performed by minimum error rate training BIBREF18 , and it was re-run for every experiment.", + "For both MT systems, we preprocessed the data as follows. For Chinese, we used KyotoMorph for segmentation, which was trained on the CTB version 5 (CTB5) and SCTB BIBREF19 . For English, we tokenized and lowercased the sentences using the tokenizer.perl script in Moses. Japanese was segmented using JUMAN BIBREF20 .", + "For NMT, we further split the words into sub-words using byte pair encoding (BPE) BIBREF21 , which has been shown to be effective for the rare word problem in NMT. Another motivation of using sub-words is making the different domains share more vocabulary, which is important especially for the resource poor domain. For the Chinese-to-English tasks, we trained two BPE models on the Chinese and English vocabularies, respectively. For the Chinese-to-Japanese tasks, we trained a joint BPE model on both of the Chinese and Japanese vocabularies, because Chinese and Japanese could share some vocabularies of Chinese characters. The number of merge operations was set to 30,000 for all the tasks." + ], + [ + "Tables 1 and 2 show the translation results on the Chinese-to-English and Chinese-to-Japanese tasks, respectively. The entries with SMT and NMT are the PBSMT and NMT systems, respectively; others are the different methods described in Section \"Methods for Comparison\" . In both tables, the numbers in bold indicate the best system and all systems that were not significantly different from the best system. The significance tests were performed using the bootstrap resampling method BIBREF22 at $p < 0.05$ .", + "We can see that without domain adaptation, the SMT systems perform significantly better than the NMT system on the resource poor domains, i.e., IWSLT-CE and WIKI-CJ; while on the resource rich domains, i.e., NTCIR-CE and ASPEC-CJ, NMT outperforms SMT. Directly using the SMT/NMT models trained on the out-of-domain data to translate the in-domain data shows bad performance. With our proposed \u201cMixed fine tuning\" domain adaptation method, NMT significantly outperforms SMT on the in-domain tasks.", + "Comparing different domain adaptation methods, \u201cMixed fine tuning\u201d shows the best performance. We believe the reason for this is that \u201cMixed fine tuning\u201d can address the over-fitting problem of \u201cFine tuning.\u201d We observed that while \u201cFine tuning\u201d overfits quickly after only 1 epoch of training, \u201cMixed fine tuning\u201d only slightly overfits until covergence. In addition, \u201cMixed fine tuning\u201d does not worsen the quality of out-of-domain translations, while \u201cFine tuning\u201d and \u201cMulti domain\u201d do. One shortcoming of \u201cMixed fine tuning\u201d is that compared to \u201cfine tuning,\u201d it took a longer time for the fine tuning process, as the time until convergence is essentially proportional to the size of the data used for fine tuning.", + "\u201cMulti domain\u201d performs either as well as (IWSLT-CE) or worse than (WIKI-CJ) \u201cFine tuning,\u201d but \u201cMixed fine tuning\u201d performs either significantly better than (IWSLT-CE) or is comparable to (WIKI-CJ) \u201cFine tuning.\u201d We believe the performance difference between the two tasks is due to their unique characteristics. As WIKI-CJ data is of relatively poorer quality, mixing it with out-of-domain data does not have the same level of positive effects as those obtained by the IWSLT-CE data.", + "The domain tags are helpful for both \u201cMulti domain\u201d and \u201cMixed fine tuning.\u201d Essentially, further fine tuning on in-domain data does not help for both \u201cMulti domain\u201d and \u201cMixed fine tuning.\u201d We believe the reason for this is that the \u201cMulti domain\u201d and \u201cMixed fine tuning\u201d methods already utilize the in-domain data used for fine tuning." + ], + [ + "In this paper, we proposed a novel domain adaptation method named \u201cmixed fine tuning\u201d for NMT. We empirically compared our proposed method against fine tuning and multi domain methods, and have shown that it is effective but is sensitive to the quality of the in-domain data used.", + "In the future, we plan to incorporate an RNN model into our current architecture to leverage abundant in-domain monolingual corpora. We also plan on exploring the effects of synthetic data by back translating large in-domain monolingual corpora. " + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-0930/instruction.md b/qasper-0930/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..8ffeae8d062fbc0a9f150930cb14a740975c1ed2 --- /dev/null +++ b/qasper-0930/instruction.md @@ -0,0 +1,92 @@ +Name of Paper: Rethinking travel behavior modeling representations through embeddings + +Question: How do their interpret the coefficients? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Representing categorical variables", + "The concept of text embeddings", + "Travel behaviour embeddings", + "Travel behaviour embeddings ::: The general idea", + "Travel behaviour embeddings ::: Methodology", + "An experiment with mode choice", + "An experiment with mode choice ::: The Swissmetro dataset", + "An experiment with mode choice ::: Principles for the model specification" + ], + "paragraphs": [ + [ + "Since their early days, representation in random utility behavior models has followed generally quite clear principles. For example, numeric quantities like travel time and cost may be directly used or transformed depending on observed non-linear efects (e.g. using log). Numeric variables that are not \u201cquantities\" per se, such as age or even geographic coordinates tend to be discretized and then transformed into vectors of dummy variables. Similarly, categorical variables such as education level or trip purpose are already discrete, and thus are also usually \u201cdummyfied\". Then, we may interact any subset of the above by combining (typically, multiplying) them, as long as we get in the end a vector of numeric values that can be incorporated in a statistical model, a linear one in the case of the most common logit model.", + "There are however phenomena that are hard to represent, and modelers end up struggling to find the right representation. For example, influence of social interactions between different persons, hierarchical decision making, autocorrelated nature of time and space, or abstract concepts such as accessibility, attitudes, personality traits and so on. The point here, is that the nature of our models seems to enforce a compromise between the true semantics of a variable (i.e. the \u201cmeaning\" of a certain information for the decision making process) and its realisation in practice. And that further research should be done to find new representation paradigms.", + "Historically speaking, the natural language processing (NLP) field has had similar dilemmas for decades, and for a while two general trends were competing: the statistical modeling approaches, and the linguistic theory based approaches. The former relied on simple representations, such as vector frequencies, or dummy variables, to become practical, while the latter used domain knowledge such as grammars or logic. Until recently, neither had considerable success in making machines able to understand or generate human language, but developments in deep neural networks together with overwhelmingly massive amounts of data (i.e. the World Wide Web) brought them to a new area, where the two are approaching each other and achieving hitherto results considered extremely hard, such as question answering, translation, next word prediction. One of the key concepts in this revolution is that of embeddings, which will be further explained in this paper.", + "Our focus here is on the representation of categorical variables. The default paradigm is dummy variables (also known as \u201cone-hot-encoding\" in machine learning literature), which have well-known limitations, namely the explosion of dimensionality and enforced ortogonality. The former happens because we assign one new \u201cdummy\" variable to each of D-1 categories, and easily go from a small original variable specification to one with hundreds of variables, bringing problems in model estimation and analysis. This often affects the data collection process itself. Since one doesn't want to end up with too many categories, we might as well give less options in a survey, or decrease the resolution of a sensor. The problem of enforced ortogonality relates to the fact that, in a dummy encoding, all categories become equidistant. The similarity between \u201cstudent\" and \u201cemployed\" is the same as between \u201cstudent\" and \u201cretired\", which in many cases (e.g. mode choice, departure time choice) goes against intuition. Other encoding methods exist, such as contrasted encoding or principal components analysis (PCA). The former ends up being a subtle variation on the dummy approach, but the latter already provides an interesting answer to the problem: categories are no longer forcibly equidistant, and the number of variables can be much reduced. However, it is a non-supervised approach. The distance between \u201cstudent\" and \u201cemployed\" will always be the same, regardless of the problem we are solving, but this may be intuitively illogical if we consider car ownership versus departure time choice models for example.", + "The key idea in this paper is to introduce a method, called Travel Behavior embeddings, that borrows much from the NLP concept. This method serves to encode categorical variables, and is dependent on the problem at hand. We will focus on mode choice, and test on a well-known dataset, by comparing with both dummy and PCA encoding. All the dataset and code are made openly available, and the reader can follow and generate results him/herself using an iPython notebook included. Our ultimate goal is certainly that the reader reuses our PyTre package for own purposes.", + "This paper presents some results and conclusions, after a relatively long exploration and analysis process, including other datasets and code variations not mentioned here for interest of clarity and replicability. While we show these concepts to be promising and innovative in this paper, one should be wary of over-hyping yet another Machine Learning/Artificial Intelligence concept: after all, Machine Learning is still essentially based on statistics. In NLP, the number of different words in consideration at a given moment can be in order of tens of thousands, while our categorical variables rarely go beyond a few dozens. This means that, for example, it becomes clear later that the least number of original categories, the less the benefit of embeddings (in the limit, a binary variable like gender, is useless to do embeddings with), and also that if we do get a significantly large and statistically representative dataset, a dummy variables representation is sufficient. We will quickly see, however, that complexity can grow quick enough to justify an embeddings based method even if without the shockingly better performance observed in NLP applications." + ], + [ + "We are generally concerned with random utility maximization (RUM) models, for they have a dominant role in travel behavior modeling. The nature of such models is predominantly numeric, linear, and quite often strictly flat (notwithstanding hierarchical variations, such as nested models BIBREF1, hierarchical Bayes BIBREF2, or non-linear transformations). As a consequence, while numerical variables (e.g. travel time, cost, or income) can be directly used as available, perhaps subject to transformations or segmentation, nominal ones bring about a greater challenge. We tend to enforce a limited set of treatments such as:", + "Dummy variables, or one-hot encoding - for each categorical variable $v$ with D categories, we get D-1 binary variables (the \u201cdummies\"). At each input vector $x_n$, with categorical value $v=d$, the value \u201c1\" will be assigned to the corresponding dummy, while \u201c0\" to all others. If $v$ corresponds to the \u201cdefault\" category, all dummies are \u201c0\".", + "Contrast encoding BIBREF3 - same as dummy encoding, but instead of \u201c1\" for each category, we have a value that results from a contrasting formula. There are many different formulas (e.g. Helmert, Sum, Backward Difference), but all consist of subtracting the mean of the target variable, for a given category, with a general stastic (e.g. the mean of the dependent variable for all categories; the mean of the dependent variable in the previous category in an ordered list).", + "Principal Components Analysis (PCA) - run the PCA algorithm on the data matrix obtained by dummy representation of the categorical variable, then re-represent it with the corresponding projected eigenvector coefficients. One selects K eigenvectors (e.g. according to a variance explained rule), and thus each category is mapped to a vector of K real values.", + "Segmenting models, mixture models - A general alternative to categorical data representation is in fact to avoid it in the first place. One obvious method would be through creating hierarchical disaggregate methods (e.g. one per category). This is not in itself a representation paradigm, but an alternative way to see this problem. It certainly raises scalability and inference concerns.", + "In datasets where behavior heterogeneity is high, and number of observations is significantly smaller than population size, increasing dimensionality by adding a variable per each category is very risky because the amount of data that is in practice usable to estimate each new coefficient becomes insufficient. A simple intuition here is by considering that, for a dummy variable that is only \u201c1\" for a few observations in the dataset, its coefficient will be \u201cactivated\" only that small number of times. If there is a lot of variance in the associated behavior, the variance of the coefficient will also be large, and the coefficient will be considered statistically insignificant.", + "The benefit of representations that map into a latent space, like embeddings and PCA, is that such a space is inevitably shared, and thus every observation contributes indirectly to all category variables. This comes with no interpretability cost, because one can always map to the \u201cdummy\" space and analyse the individual coefficients, as will be shown in our experiments." + ], + [ + "The idea of text embeddings comes from a simple re-representation necessity. A natural-language processing system is itself also a numeric machine, therefore it requires each individual word in a dictionary to match its own numeric representation. Just as in our travel models, a possible solution has been to use dummy variables, and it is quite obvious that the dimensionality of such a one-hot encoding vector, quickly becomes overwhelming. Think for example next word prediction algorithm, like the one we have in our smartphones. It is essentially a skip-gram BIBREF4 model that predicts the next word, given the n words before. The English dictionary has about 300000 words, and if we have about 5 words before for context, the number of independent variables of the model would become 1.5 million!", + "The goal of text embeddings algorithms (e.g. Word2Vec BIBREF5) is to a) reduce the representation of each word to a computationally acceptable dimension, while simultaneously b) learning the semantic distance between different words. In other words, the euclidean distance of semantically related words (e.g. \u201cdog\" and \u201ccat\") in this new space should be smaller than unrelated words (e.g. \u201cdog\" and \u201coptimize\"). As mentioned before, in a dummy (or one-hot) encoding, all distances between words are equal by definition.", + "The word embeddings methodology is very well explained in several webpages such as BIBREF6, so the reader is strongly encouraged to visit them first. However, for the sake of completeness, we summarize here the general idea.", + "Imagine the following task: given a word $w_i$ in a text, predict the next word $w_o$. If we solve it with a neural network model, we could have the architecture in Figure FIGREF8, where the input consists simply of the one-hot-encoding representation of the word (i.e. one dummy variable for each word in a dictionary of dimensionality $D$), and the output corresponds to the probability of each word in the dictionary being the next one (also a vector with dimensionality $D$).", + "The output layer thus consists simply of a softmax function. In other words, exactly the classical multinomial logit formulation that we would have in an RUM, in which each different word corresponds to an \u201calternative\".", + "The concept of embeddings is directly associated to the hidden layer, which is a set of linear activation neurons, typically with a dimensionality $K< BYR RRAAMAAN DAARISTAANA AMAANAY (T1-1-50-18)", + " DWWRA HAWLER CHIRAAYA SARDAAN NABWW (T1-1-50-19)", + " SAALL DYWAAR QWTAABXAANA NACHIN (T1-1-50-20)", + " XWENDIN ANDAAMAANY GASHA (T1-1-50-21)", + " NAMAAM WRYAA KIRD PSHWWDAA (T1-1-50-22)", + " DARCHWWY DAKAN DAKAWET (T1-1-50-23)", + " CHAND BIRAAT MAQAST (T1-1-50-24)", + " BAAXCHAKAY DAAYK DARCHWWY (T1-1-50-25)", + " RROZH JWAAN DAKAWET ZYAANYAAN (T1-1-50-26)", + "" + ], + [ + "The corpus includes 2000 sentences. Theses sentence are random renderings of 200 sentences, which we have taken from Sorani Kurdish books of the grades one to three of the primary school in the Kurdistan Region of Iraq. The reason that we have taken only 200 sentences is to have a smaller dictionary and also to increase the repetition of each word in the narrated speech. We transformed the corpus sentences, which are in Persian-Arabic script, into the format which complies with the suggested phones for the related Sorani letters (see Section SECREF6)." + ], + [ + "Two thousand narration files were created. We used Audacity to record the narrations. We used a normal laptop in a quiet room and minimized the background noise. However, we could not manage to avoid the noise of the fan of the laptop. A single speaker narrated the 2000 sentences, which took several days. We set the Audacity software to have a sampling rate of 16, 16-bit bit rate, and a mono (single) channel. The noise reduction db was set to 6, the sensitivity to 4.00, and the frequency smoothing to 0." + ], + [ + "We created the language from the transcriptions. The model was created using CMUSphinx in which (fixed) discount mass is 0.5, and backoffs are computed using the ratio method. The model includes 283 unigrams, 5337 bigrams, and 6935 trigrams." + ], + [ + "We presented a dataset, BD-4SK-ASR, that could be used in training and developing an acoustic model for Automatic Speech Recognition in CMUSphinx environment for Sorani Kurdish. The Kurdish books of grades one to three of primary schools in the Kurdistan Region of Iraq were used to extract 200 sample sentences. The dataset includes the dictionary, the phoneset, the transcriptions of the corpus sentences using the suggested phones, the recorded narrations of the sentences, and the acoustic model. The dataset could be used to start experiments on Sorani Kurdish ASR.", + "As it was mentioned before, research and development on Kurdish ASR require a huge amount of effort. A variety of areas must be explored, and various resources must be collected and developed. The multi-dialect characteristic of Kurdish makes these tasks rather demanding. To participate in these efforts, we are interested in the expansion of Kurdish ASR by developing a larger dataset based on larger Sorani corpora, working on the other Kurdish dialects, and using new environments for ASR such as Kaldi." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-1071/instruction.md b/qasper-1071/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..d99c0cfbc505f17105d7a488db1164d7f9e35e36 --- /dev/null +++ b/qasper-1071/instruction.md @@ -0,0 +1,58 @@ +Name of Paper: Grounding the Semantics of Part-of-Day Nouns Worldwide using Twitter + +Question: How many languages are included in the tweets? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Materials and methods", + "Results and validation", + "Worldwide average greeting times", + "Daily analysis", + "Conclusion", + "Acknowledgments" + ], + "paragraphs": [ + [ + "Human languages are intertwined with their cultures and societies, having evolved together, reflecting them and in turn shaping them BIBREF0 , BIBREF1 . Part-of-day nouns (e.g. \u2018morning\u2019 or \u2018night\u2019) are an example of this, as their meaning depends on how each language's speakers organize their daily schedule. For example, while the morning in English-speaking countries is assumed to end at noon, the Spanish term (\u2018ma\u00f1ana\u2019) is understood to span until lunch time, which normally takes place between 13:00 and 15:00 in Spain. It is fair to relate this difference to cultural (lunch being the main meal of the day in Spain, as opposed to countries like the uk, and therefore being a milestone in the daily timetable) and sociopolitical factors (the late lunch time being influenced by work schedules and the displacement of the Spanish time zones with respect to solar time). Similar differences have been noted for different pairs of languages BIBREF2 and for cultures using the same language BIBREF3 , based on manual study, field research and interviews with natives. Work on automatically extracting the semantics of part-of-day nouns is scarce, as classic corpora are not timestamped. Reiter2003a,Reiter2003b overcome it by analyzing weather forecasts and aligning them to timestamped simulations, giving approximate groundings for time-of-day nouns and showing idiolectal variation on the term \u2018evening\u2019, but the work is limited to English.", + "The relation between language and sociocultural factors implies that the semantics of part-of-day nouns (e.g. 'end of the morning') cannot be studied in isolation from social habits (e.g. 'typical lunch time'). A relevant study of such habits is done by walch2016global, who develop an app to collect sleep habits from users worldwide. While they do not study the meaning of words, their insights are used for validation.", + "We propose a new approach to study the semantics of part-of-day nouns by exploiting Twitter and the time-specific greetings (e.g. \u2018good morning\u2019) used in different cultures. By mining tweets with these greetings, we obtain a large, worldwide sample of their usage. Since many tweets come with time and geolocation metadata, we can know the local time and country at which each one was emitted. The main contribution of the paper is to show how it is possible to learn the semantics of these terms in a much more extensive way than previous work, at a global scale, with less effort and allowing statistical testing of differences in usage between terms, countries and languages." + ], + [ + "To ground the semantics of greetings we used 5 terms as seeds: \u2018good morning\u2019, \u2018good afternoon\u2019, \u2018good evening\u2019, \u2018good night\u2019 and \u2018hello\u2019 (a time-unspecific greeting used for comparison). We translated them to 53 languages and variants using Bing translator. We use italics to refer to greetings irrespective of the language. 172,802,620 tweets were collected from Sept. 2 to Dec. 7 2016.", + "For some languages (e.g. Spanish), there is no differentiation between \u2018good evening\u2019 and \u2018good night\u2019, and they both are translated to the same expression. For some others, some expressions cannot be considered equivalent, e.g. \u2018good morning\u2019 is translated to \u2018bonjour\u2019 in French, which is however commonly used as \u2018hello\u2019, or simply as \u2018good day\u2019.", + "Text preprocessing is not necessary: we rely on metadata, not on the tweet itself, and only the seed words are needed to categorize tweets within a part of day. To clean up the data, we removed retweets, as they last for hours, biasing the temporal analysis. Duplicate tweets were kept, as similar messages from different days and users (e.g. \u2018good night!\u2019) are needed for the task at hand. Tweets need to be associated with a timestamp and country-level geolocation. Tweets have a creation time, composed of a utc time and a utc offset that varies depending on the time zone. However, most tweets are not geolocated and we must rely on the data provided by the user. This may be fake or incomplete, e.g. specifying only a village. We used fine-grained databases to do the mapping to the country level location and performed a sanity check, comparing the Twitter offset to the valid set of offsets for that country, to reduce the amount of wrongly geolocated tweets. Comparing the solar and standard time could provide more insights, but this requires a fine-grained geolocation of the tweets. We obtained a dataset of 10,523,349 elements, available at https://github.com/aghie/peoples2018grounding: 4,503,077 good morning's, 599,586 good afternoon's, 214,231 good evening's, 880,003 good night's and 4,359,797 hello's." + ], + [ + "Given a country, some of the tweets are written in foreign languages for reasons like tourism or immigration. This paper refers to tweets written in official or de facto languages, unless otherwise specified. Also, analyzing differences according to criteria such as gender or solar time can be relevant. As determining the impact of all those is a challenge on its own, we focus on the primary research question: can we learn semantics of the part-of-day nouns from simple analysis of tweets? To verify data quality, good morning tweets were revised: out of 1 000 random tweets from the usa, 97.9% were legitimate greetings and among the rest, some reflected somehow that the user just started the day (e.g \u2018Didn't get any good morning sms\u2019). We did the same for Spain (98,1% legitimate), Brazil (97.8%) and India (99.6%).", + "Existing work and dated events are used to ratify the results presented below." + ], + [ + "Table TABREF7 shows the average greeting times for the countries from which we collected more data. Asian, African and American countries tend to begin the day earlier than Europe (with exceptions, e.g. Germany). The table reflects that countries in southern Europe (e.g. Spain, Portugal or Greece) start the day later than the northern ones (the Netherlands or uk). For some countries, e.g. France, this information is known to be biased, as good morning (\u2018bonjour\u2019) is used all along the day. A validation at a fine-grained scale is unfeasible, but the results at the country level are in line with Figure 3 of walch2016global, e.g., they state that Japan, the usa or Germany have earlier wake up times than Spain, Brazil or Turkey.", + "The average greeting times for good afternoon reveal insights that may stem from cultural differences (e.g. lunch break time). Anglo-Saxon and South Asian countries have the earliest afternoon (with averages between 13:00 and 14:00), while in Mediterranean countries the morning lasts longer (average greeting times for good afternoon around 15:00 or 16:00). A number of countries under the influence of the United Kingdom, such as the United States, Pakistan or India show earlier afternoon times. The opposite happens in South America, historically influenced by Portuguese and Spanish colonialism during the Early modern period, which exhibits later afternoon times.", + "This poses interesting questions for future work, such as whether there is a particular reason that could justify this behavior, like having more similar cuisine practices. In this context, the adoption of food practices in colonialism has been already studied by anthropologists and historians BIBREF4 . trigg2004food points out how in the early period of the Spanish colonialism in the Americas, they `civilized' the Indigenous community by making them adopt manners, dress and customs. She points that the role of food was specially relevant due to its large social component, and was not limited to the way the food was eaten, but also prepared, served and consumed.", + "Twitter also reflects differences between countries regarding night life. On the one hand, Anglo-Saxon countries wish good night earlier (from 19:49 in the uk to 21:10 in Canada) than other societies. On the other hand, southern European countries go to bed later, and some of them even wish a good night after midnight (e.g. Spain). Comparing to BIBREF5 , we find similar tendencies. For example, in their study Spain, Turkey or Brazil use the smartphone until later than Canada, the usa or the uk, and therefore they go later to bed. Our Twitter approach also captures the particular case of Japanese mentioned by BIBREF5 : they wake up very early, but use the smartphone until late in the night, suggesting a later bed time.", + "A fine-grained analysis shows how Twitter captures other cultural and working differences. Figure FIGREF8 charts the average day time for good morning for the usa, Brazil, Spain and India during part of the polling period. The time peaks in the weekends for many of the countries, showing that Twitter captures how business and work are reduced during holidays, resulting in later wake up times.", + "However, this is not visible in some countries where working conditions are sometimes questioned BIBREF6 : for India the weekend peak is less pronounced, which can be considered as an indicator that a significant part of its population does not enjoy work-free weekends.", + "The usage of part-of-day expressions can be helpful to understand more complex issues, such as how foreigners integrate into a country and adapt to its daily schedule. We take the usa as example, as it has a large foreign community of Spanish speakers, mainly from Mexico (and in a smaller proportion from other Latin American countries). If we calculate the average day time for the Spanish form of \u2018good morning\u2019 (\u2018buenos d\u00edas\u2019) in the usa, we obtain that the result is 08:09, while the corresponding English greeting's average time is 08:33. This is reinforced by Figure FIGREF10 , where \u2018buenos d\u00edas\u2019 average day time is consistently lower than \u2018good morning\u2019. This would be in line to their presence in low-wage jobs that require to wake up earlier, e.g. waiter, cleaning or construction work BIBREF7 , BIBREF8 .", + "It is worth noting that, assuming that these \u2018buenos d\u00edas\u2019 greetings come from latinos, those in the usa wake up even earlier than in their countries of origin (see Table TABREF7 ).", + "Figure FIGREF8 also shows how national holidays influence societies. For example, Nov. 2 (Day of the Dead) and Nov. 15 (Proclamation of the Republic) are holidays in Brazil, producing a peak in that country's graph similar to the behavior in the weekends. Similarly, Nov. 1 (All Saints' Day) and Dec. 6 (Constitution Day) are holidays in Spain and similar peaks are observed too. From Figure FIGREF10 we can see how Thanksgiving (Nov. 24 in 2016) reflects a four-day weekend in the usa: many businesses allow employees to take this holiday from Thursday, resulting into a gradual and increasing peak that spans until Sunday. This is captured by the English good mornings, but not by the Spanish ones. The day after the usa 2016 elections (Nov. 9), a valley occurs on the good morning time for the States (Figure FIGREF8 ). The winner was not known until 03:00, suggesting that the distribution of greetings reflects social behaviors in other special events." + ], + [ + "Twitter can be used to do a time-of-day analysis, e.g., as said in \u00a7 SECREF6 , \u2018bonjour\u2019 is assumed to be used all along the day. To test this, we take Canada, where French and English are official languages. Figure FIGREF12 shows how \u2018bonjour\u2019 and \u2018salut\u2019 (\u2018hello\u2019) are used all along the day, while \u2018good morning\u2019 is used in the morning hours. English and French hello's share a similar distribution.", + "Figure FIGREF13 shows a greeting area chart for the usa, showing how \u2018good evening\u2019 and \u2018good afternoon\u2019 are well differentiated, with the transition happening over 16:30. This contrasts to countries such as Spain (Figure FIGREF14 ), where the language has a single word (\u2018tarde\u2019) for \u2018evening\u2019 and \u2018afternoon\u2019, whose greeting spans from over 14:00, as the morning ends late (see \u00a7 SECREF1 ), to 21:00.", + "Area plots like these give a clear picture of the semantics of part-of-day nouns, as they depict the exact times when they are used. The precise semantics can be grounded more rigorously using statistical testing to know the exact time intervals at which people significantly use a specific greeting.", + "For example, to know when to switch from good morning to good afternoon in Spanish, we can: (1) group the number of \u2018buenos d\u00edas\u2019 (\u2018good morning\u2019) and \u2018buenas tardes\u2019 (\u2018good afternoon\u2019) by intervals of 10 minutes, and (2) apply a binomial test to each interval, to determine if one of the greetings is significantly more likely to occur than the other (assuming equal probability of occurrence). For example, for Spain, we obtain that the morning ends at 14:00 (p-value= INLINEFORM0 at 14:00, 0.09 at 14:10) and the afternoon starts at 14:40 (p-value becomes statistically significant again with INLINEFORM1 , showing a significant majority of good afternoon)." + ], + [ + "We crawled Twitter to study the semantics of part-of-day nouns in different countries and societies, showed examples from the polled period and ratified them against existing research and dated events. For space reasons we cannot show insights for all scenarios, but full results are at https://github.com/aghie/peoples2018grounding." + ], + [ + "DV and CGR receive funding from the European Research Council (ERC), under the European Union's Horizon 2020 research and innovation programme (FASTPARSE, grant agreement No 714150), from the TELEPARES-UDC project (FFI2014-51978-C2-2-R) and the ANSWER-ASAP project (TIN2017-85160-C2-1-R) from MINECO, and from Xunta de Galicia (ED431B 2017/01)." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-1076/instruction.md b/qasper-1076/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..0a017848316e590c4c5db110b614c5907ae7ace4 --- /dev/null +++ b/qasper-1076/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: QA4IE: A Question Answering based Framework for Information Extraction + +Question: Was this benchmark automatically created from an existing dataset? \ No newline at end of file diff --git a/qasper-1077/instruction.md b/qasper-1077/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..963798ba524344cb186eeea3df4a7a381aaed112 --- /dev/null +++ b/qasper-1077/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: A Resource for Studying Chatino Verbal Morphology + +Question: How does morphological analysis differ from morphological inflection? \ No newline at end of file diff --git a/qasper-1078/instruction.md b/qasper-1078/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..44cdf590750f103de754f399bbe83e6b3f9d1f84 --- /dev/null +++ b/qasper-1078/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: A Resource for Studying Chatino Verbal Morphology + +Question: What was the criterion used for selecting the lemmata? \ No newline at end of file diff --git a/qasper-1082/instruction.md b/qasper-1082/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..1ed6721c1686817547056b7a84acdf8f4a998b62 --- /dev/null +++ b/qasper-1082/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: A Resource for Studying Chatino Verbal Morphology + +Question: How was annotation done? \ No newline at end of file diff --git a/qasper-1083/instruction.md b/qasper-1083/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..84f03f39e3c22ce12666350608a4f8a40ac7bd10 --- /dev/null +++ b/qasper-1083/instruction.md @@ -0,0 +1,74 @@ +Name of Paper: A Resource for Studying Chatino Verbal Morphology + +Question: How was the data collected? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "The Chatino Language", + "The Chatino Language ::: Typology and Writing System", + "The Chatino Language ::: Verb Morphology", + "The Resource", + "Baseline Results ::: Inflectional realization", + "Baseline Results ::: Morphological Analysis", + "Baseline Results ::: Lemmatization", + "Related Work", + "Conclusion", + "Acknowledgements" + ], + "paragraphs": [ + [ + "The recent years have seen unprecedented forward steps for Natural Language Processing (NLP) over almost every NLP subtask, relying on the advent of large data collections that can be leveraged to train deep neural networks. However, this progress has solely been observed in languages with significant data resources, while low-resource languages are left behind.", + "The situation for endangered languages is usually even worse, as the focus of the scientific community mostly relies in language documentation. The typical endangered language documentation process typically includes the creation of language resources in the form of word lists, audio and video recordings, notes, or grammar fragments, with the created resources then stored into large online linguistics archives. This process is often hindered by the so-called Transcription Bottleneck, but recent advances BIBREF0, BIBREF1 provide promising directions towards a solution for this issue.", + "However, language documentation and linguistic description, although extremely important itself, does not meaningfully contribute to language conservation, which aims to ensure that the language stays in use. We believe that a major avenue towards continual language use is by further creating language technologies for endangered languages, essentially elevating them to the same level as high-resource, economically or politically stronger languages.", + "The majority of the world's languages are categorized as synthetic, meaning that they have rich morphology, be it fusional, agglutinative, polysynthetic, or a mixture thereof. As Natural Language Processing (NLP) keeps expanding its frontiers to encompass more and more languages, modeling of the grammatical functions that guide language generation is of utmost importance. It follows, then, that the next crucial step for expanding NLP research on endangered languages is creating benchmarks for standard NLP tasks in such languages.", + "With this work we take a small first step towards this direction. We present a resource that allows for benchmarking two NLP tasks in San Juan Quiahije Chatino, an endangered language spoken in southern Mexico: morphological analysis and morphological inflection, with a focus on the verb morphology of the language.", + "We first briefly discuss the Chatino language and the intricacies of its verb morphology (\u00a7SECREF2), then describe the resource (\u00a7SECREF3), and finally present baseline results on both the morphological analysis and the inflection tasks using state-of-the-art neural models (\u00a7SECREF4). We make our resource publicly available online." + ], + [ + "Chatino is a group of languages spoken in Oaxaca, Mexico. Together with the Zapotec language group, the Chatino languages form the Zapotecan branch of the Otomanguean language family. There are three main Chatino languages: Zenzontepec Chatino (ZEN, ISO 639-2 code czn), Tataltepec Chatino (TAT, cta), and Eastern Chatino (ISO 639-2 ctp, cya, ctz, and cly) (E.Cruz 2011 and Campbell 2011). San Juan Quiahije Chatino (SJQ), the language of the focus of this study, belongs to Eastern Chatino, and is used by about 3000 speakers." + ], + [ + "Eastern Chatino languages , including SJQ Chatino, are intensively tonal BIBREF2, BIBREF3. Tones mark both lexical and grammatical distinctions in Eastern Chatino languages.", + "In SJQ Chatino, there are eleven tones. Three different systems for representing tone distinctions are employed in the literature: the S-H-M-L system of BIBREF2; the numeral system of BIBREF4; and the alphabetic system of BIBREF3. The correspondences among these three systems are given in Table . For present purposes, we will use numeral representations of the second sort. The number 1 represents a high pitch, 4 represents a low pitch, and double digits represent contour tones." + ], + [ + "SJQ Chatino verb inflection distinguishes four aspect/mood categories: completive (`I did'), progressive (`I am doing'), habitual (`I habitually do') and potential (`I might do'). In each of these categories, verbs inflect for three persons (first, second, third) and two numbers (singular, plural) and distinguish inclusive and exclusive categories of the first person plural (`we including you' vs `we excluding you'). Verbs can be classified into dozens of different conjugation classes. Each conjugation class involves its own tone pattern; each tone pattern is based on a series of three person/number (PN) triplets. A PN triplet [X, Y, Z] consists of three tones: tone X is employed in the third person singular as well as in all plural forms; tone Y is employed in the second person singular, and tone Z, in the third person singular. Thus, a verb's membership in a particular conjugation class entails the assignment of one tone triplet to completive forms, another to progressive forms, and a third to habitual and potential forms. The paradigm of the verb lyu1 `fall' in Table illustrates: the conjugation class to which this verb belongs entails the assignment of the triplet [1, 42, 20] to the completive, [1, 42, 32] to the progressive, and [20, 42, 32] to the habitual and potential. Verbs in other conjugation classes exhibit other triplet series." + ], + [ + "We provide a hand-curated collection of complete inflection tables for 198 lemmata. The morphological tags follow the guidelines of the UniMorph schema BIBREF6, BIBREF7, in order to allow for the potential of cross-lingual transfer learning, and they are tagged with respect to:", + "Person: first (1), second (2), and third (3)", + "Number: singular (SG) ad plural (PL)", + "Inclusivity (only applicable to first person plural verbs: inclusive (INCL) and exclusive (EXCL)", + "Aspect/mood: completive (CPL), progressive (PROG), potential (POT), and habitual (HAB).", + "Two examples of complete inflection tables for the verbs ndyu2 `fell from above' and lyu1 `fall' are shown in Table . Note how the first verb has the same PN triplet for all four aspect/mood categories, while the second paradigm is more representative in that it involves three different triplets (one for the completive, another for the progressive, and another for the habitual/potential). This variety is at the core of why the SJQ verb morphology is particularly interesting, and a challenging testcase for modern NLP systems.", + "In total, we end up with 4716 groupings (triplets) of a lemma, a tag-set, and a form; we split these groupings randomly into a training set (3774 groupings), a development set (471 groupings), and test set (471 groupings). Basic statistics of the corpus are outlined in Table 1 . Compared to all the other languages from the Unimorph project, this puts SJQ Chatino in the low- to mid-resource category, but nonetheless it is more than enough for benchmarking purposes." + ], + [ + "Inflectional realization defines the inflected forms of a lexeme/lemma. As a computational task, often referred to as simply \u201cmorphological inflection,\" inflectional realization is framed as a mapping from the pairing of a lemma with a set of morphological tags to the corresponding word form. For example, the inflectional realization of SJQ Chatino verb forms entails a mapping of the pairing of the lemma lyu1 `fall' with the tag-set 1;SG;PROG to the word form nlyon32.", + "Morphological inflection has been thoroughly studied in monolingual high resource settings, especially through the recent SIGMORPHON challenges BIBREF8, BIBREF9, BIBREF10, with the latest iteration focusing more on low-resource settings, utilizing cross-lingual transfer BIBREF11. We use the guidelines of the state-of-the-art approach of BIBREF12 that achieved the highest inflection accuracy in the latest SIGMORPHON 2019 morphological inflection shared task. Our models are implemented in DyNet BIBREF13.", + "Inflection results are outlined in Table . In the `standard' setting we simply train on the pre-defined training set, achieving an exact-match accuracy of 60% over the test set. Interestingly, the data augmentation approach of BIBREF12 that hallucinates new training paradigms based on character level alignments does not heed significant improvements in accuracy (only 2 percentage points increase, cf. with more than 15 percentage points increases in other languages). These results indicate that automatic morphological inflection for low-resource tonal languages like SJQ Chatino poses a particularly challenging setting, which perhaps requires explicit handling of tone information by the model." + ], + [ + "Morphological analysis is the task of creating a morphosyntactic description for a given word. It can be framed in a context-agnostic manner (as in our case) or within a given context, as for instance for the SIGMORPHON 2019 second shared task BIBREF11. We trained an encoder-decoder model that receives the form as character-level input, encodes it with a BiLSTM encoder, and then an attention enhanced decoder BIBREF14 outputs the corresponding sequence of morphological tags, implemented in DyNet. The baseline results are shown in Table . The exact-match accuracy of 67% is lower than the average accuracy that context-aware systems can achieve, and it highlights the challenge that the complexity of the tonal system of SJQ Chatino can pose." + ], + [ + "Lemmatization is the task of retrieving the underlying lemma from which an inflected form was derived. Although in some languages the lemma is distinct from all forms, in SJQ Chatino the lemma is defined as the completive third-person singular form. As a computational task, lemmatization entails producing the lemma given an inflected form (and possibly, given a set of morphological tags describing the input form). Popular approaches tackle it as a character-level edit sequence generation task BIBREF15, or as a character-level sequence-to-sequence task BIBREF16. For our baseline lemmatization systems we follow the latter approach. We trained a character level encoder-decoder model, similar to the above-mentioned inflection system, implemented in DyNet.", + "The baseline results, with and without providing gold morphological tags along with the inflected form as input, are outlined in Table . We find that automatic lemmatization in SJQ Chatino achieves fairly high accuracy even with our simple baseline models (89% accuracy, $0.27$ average Levenshtein distance) and that providing the gold morphological tags provides a performance boost indicated by small improvements on both metrics. It it worth noting, though, that these results are also well below the $94--95\\%$ average accuracy and $0.13$ average Levenshtein distance that lemmatization models achieved over 107 treebanks in 66 languages for the SIGMORPHON 2019 shared task BIBREF11." + ], + [ + "Our work builds and expands upon previous work on Indigenous languages of the Americas specifically focusing on the complexity of their morphology. Among other works similar to ours, BIBREF17 focused on the morphology of Dene verbs, BIBREF18 on Arapaho verbs, BIBREF19 on Shipibo-Konibo, and BIBREF20 on Saint Lawrence Island and Central Siberian Yupik. BIBREF21 describe an approach for elicit complete inflection paradigms, with experiments in languages like Nahuatl. Our resource is the first one for SJQ Chatino, but it also provides an exciting new data point in the computational study of morphological analysis, lemmatization, and inflection, as it is the first one in a tonal language with explicit tonal markings in the writing system. In a similar vein, the Oto-Manguean Inflectional Class Database BIBREF22 provides a valuable resource for studying the verbal morphology of Oto-Manguean languages (including a couple of other Chatino variants: Yaitepec and Zenzotepec Chatino) but not in a format suitable for computational experiments." + ], + [ + "We presented a resource of 198 complete inflectional paradigms in San Juan Quiahije Chatino, which will facilitate research in computational morphological analysis and inflection for low-resource tonal languages and languages of Mesoamerica. We also provide strong baseline results on computational morphological analysis, lemmatization, and inflection realization, using character-level neural encoder-decoder systems.", + "For future work, while we will keep expanding our resource to include more paradigms, we will also follow the community guidelines in extending our resource to include morphological analysis and inflection examples in context." + ], + [ + "Part of this work was done during the Workshop on Language Technology for Language Documentation and Revitalization. This material is based upon work generously supported by the National Science Foundation under grant 1761548." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-1085/instruction.md b/qasper-1085/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..fbd905252de30ee3cde4d2ece0b44c5e2e5c4bbd --- /dev/null +++ b/qasper-1085/instruction.md @@ -0,0 +1,77 @@ +Name of Paper: N-GrAM: New Groningen Author-profiling Model + +Question: On which task does do model do worst? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Final System", + "Data Analysis", + "Alternative Features and Methods: An Analysis of Negative Results", + "Supplementary Data and Features", + "Modelling", + "Results on Test Data", + "Conclusion" + ], + "paragraphs": [ + [ + "With the rise of social media, more and more people acquire some kind of on-line presence or persona, mostly made up of images and text. This means that these people can be considered authors, and thus that we can profile them as such. Profiling authors, that is, inferring personal characteristics from text, can reveal many things, such as their age, gender, personality traits, location, even though writers might not consciously choose to put indicators of those characteristics in the text. The uses for this are obvious, for cases like targeted advertising and other use cases, such as security, but it is also interesting from a linguistic standpoint.", + "In the shared task on author profiling BIBREF0 , organised within the PAN framework BIBREF1 , the aim is to infer Twitter users' gender and language variety from their tweets in four different languages: English, Spanish, Arabic, and Portuguese. Gender consists of a binary classification (male/female), whereas language variety differs per language, from 2 varieties for Portuguese (Brazilian and Portugal) to 7 varieties for Spanish (Argentina, Chile, Colombia, Mexico, Peru, Spain, Venezuela). The challenge is thus to classify users along two very different axes, and in four highly different languages \u2013 forcing participants to either build models that can capture these traits very generally (language-independent) or tailor-make models for each language or subtask.", + "Even when looking at the two tasks separately, it looks like the very same features could be reliable clues for classification. Indeed, for both profiling authors on Twitter as well as for discriminating between similar languages, word and character n-grams have proved to be the strongest predictors of gender as well as language varieties. For language varieties discrimination, the systems that performed best at the DSL shared tasks in 2016 (on test set B, i.e. social media) used word/character n-grams, independently of the algorithm BIBREF2 . The crucial contribution of these features was also observed by BIBREF3 , BIBREF4 , who participated in the 2017 DSL shared task with the two best performing systems. For author profiling, it has been shown that tf-idf weighted n-gram features, both in terms of characters and words, are very successful in capturing especially gender distinctions BIBREF5 . If different aspects such as language variety and gender of a speaker on Twitter might be captured by the same features, can we build a single model that will characterise both aspects at once?", + "In the context of the PAN 2017 competition on user profiling we therefore experimented with enriching a basic character and word n-gram model by including a variety of features that we believed should work. We also tried to view the task jointly and model the two problems as one single label, but single modelling worked best.", + "In this paper we report how our final submitted system works, and provide some general data analysis, but we also devote substantial space to describing what we tried (under which motivations), as we believe this is very informative towards future developments of author profiling systems." + ], + [ + "After an extensive grid-search we submitted as our final run, a simple SVM system (using the scikit-learn LinearSVM implementation) that uses character 3- to 5-grams and word 1- to 2-grams with tf-idf weighting with sublinear term frequency scaling, where instead of the standard term frequency the following is used:", + " INLINEFORM0 ", + "We ran the grid search over both tasks and all languages on a 64-core machine with 1 TB RAM (see Table TABREF2 for the list of values over which the grid search was performed). The full search took about a day to complete. In particular, using min_df=2 (i.e. excluding all terms that are used by only one author) seems to have a strong positive effect and greatly reduces the feature size as there are many words that appear only once. The different optimal parameters for different languages provided only a slight performance boost for each language. We decided that this increase was too small to be significant, so we decided to use a single parameter set for all languages and both tasks." + ], + [ + "The training dataset provided consist of 11400 sets of tweets, each set representing a single author. The target labels are evenly distributed across variety and gender. The labels for the gender classification task are `male' and `female'. Table TABREF4 shows the labels for the language variation task and also shows the data distribution across languages.", + "We produced two visualisations, one per label (i.e. variety and gender), in order to gain some insights that could help the feature engineering process. For the variety label we trained a decision tree classifier using word unigrams: although the performance is poor (accuracy score of 0.63) this setup has the benefit of being easy to interpret: Figure FIGREF3 shows which features are used for the first splits of the tree.", + "We also created a visualisation of the English dataset using the tool described in BIBREF6 , and comparing the most frequent words used by males to those used by females. The visualisation shown in Figure SECREF6 indicates several interesting things about the gendered use of language. The words used often by males and very seldom by females are often sport-related, and include words such as \u201cleague\u201d, and \u201cchelsea\u201d. There are several emojis that are used frequently by females and infrequently by males, e.g. \u201c\u201d, \u201c\u201d, as well as words like \u201ckitten\u201d, \u201cmom\u201d, \u201csister\u201d and \u201cchocolate\u201d. In the top right of the visualisation we see words like \u201ctrump\u201d and \u201csleep\u201d, which indicates that these words are used very frequently, but equally so by both genders. This also shows that distinguishing words include both time-specific ones, like \u201cgilmore\u201d and \u201cimacelebrityau\u201d, and general words from everyday life, which are less likely to be subject to time-specific trends, like \u201cplayer\u201d, and \u201cchocolate\u201d." + ], + [ + "This section is meant to highlight all of the potential contributions to the systems which turned out to be detrimental to performance, when compared to the simpler system that we have described in Section SECREF2 . We divide our attempts according to the different ways we attempted to enhance performance: manipulating the data itself (adding more, and changing preprocessing), using a large variety of features, and changing strategies in modelling the problem by using different algorithms and paradigms. All reported results are on the PAN 2017 training data using five-fold cross-validation, unless otherwise specified." + ], + [ + "We extended the training dataset by adding data and gender labels from the PAN 16 Author Profiling shared task BIBREF5 . However, the additional data consistently resulted in lower cross-validation scores than when using only the training data provided with the PAN 17 task. One possible explanation for this is that our unigram model captures aspects that are tied specifically to the PAN 17 dataset, because it contains topics that may not be present in datasets that were collected in a different time period. To confirm this, we attempted to train on English data from PAN 17 and predict gender labels for the English data from PAN 16, as well as vice versa. Training on the PAN 16 data resulted in an accuracy score of 0.754 for the PAN 17 task, and training on PAN 17 gave an accuracy score of 0.70 for PAN 16, both scores significantly lower than cross-validated results on data from a single year.", + "We attempted to classify the English tweets by Gender using only the data collected by BIBREF7 . This dataset consists of aggregated word counts by gender for about 14,000 Twitter users and 9 million Tweets. We used this data to calculate whether each word in our dataset was a `male' word (used more by males), or a `female' word, and classified users as male or female based on a majority count of the words they used. Using this method we achieved 71.2 percent accuracy for the English gender data, showing that this simple method can provide a reasonable baseline to the gender task.", + "We experimented with different tokenization techniques for different languages, but our average results did not improve, so we decided to use the default scikit-learn tokenizer.", + "We tried adding POS-tags to the English tweets using the spaCy tagger: compared to the model using unigrams only the performances dropped slightly for gender and a bit more for variety:", + "It is not clear whether the missed increase in performance is due to the fact that the data are not normal (i.e. the tokenizer is not Twitter specific) or to the fact that POS tags confuse the classifier. Considering the results we decided not to include a POS-tagger in the final system.", + "()", + "In April 2015, SwiftKey did an extensive report on emoji use by country. They discovered that emoji use varies across languages and across language varieties. For example, they found that Australians use double the average amount of alcohol-themed emoji and use more junk food and holiday emoji than anywhere else in the world.", + "We tried to leverage these findings but the results were disappointing. We used a list of emojis as a vocabulary for the td/idf vectorizer. Encouraged by the results of the SwiftKey report, we tried first to use emojis as the only vocabulary and although the results are above the baseline and also quite high considering the type of features, they were still below the simple unigram model. Adding emojis as extra features to the unigram model also did not provide any improvement.", + "Since emojis are used across languages we built a single model for the four languages. We trained the model for the gender label on English, Portuguese and Arabic and tested it on Spanish: the system scored 0.67 in accuracy.", + "We looked at accuracy scores for the English gender and variety data more closely. We tried different representations of the tweet texts, to see what kind of words were most predictive of variety and gender. Specifically, we look at using only words that start with an uppercase letter, only words that start with a lowercase letter, only Twitter handles (words that start with an \"@\") and all the text excluding the handles.", + "It is interesting that the accuracies are so high although we are using only a basic unigram model, without looking at the character n-grams that we include in our final model. Representing each text only by the Twitter handles used in that text results in 0.77 accuracy for variety, probably because users tend to interact with other users who are in the same geographic area. However, excluding handles from the texts barely decreases performance for the variety task, showing that while the handles can be discriminative, they are not necessary for this task. It is also interesting to note that for this dataset, looking only at words beginning with an uppercase character results in nearly the same score for the Gender task as we get when using all of the available text, while using only lowercase words decreases performance. The opposite is true for the variety task, where using lowercase-only words results in as good performance as using all the text, but using only uppercase words decreases accuracy by over 10 percent.", + "We tried using the counts of geographical names related to the language varieties were as a feature. We also treated this list of locations as vocabulary for our model. Both these approaches did not improve our model.", + "We then tried enriching the data to improve the Unigram model. For each of the language varieties, we obtained 100 geographical location names, representing the cities with the most inhabitants. When this location was mentioned in the tweet, the language variety the location was part of was added to the tweet.", + "We attempted to use Twitter handles in a similar manner. The 100 most-followed Twitter users per language variety were found and the language variety was added to the text when one of its popular Twitter users was mentioned.", + "Unfortunately, this method did not improve our model. We suspect that the information is being captured by the n-gram model, which could explain why this did not improve performance.", + "We have tried the partial setup of last year's winning system, GronUP BIBREF8 , with the distinction that we had to classify language variety instead of age groups. We have excluded the features that are language-dependent (i.e. pos-tagging and misspelling/typos), and experimented with various feature combinations of the rest while keeping word and character n-grams the same. We achieved average accuracy from 0.810 to 0.830, which is clearly lower than our simple final model." + ], + [ + "We tried to build a single model that predicts at the same time both the language variety and the gender of each user: as expected (since the task is harder) the performance goes down when compared to a model trained independently on each label. However, as highlighted in Table TABREF21 , the results are still surprisingly high. To train the system we simply merged the two labels.", + "We experimented with Facebook's FastText system, which is an out-of-the-box supervised learning classifier BIBREF9 . We used only the data for the English gender task, trying both tweet-level and author-level classification. We pre-processed all text with the NLTK Tweet Tokenizer and used the classification-example script provided with the FastText code base. Training on 3,000 authors and testing on 600 authors gave an accuracy score of 0.64. Changing the FastText parameters such as number of epochs, word n-grams, and learning rate showed no improvement. We achieved an accuracy on 0.79 when we attempted to classify on a per-tweet basis (300,000 tweets for training and 85,071 for test), but this is an easier task as some authors are split over the training and test sets. There are various ways to summarise per-tweet predictions into author-predictions, but we did not experiment further as it seemed that the SVM system worked better for the amount of data we have.", + "In the final system we used the SVM classifier because it outperformed all the others that we tried. Table TABREF23 highlights the results." + ], + [ + "For the final evaluation we submitted our system, N-GrAM, as described in Section 2. Overall, N-GrAM came first in the shared task, with a score of 0.8253 for gender 0.9184 for variety, a joint score of 0.8361 and an average score of 0.8599 (final rankings were taken from this average score BIBREF0 ). For the global scores, all languages are combined. We present finer-grained scores showing the breakdown per language in Table TABREF24 . We compare our gender and variety accuracies against the LDR-baseline BIBREF10 , a low dimensionality representation especially tailored to language variety identification, provided by the organisers. The final column, + 2nd shows the difference between N-GrAM and that achieved by the second-highest ranked system (excluding the baseline).", + "Results are broken down per language, and are summarised as both joint and average scores. The joint score is is the percentage of texts for which both gender and variety were predicted correctly at the same time. The average is calculated as the mean over all languages.", + "N-GrAM ranked first in all cases except for the language variety task. In this case, the baseline was the top-ranked system, and ours was second by a small margin. Our system significantly out-performed the baseline on the joint task, as the baseline scored significantly lower for the gender task than for the variety task." + ], + [ + "We conclude that, for the current author profiling task, a seemingly simple system using word and character n-grams and an SVM classifier proves very hard to beat. Indeed, N-GrAM turned out to be the best-performing out of the 22 systems submitted in this shared task. Using additional training data, `smart' features, and hand-crafted resources hurts rather than helps performance. A possible lesson to take from this would be that manually crafting features serves only to hinder a machine learning algorithm's ability to find patterns in a dataset, and perhaps it is better to focus one's efforts on parameter optimisation instead of feature engineering.", + "However, we believe that this is too strong a conclusion to draw from this limited study, since several factors specific to this setting need to be taken into account. For one, a support vector machine clearly outperforms other classifiers, but this does not mean that this is an inherently more powerful. Rather, we expect that an SVM is the best choice for the given amount of training data, but with more training data, a neural network-based approach would achieve better results.", + "Regarding the frustrating lack of benefit from more advanced features than n-grams, a possible explanation comes from a closer inspection of the data. Both the decision tree model (see Figure FIGREF3 ) and the data visualisation (see Figure SECREF6 ) give us an insight in the most discriminating features in the dataset. In the case of language variety, we see that place names can be informative features, and could therefore be used as a proxy for geographical location, which in turn serves as a proxy for language variety. Adding place names explicitly to our model did not yield performance improvements, which we take to indicate that this information is already captured by n-gram features. Whether and how geographical information in the text can be useful in identifying language variety, is a matter for future research.", + "In the case of gender, many useful features are ones that are highly specific to the Twitter platform (#iconnecthearts), time (cruz), and topics (pbsnewshour) in this dataset, which we suspect would not carry over well to other datasets, but provide high accuracy in this case. Conversely, features designed to capture gender in a more general sense do not yield any benefit over the more specific features, although they would likely be useful for a robust, cross-dataset system. These hypotheses could be assessed in the future by testing author profiling systems in a cross-platform, cross-time setting.", + " Scatter plot of terms commonly used by male and female English speakers." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-1103/instruction.md b/qasper-1103/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..ce8539ba1ccfd66110bcda6b63de0506fcc920d0 --- /dev/null +++ b/qasper-1103/instruction.md @@ -0,0 +1,176 @@ +Name of Paper: Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer + +Question: What improvement does the MOE model make over the SOTA on machine translation? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Conditional Computation", + "Our Approach: The Sparsely-Gated Mixture-of-Experts Layer", + "Related work on Mixtures of Experts", + "The Structure of the Mixture-of-Experts layer", + "Gating Network", + "The Shrinking Batch Problem", + "Network Bandwidth", + "Balancing Expert Utilization", + "1 Billion Word Language Modeling Benchmark", + "100 Billion Word Google News Corpus", + "Machine Translation (Single Language Pair)", + "Multilingual Machine Translation", + "Conclusion", + "Appendices", + "Load-Balancing Loss", + "Hierachical Mixture of Experts", + "1 Billion Word Language Modeling Benchmark - Experimental Details", + "100 Billion Word Google News Corpus - Experimental Details", + "Machine Translation - Experimental Details", + "Strictly Balanced Gating", + "Attention Function" + ], + "paragraphs": [ + [ + "Exploiting scale in both training data and model size has been central to the success of deep learning. When datasets are sufficiently large, increasing the capacity (number of parameters) of neural networks can give much better prediction accuracy. This has been shown in domains such as text BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , images BIBREF4 , BIBREF5 , and audio BIBREF6 , BIBREF7 . For typical deep learning models, where the entire model is activated for every example, this leads to a roughly quadratic blow-up in training costs, as both the model size and the number of training examples increase. Unfortunately, the advances in computing power and distributed computation fall short of meeting such demand.", + "Various forms of conditional computation have been proposed as a way to increase model capacity without a proportional increase in computational costs BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 . In these schemes, large parts of a network are active or inactive on a per-example basis. The gating decisions may be binary or sparse and continuous, stochastic or deterministic. Various forms of reinforcement learning and back-propagation are proposed for trarining the gating decisions.", + "While these ideas are promising in theory, no work to date has yet demonstrated massive improvements in model capacity, training time, or model quality. We blame this on a combination of the following challenges:", + "Modern computing devices, especially GPUs, are much faster at arithmetic than at branching. Most of the works above recognize this and propose turning on/off large chunks of the network with each gating decision.", + "Large batch sizes are critical for performance, as they amortize the costs of parameter transfers and updates. Conditional computation reduces the batch sizes for the conditionally active chunks of the network.", + "Network bandwidth can be a bottleneck. A cluster of GPUs may have computational power thousands of times greater than the aggregate inter-device network bandwidth. To be computationally efficient, the relative computational versus network demands of an algorithm must exceed this ratio. Embedding layers, which can be seen as a form of conditional computation, are handicapped by this very problem. Since the embeddings generally need to be sent across the network, the number of (example, parameter) interactions is limited by network bandwidth instead of computational capacity.", + "Depending on the scheme, loss terms may be necessary to achieve the desired level of sparsity per-chunk and/or per example. BIBREF13 use three such terms. These issues can affect both model quality and load-balancing.", + "Model capacity is most critical for very large data sets. The existing literature on conditional computation deals with relatively small image recognition data sets consisting of up to 600,000 images. It is hard to imagine that the labels of these images provide a sufficient signal to adequately train a model with millions, let alone billions of parameters.", + "In this work, we for the first time address all of the above challenges and finally realize the promise of conditional computation. We obtain greater than 1000x improvements in model capacity with only minor losses in computational efficiency and significantly advance the state-of-the-art results on public language modeling and translation data sets." + ], + [ + "Our approach to conditional computation is to introduce a new type of general purpose neural network component: a Sparsely-Gated Mixture-of-Experts Layer (MoE). The MoE consists of a number of experts, each a simple feed-forward neural network, and a trainable gating network which selects a sparse combination of the experts to process each input (see Figure FIGREF8 ). All parts of the network are trained jointly by back-propagation.", + "While the introduced technique is generic, in this paper we focus on language modeling and machine translation tasks, which are known to benefit from very large models. In particular, we apply a MoE convolutionally between stacked LSTM layers BIBREF15 , as in Figure FIGREF8 . The MoE is called once for each position in the text, selecting a potentially different combination of experts at each position. The different experts tend to become highly specialized based on syntax and semantics (see Appendix SECREF84 Table TABREF92 ). On both language modeling and machine translation benchmarks, we improve on best published results at a fraction of the computational cost." + ], + [ + "Since its introduction more than two decades ago BIBREF16 , BIBREF17 , the mixture-of-experts approach has been the subject of much research. Different types of expert architectures hae been proposed such as SVMs BIBREF18 , Gaussian Processes BIBREF19 , BIBREF20 , BIBREF21 , Dirichlet Processes BIBREF22 , and deep networks. Other work has focused on different expert configurations such as a hierarchical structure BIBREF23 , infinite numbers of experts BIBREF24 , and adding experts sequentially BIBREF25 . BIBREF26 suggest an ensemble model in the format of mixture of experts for machine translation. The gating network is trained on a pre-trained ensemble NMT model.", + "The works above concern top-level mixtures of experts. The mixture of experts is the whole model. BIBREF10 introduce the idea of using multiple MoEs with their own gating networks as parts of a deep model. It is intuitive that the latter approach is more powerful, since complex problems may contain many sub-problems each requiring different experts. They also allude in their conclusion to the potential to introduce sparsity, turning MoEs into a vehicle for computational computation.", + "Our work builds on this use of MoEs as a general purpose neural network component. While BIBREF10 uses two stacked MoEs allowing for two sets of gating decisions, our convolutional application of the MoE allows for different gating decisions at each position in the text. We also realize sparse gating and demonstrate its use as a practical way to massively increase model capacity." + ], + [ + "The Mixture-of-Experts (MoE) layer consists of a set of INLINEFORM0 \u201cexpert networks\" INLINEFORM1 , and a \u201cgating network\" INLINEFORM2 whose output is a sparse INLINEFORM3 -dimensional vector. Figure FIGREF8 shows an overview of the MoE module. The experts are themselves neural networks, each with their own parameters. Although in principle we only require that the experts accept the same sized inputs and produce the same-sized outputs, in our initial investigations in this paper, we restrict ourselves to the case where the models are feed-forward networks with identical architectures, but with separate parameters.", + "Let us denote by INLINEFORM0 and INLINEFORM1 the output of the gating network and the output of the INLINEFORM2 -th expert network for a given input INLINEFORM3 . The output INLINEFORM4 of the MoE module can be written as follows: DISPLAYFORM0 ", + "We save computation based on the sparsity of the output of INLINEFORM0 . Wherever INLINEFORM1 , we need not compute INLINEFORM2 . In our experiments, we have up to thousands of experts, but only need to evaluate a handful of them for every example. If the number of experts is very large, we can reduce the branching factor by using a two-level hierarchical MoE. In a hierarchical MoE, a primary gating network chooses a sparse weighted combination of \u201cexperts\", each of which is itself a secondary mixture-of-experts with its own gating network. In the following we focus on ordinary MoEs. We provide more details on hierarchical MoEs in Appendix SECREF60 .", + "Our implementation is related to other models of conditional computation. A MoE whose experts are simple weight matrices is similar to the parameterized weight matrix proposed in BIBREF12 . A MoE whose experts have one hidden layer is similar to the block-wise dropout described in BIBREF13 , where the dropped-out layer is sandwiched between fully-activated layers." + ], + [ + "A simple choice of non-sparse gating function BIBREF17 is to multiply the input by a trainable weight matrix INLINEFORM0 and then apply the INLINEFORM1 function. DISPLAYFORM0 ", + "We add two components to the Softmax gating network: sparsity and noise. Before taking the softmax function, we add tunable Gaussian noise, then keep only the top k values, setting the rest to INLINEFORM0 (which causes the corresponding gate values to equal 0). The sparsity serves to save computation, as described above. While this form of sparsity creates some theoretically scary discontinuities in the output of gating function, we have not yet observed this to be a problem in practice. The noise term helps with load balancing, as will be discussed in Appendix SECREF51 . The amount of noise per component is controlled by a second trainable weight matrix INLINEFORM1 . DISPLAYFORM0 DISPLAYFORM1 ", + "We train the gating network by simple back-propagation, along with the rest of the model. If we choose INLINEFORM0 , the gate values for the top k experts have nonzero derivatives with respect to the weights of the gating network. This type of occasionally-sensitive behavior is described in BIBREF9 with respect to noisy rectifiers. Gradients also back-propagate through the gating network to its inputs. Our method differs here from BIBREF13 who use boolean gates and a REINFORCE-style approach to train the gating network." + ], + [ + "On modern CPUs and GPUs, large batch sizes are necessary for computational efficiency, so as to amortize the overhead of parameter loads and updates. If the gating network chooses INLINEFORM0 out of INLINEFORM1 experts for each example, then for a batch of INLINEFORM2 examples, each expert receives a much smaller batch of approximately INLINEFORM3 examples. This causes a naive MoE implementation to become very inefficient as the number of experts increases. The solution to this shrinking batch problem is to make the original batch size as large as possible. However, batch size tends to be limited by the memory necessary to store activations between the forwards and backwards passes. We propose the following techniques for increasing the batch size:", + "In a conventional distributed training setting, multiple copies of the model on different devices asynchronously process distinct batches of data, and parameters are synchronized through a set of parameter servers. In our technique, these different batches run synchronously so that they can be combined for the MoE layer. We distribute the standard layers of the model and the gating network according to conventional data-parallel schemes, but keep only one shared copy of each expert. Each expert in the MoE layer receives a combined batch consisting of the relevant examples from all of the data-parallel input batches. The same set of devices function as data-parallel replicas (for the standard layers and the gating networks) and as model-parallel shards (each hosting a subset of the experts). If the model is distributed over INLINEFORM0 devices, and each device processes a batch of size INLINEFORM1 , each expert receives a batch of approximately INLINEFORM2 examples. Thus, we achieve a factor of INLINEFORM3 improvement in expert batch size.", + "In the case of a hierarchical MoE (Section SECREF60 ), the primary gating network employs data parallelism, and the secondary MoEs employ model parallelism. Each secondary MoE resides on one device.", + "This technique allows us to increase the number of experts (and hence the number of parameters) by proportionally increasing the number of devices in the training cluster. The total batch size increases, keeping the batch size per expert constant. The memory and bandwidth requirements per device also remain constant, as do the step times, as does the amount of time necessary to process a number of training examples equal to the number of parameters in the model. It is our goal to train a trillion-parameter model on a trillion-word corpus. We have not scaled our systems this far as of the writing of this paper, but it should be possible by adding more hardware.", + "In our language models, we apply the same MoE to each time step of the previous layer. If we wait for the previous layer to finish, we can apply the MoE to all the time steps together as one big batch. Doing so increases the size of the input batch to the MoE layer by a factor of the number of unrolled time steps.", + "We suspect that even more powerful models may involve applying a MoE recurrently. For example, the weight matrices of a LSTM or other RNN could be replaced by a MoE. Sadly, such models break the convolutional trick from the last paragraph, since the input to the MoE at one timestep depends on the output of the MoE at the previous timestep. BIBREF27 describe a technique for drastically reducing the number of stored activations in an unrolled RNN, at the cost of recomputing forward activations. This would allow for a large increase in batch size." + ], + [ + "Another major performance concern in distributed computing is network bandwidth. Since the experts are stationary (see above) and the number of gating parameters is small, most of the communication involves sending the inputs and outputs of the experts across the network. To maintain computational efficiency, the ratio of an expert's computation to the size of its input and output must exceed the ratio of computational to network capacity of the computing device. For GPUs, this may be thousands to one. In our experiments, we use experts with one hidden layer containing thousands of RELU-activated units. Since the weight matrices in the expert have sizes INLINEFORM0 _ INLINEFORM1 _ INLINEFORM2 and INLINEFORM3 _ INLINEFORM4 _ INLINEFORM5 , the ratio of computation to input and output is equal to the size of the hidden layer. Conveniently, we can increase computational efficiency simply by using a larger hidden layer, or more hidden layers." + ], + [ + "We have observed that the gating network tends to converge to a state where it always produces large weights for the same few experts. This imbalance is self-reinforcing, as the favored experts are trained more rapidly and thus are selected even more by the gating network. BIBREF10 describe the same phenomenon, and use a hard constraint at the beginning of training to avoid this local minimum. BIBREF13 include a soft constraint on the batch-wise average of each gate.", + "We take a soft constraint approach. We define the importance of an expert relative to a batch of training examples to be the batchwise sum of the gate values for that expert. We define an additional loss INLINEFORM0 , which is added to the overall loss function for the model. This loss is equal to the square of the coefficient of variation of the set of importance values, multiplied by a hand-tuned scaling factor INLINEFORM1 . This additional loss encourages all experts to have equal importance. DISPLAYFORM0 DISPLAYFORM1 ", + "While this loss function can ensure equal importance, experts may still receive very different numbers of examples. For example, one expert may receive a few examples with large weights, and another may receive many examples with small weights. This can cause memory and performance problems on distributed hardware. To solve this problem, we introduce a second loss function, INLINEFORM0 , which ensures balanced loads. Appendix SECREF51 contains the definition of this function, along with experimental results." + ], + [ + "This dataset, introduced by BIBREF28 consists of shuffled unique sentences from news articles, totaling approximately 829 million words, with a vocabulary of 793,471 words.", + "The best previously published results BIBREF2 use models consisting of one or more stacked Long Short-Term Memory (LSTM) layers BIBREF15 , BIBREF29 . The number of parameters in the LSTM layers of these models vary from 2 million to 151 million. Quality increases greatly with parameter count, as do computational costs. Results for these models form the top line of Figure FIGREF32 -right.", + "Our models consist of two stacked LSTM layers with a MoE layer between them (see Figure FIGREF8 ). We vary the sizes of the layers and the number of experts. For full details on model architecture, training regimen, additional baselines and results, see Appendix SECREF65 .", + "To investigate the effects of adding capacity, we trained a series of MoE models all with roughly equal computational costs: about 8 million multiply-and-adds per training example per timestep in the forwards pass, excluding the softmax layer. We call this metric (ops/timestep). We trained models with flat MoEs containing 4, 32, and 256 experts, and models with hierarchical MoEs containing 256, 1024, and 4096 experts. Each expert had about 1 million parameters. For all the MoE layers, 4 experts were active per input.", + "The results of these models are shown in Figure FIGREF32 -left. The model with 4 always-active experts performed (unsurprisingly) similarly to the computationally-matched baseline models, while the largest of the models (4096 experts) achieved an impressive 24% lower perplexity on the test set.", + "In addition to the largest model from the previous section, we trained two more MoE models with similarly high capacity (4 billion parameters), but higher computation budgets. These models had larger LSTMs, and fewer but larger and experts. Details can be found in Appendix UID77 . Results of these three models form the bottom line of Figure FIGREF32 -right. Table TABREF33 compares the results of these models to the best previously-published result on this dataset . Even the fastest of these models beats the best published result (when controlling for the number of training epochs), despite requiring only 6% of the computation.", + "We trained our models using TensorFlow BIBREF30 on clusters containing 16-32 Tesla K40 GPUs. For each of our models, we determine computational efficiency in TFLOPS/GPU by dividing the number of floating point operations required to process one training batch by the observed step time and the number of GPUs in the cluster. The operation counts used here are higher than the ones we report in our ops/timestep numbers in that we include the backwards pass, we include the importance-sampling-based training of the softmax layer, and we count a multiply-and-add as two separate operations. For all of our MoE models, the floating point operations involved in the experts represent between 37% and 46% of the total.", + "For our baseline models wtih no MoE, observed computational efficiency ranged from 1.07-1.29 TFLOPS/GPU. For our low-computation MoE models, computation efficiency ranged from 0.74-0.90 TFLOPS/GPU, except for the 4-expert model which did not make full use of the available parallelism. Our highest-computation MoE model was more efficient at 1.56 TFLOPS/GPU, likely due to the larger matrices. These numbers represent a significant fraction of the theoretical maximum of 4.29 TFLOPS/GPU claimed by NVIDIA. Detailed results are in Appendix SECREF65 , Table TABREF76 ." + ], + [ + "On the 1-billion-word corpus, adding additional capacity seems to produce diminishing returns as the number of parameters in the MoE layer exceeds 1 billion, as can be seen in Figure FIGREF32 -left. We hypothesized that for a larger training set, even higher capacities would produce significant quality improvements.", + "We constructed a similar training set consisting of shuffled unique sentences from Google's internal news corpus, totalling roughly 100 billion words. Similarly to the previous section, we tested a series of models with similar computational costs of about 8 million ops/timestep. In addition to a baseline LSTM model, we trained models augmented with MoE layers containing 32, 256, 1024, 4096, 16384, 65536, and 131072 experts. This corresponds to up to 137 billion parameters in the MoE layer. Details on architecture, training, and results are given in Appendix SECREF78 .", + "Figure FIGREF37 shows test perplexity as a function of capacity after training on 10 billion words (top line) and 100 billion words (bottom line). When training over the full 100 billion words, test perplexity improves significantly up to 65536 experts (68 billion parameters), dropping 39% lower than the computationally matched baseline, but degrades at 131072 experts, possibly a result of too much sparsity. The widening gap between the two lines demonstrates (unsurprisingly) that increased model capacity helps more on larger training sets.", + "Even at 65536 experts (99.994% layer sparsity), computational efficiency for the model stays at a respectable 0.72 TFLOPS/GPU." + ], + [ + "Our model was a modified version of the GNMT model described in BIBREF3 . To reduce computation, we decreased the number of LSTM layers in the encoder and decoder from 9 and 8 to 3 and 2 respectively. We inserted MoE layers in both the encoder (between layers 2 and 3) and the decoder (between layers 1 and 2). Each MoE layer contained up to 2048 experts each with about two million parameters, adding a total of about 8 billion parameters to the models. Further details on model architecture, testing procedure and results can be found in Appendix SECREF84 .", + "We benchmarked our method on the WMT'14 En INLINEFORM0 Fr and En INLINEFORM1 De corpora, whose training sets have 36M sentence pairs and 5M sentence pairs, respectively. The experimental protocols were also similar to those in BIBREF3 : newstest2014 was used as the test set to compare against previous work BIBREF31 , BIBREF32 , BIBREF3 , while the combination of newstest2012 and newstest2013 was used as the development set. We also tested the same model on a Google's Production English to French data.", + "Tables TABREF42 , TABREF43 , and TABREF44 show the results of our largest models, compared with published results. Our approach achieved BLEU scores of 40.56 and 26.03 on the WMT'14 En INLINEFORM0 Fr and En INLINEFORM1 De benchmarks. As our models did not use RL refinement, these results constitute significant gains of 1.34 and 1.12 BLEU score on top of the strong baselines in BIBREF3 . The perplexity scores are also better. On the Google Production dataset, our model achieved 1.01 higher test BLEU score even after training for only one sixth of the time." + ], + [ + " BIBREF35 train a single GNMT BIBREF3 model on a very large combined dataset of twelve language pairs. Results are somewhat worse than those for 12 separately trained single-pair GNMT models. This is not surprising, given that the twelve models have 12 times the capacity and twelve times the aggregate training of the one model. We repeat this experiment with a single MoE-augmented model. See Appendix SECREF84 for details on model architecture. We train our model on the same dataset as BIBREF35 and process the same number of training examples (about 3 billion sentence pairs). Our training time was shorter due to the lower computational budget of our model.", + "Results for the single-pair GNMT models, the multilingual GNMT model and the multilingual MoE model are given in Table TABREF50 . The MoE model achieves 19% lower perplexity on the dev set than the multilingual GNMT model. On BLEU score, the MoE model significantly beats the multilingual GNMT model on 11 of the 12 language pairs (by as much as 5.84 points), and even beats the monolingual GNMT models on 8 of 12 language pairs. The poor performance on English INLINEFORM0 Korean seems to be a result of severe overtraining, as for the rarer language pairs a small number of real examples were highly oversampled in the training corpus.", + "" + ], + [ + "This work is the first to demonstrate major wins from conditional computation in deep networks. We carefully identified the design considerations and challenges of conditional computing and addressed them with a combination of algorithmic and engineering solutions. While we focused on text, conditional computation may help in other domains as well, provided sufficiently large training sets. We look forward to seeing many novel implementations and applications of conditional computation in the years to come." + ], + [ + "tocsectionAppendices" + ], + [ + "As discussed in section SECREF4 , for load-balancing purposes, we want to define an additional loss function to encourage experts to receive roughly equal numbers of training examples. Unfortunately, the number of examples received by an expert is a discrete quantity, so it can not be used in back-propagation. Instead, we define a smooth estimator INLINEFORM0 of the number of examples assigned to each expert for a batch INLINEFORM1 of inputs. The smoothness allows us to back-propagate gradients through the estimator. This is the purpose of the noise term in the gating function. We define INLINEFORM2 as the probability that INLINEFORM3 is nonzero, given a new random choice of noise on element INLINEFORM4 , but keeping the already-sampled choices of noise on the other elements. To compute INLINEFORM5 , we note that the INLINEFORM6 is nonzero if and only if INLINEFORM7 is greater than the INLINEFORM8 -greatest element of INLINEFORM9 excluding itself. The probability works out to be: DISPLAYFORM0 ", + "Where INLINEFORM0 means the kth highest component of INLINEFORM1 , excluding component INLINEFORM2 . Simplifying, we get: DISPLAYFORM0 ", + "Where INLINEFORM0 is the CDF of the standard normal distribution. DISPLAYFORM0 ", + "We can now define the load loss to be the square of the coefficient of variation of the load vector, multiplied by a hand-tuned scaling factor INLINEFORM0 . DISPLAYFORM0 ", + "To avoid out-of-memory errors, we need to initialize the network in a state of approximately equal expert load (since the soft constraints need some time to work). To accomplish this, we initialize the matrices INLINEFORM0 and INLINEFORM1 to all zeros, which yields no signal and some noise.", + "We trained a set of models with identical architecture (the MoE-256 model described in Appendix SECREF65 ), using different values of INLINEFORM0 and INLINEFORM1 . We trained each model for 10 epochs, then measured perplexity on the test set. We also measured the coefficients of variation in INLINEFORM2 and INLINEFORM3 , as well as ratio of the load on the most overloaded expert to the average load. This last value is significant for load balancing purposes on distributed hardware. All of these metrics were averaged over several training batches.", + "Results are reported in Table TABREF58 . All the combinations containing at least one the two losses led to very similar model quality, where having no loss was much worse. Models with higher values of INLINEFORM0 had lower loads on the most overloaded expert." + ], + [ + "If the number of experts is very large, we can reduce the branching factor by using a two-level hierarchical MoE. In a hierarchical MoE, a primary gating network chooses a sparse weighted combination of \u201cexperts\", each of which is itself a secondary mixture-of-experts with its own gating network. If the hierarchical MoE consists of INLINEFORM0 groups of INLINEFORM1 experts each, we denote the primary gating network by INLINEFORM2 , the secondary gating networks by INLINEFORM3 , and the expert networks by INLINEFORM4 . The output of the MoE is given by: DISPLAYFORM0 ", + "Our metrics of expert utilization change to the following: DISPLAYFORM0 DISPLAYFORM1 ", + " INLINEFORM0 and INLINEFORM1 deonte the INLINEFORM2 functions for the primary gating network and INLINEFORM3 secondary gating network respectively. INLINEFORM4 denotes the subset of INLINEFORM5 for which INLINEFORM6 .", + "It would seem simpler to let INLINEFORM0 , but this would not have a gradient with respect to the primary gating network, so we use the formulation above." + ], + [ + "Our model consists of five layers: a word embedding layer, a recurrent Long Short-Term Memory (LSTM) layer BIBREF15 , BIBREF29 , a MoE layer, a second LSTM layer, and a softmax layer. The dimensionality of the embedding layer, the number of units in each LSTM layer, and the input and output dimensionality of the MoE layer are all equal to 512. For every layer other than the softmax, we apply drouput BIBREF43 to the layer output, dropping each activation with probability INLINEFORM0 , otherwise dividing by INLINEFORM1 . After dropout, the output of the previous layer is added to the layer output. This residual connection encourages gradient flow BIBREF37 .", + "Each expert in the MoE layer is a feed forward network with one ReLU-activated hidden layer of size 1024 and an output layer of size 512. Thus, each expert contains INLINEFORM0 parameters. The output of the MoE layer is passed through a sigmoid function before dropout. We varied the number of experts between models, using ordinary MoE layers with 4, 32 and 256 experts and hierarchical MoE layers with 256, 1024 and 4096 experts. We call the resulting models MoE-4, MoE-32, MoE-256, MoE-256-h, MoE-1024-h and MoE-4096-h. For the hierarchical MoE layers, the first level branching factor was 16, corresponding to the number of GPUs in our cluster. We use Noisy-Top-K Gating (see Section UID14 ) with INLINEFORM1 for the ordinary MoE layers and INLINEFORM2 at each level of the hierarchical MoE layers. Thus, each example is processed by exactly 4 experts for a total of 4M ops/timestep. The two LSTM layers contribute 2M ops/timestep each for the desired total of 8M.", + "The MoE-4 model does not employ sparsity, since all 4 experts are always used. In addition, we trained four more computationally-matched baseline models with no sparsity:", + "MoE-1-Wide: The MoE layer consists of a single \"expert\" containing one ReLU-activated hidden layer of size 4096.", + "MoE-1-Deep: The MoE layer consists of a single \"expert\" containing four ReLU-activated hidden layers, each with size 1024.", + "4xLSTM-512: We replace the MoE layer with two additional 512-unit LSTM layers.", + "LSTM-2048-512: The model contains one 2048-unit LSTM layer (and no MoE). The output of the LSTM is projected down to 512 dimensions BIBREF41 . The next timestep of the LSTM receives the projected output. This is identical to one of the models published in BIBREF2 . We re-ran it to account for differences in training regimen, and obtained results very similar to the published ones.", + "The models were trained on a cluster of 16 K40 GPUs using the synchronous method described in Section SECREF3 . Each batch consisted of a set of sentences totaling roughly 300,000 words. In the interest of time, we limited training to 10 epochs, (27,000 steps). Training took 12-16 hours for all models, except for MoE-4, which took 18 hours (since all the expert computation was performed on only 4 of 16 GPUs). We used the Adam optimizer BIBREF39 . The base learning rate was increased linearly for the first 1000 training steps, and decreased after that so as to be proportional to the inverse square root of the step number. The Softmax output layer was trained efficiently using importance sampling similarly to the models in BIBREF2 . For each model, we performed a hyper-parmeter search to find the best dropout probability, in increments of 0.1.", + "To ensure balanced expert utilization we set INLINEFORM0 and INLINEFORM1 , as described in Section SECREF4 and Appendix SECREF51 .", + "We evaluate our model using perplexity on the holdout dataset, used by BIBREF28 , BIBREF2 . We follow the standard procedure and sum over all the words including the end of sentence symbol. Results are reported in Table TABREF76 . For each model, we report the test perplexity, the computational budget, the parameter counts, the value of INLINEFORM0 , and the computational efficiency.", + "We ran two additional models (MoE-34M and MoE-143M) to investigate the effects of adding more computation in the presence of a large MoE layer. These models have computation budgets of 34M and 143M ops/timestep. Similar to the models above, these models use a MoE layer between two LSTM layers. The dimensionality of the embedding layer, and the input and output dimensionality of the MoE layer are set to 1024 instead of 512. For MoE-34M, the LSTM layers have 1024 units. For MoE-143M, the LSTM layers have 4096 units and an output projection of size 1024 BIBREF41 . MoE-34M uses a hierarchical MoE layer with 1024 experts, each with a hidden layer of size 2048. MoE-143M uses a hierarchical MoE layer with 256 experts, each with a hidden layer of size 8192. Both models have 4B parameters in the MoE layers. We searched for the best INLINEFORM0 for each model, and trained each model for 10 epochs.", + "The two models achieved test perplexity of INLINEFORM0 and INLINEFORM1 respectively, showing that even in the presence of a large MoE, more computation is still useful. Results are reported at the bottom of Table TABREF76 . The larger of the two models has a similar computational budget to the best published model from the literature, and training times are similar. Comparing after 10 epochs, our model has a lower test perplexity by INLINEFORM2 ." + ], + [ + "The models are similar in structure to the 8-million-operations-per-timestep models described in the previous section. We vary the number of experts between models, using an ordinary MoE layer with 32 experts and hierarchical MoE layers with 256, 1024, 4096, 16384, 65536 and 131072 experts. For the hierarchical MoE layers, the first level branching factors are 32, 32, 64, 128, 256 and 256, respectively.", + "Models are trained on a cluster of 32 Tesla K40 GPUs, except for the last two models, which are trained on clusters of 64 and 128 GPUs so as to have enough memory for all the parameters. For all models, training batch sizes are approximately 2.5 million words. Models are trained once-through over about 100 billion words.", + "We implement several memory optimizations in order to fit up to 1 billion parameters per GPU. First, we do not store the activations of the hidden layers of the experts, but instead recompute them on the backwards pass. Secondly, we modify the optimizer on the expert parameters to require less auxiliary storage:", + "The Adam optimizer BIBREF39 keeps first and second moment estimates of the per-parameter gradients. This triples the required memory. To avoid keeping a first-moment estimator, we set INLINEFORM0 . To reduce the size of the second moment estimator, we replace it with a factored approximation. For a matrix of parameters, instead of maintaining a full matrix of second-moment estimators, we maintain vectors of row-wise and column-wise averages of that matrix. At each step, the matrix of estimators is taken to be the outer product of those two vectors divided by the mean of either one. This technique could similarly be applied to Adagrad BIBREF36 .", + "We evaluate our model using perplexity on a holdout dataset. Results are reported in Table TABREF81 . Perplexity after 100 billion training words is 39% lower for the 68-billion-parameter MoE model than for the baseline model. It is notable that the measured computational efficiency of the largest model (0.30 TFLOPS/GPU) is very low compared to the other models. This is likely a result of the fact that, for purposes of comparison to the other models, we did not increase the training batch size proportionally to the number of GPUs. For comparison, we include results for a computationally matched baseline model consisting of 4 LSTMs, and for an unpruned 5-gram model with Kneser-Ney smoothing BIBREF40 ." + ], + [ + "Our model is a modified version of the GNMT model described in BIBREF3 . To reduce computation, we decrease the number of LSTM layers in the encoder and decoder from 9 and 8 to 3 and 2 respectively. We insert MoE layers in both the encoder (between layers 2 and 3) and the decoder (between layers 1 and 2). We use an attention mechanism between the encoder and decoder, with the first decoder LSTM receiving output from and providing input for the attention . All of the layers in our model have input and output dimensionality of 512. Our LSTM layers have 2048 hidden units, with a 512-dimensional output projection. We add residual connections around all LSTM and MoE layers to encourage gradient flow BIBREF37 . Similar to GNMT, to effectively deal with rare words, we used sub-word units (also known as \u201cwordpieces\") BIBREF42 for inputs and outputs in our system.", + "We use a shared source and target vocabulary of 32K wordpieces. We also used the same beam search technique as proposed in BIBREF3 .", + "We train models with different numbers of experts in the MoE layers. In addition to a baseline model with no MoE layers, we train models with flat MoE layers containing 32 experts, and models with hierarchical MoE layers containing 512 and 2048 experts. The flat MoE layers use INLINEFORM0 and the hierarchical MoE models use INLINEFORM1 at each level of the gating network. Thus, each input is processed by exactly 4 experts in each MoE layer. Each expert in the MoE layer is a feed forward network with one hidden layer of size 2048 and ReLU activation. Thus, each expert contains INLINEFORM2 parameters. The output of the MoE layer is passed through a sigmoid function. We use the strictly-balanced gating function described in Appendix SECREF93 .", + "We used the same model architecture as for the single-language-pair models, with the following exceptions: We used noisy-top-k gating as described in Section UID14 , not the scheme from Appendix SECREF93 . The MoE layers in the encoder and decoder are non-hierarchical MoEs with INLINEFORM0 experts, and INLINEFORM1 . Each expert has a larger hidden layer of size 8192. This doubles the amount of computation in the MoE layers, raising the computational budget of the entire model from 85M to 102M ops/timestep.", + "We trained our networks using the Adam optimizer BIBREF39 . The base learning rate was increased linearly for the first 2000 training steps, held constant for an additional 8000 steps, and decreased after that so as to be proportional to the inverse square root of the step number. For the single-language-pair models, similarly to BIBREF3 , we applied dropout BIBREF43 to the output of all embedding, LSTM and MoE layers, using INLINEFORM0 . Training was done synchronously on a cluster of up to 64 GPUs as described in section SECREF3 . Each training batch consisted of a set of sentence pairs containing roughly 16000 words per GPU.", + "To ensure balanced expert utilization we set INLINEFORM0 and INLINEFORM1 , as described in Section SECREF4 and Appendix SECREF51 .", + "We evaluated our models using the perplexity and the standard BLEU score metric. We reported tokenized BLEU score as computed by the multi-bleu.pl script, downloaded from the public implementation of Moses (on Github), which was also used in BIBREF31 .", + "Tables TABREF42 , TABREF43 and TABREF44 in Section SECREF39 show comparisons of our results to other published methods. Figure FIGREF91 shows test perplexity as a function of number of words in the (training data's) source sentences processed for models with different numbers of experts. As can be seen from the Figure, as we increased the number of experts to approach 2048, the test perplexity of our model continued to improve.", + "We found that the experts indeed become highly specialized by syntax and/or semantics, as can be seen in Table TABREF92 . For example, one expert is used when the indefinite article \u201ca\" introduces the direct object in a verb phrase indicating importance or leadership." + ], + [ + "Due to some peculiarities in our infrastructure which have since been fixed, at the time we ran some of the machine translation experiments, our models ran faster if every expert received exactly the same batch size. To accommodate this, we used a different gating function which we describe below.", + "Recall that we define the softmax gating function to be: DISPLAYFORM0 ", + "To obtain a sparse gating vector, we multiply INLINEFORM0 component-wise with a sparse mask INLINEFORM1 and normalize the output. The mask itself is a function of INLINEFORM2 and specifies which experts are assigned to each input example: DISPLAYFORM0 ", + "To implement top-k gating in this formulation, we would let INLINEFORM0 , where: DISPLAYFORM0 ", + "To force each expert to receive the exact same number of examples, we introduce an alternative mask function, INLINEFORM0 , which operates over batches of input vectors. Instead of keeping the top INLINEFORM1 values per example, we keep the top INLINEFORM2 values per expert across the training batch, where INLINEFORM3 , so that each example is sent to an average of INLINEFORM4 experts. DISPLAYFORM0 ", + "As our experiments suggest and also observed in BIBREF38 , using a batchwise function during training (such as INLINEFORM0 ) requires modifications to the inference when we may not have a large batch of examples. Our solution to this is to train a vector INLINEFORM1 of per-expert threshold values to approximate the effects of the batchwise mask. We use the following mask at inference time: DISPLAYFORM0 ", + "To learn the threshold values, we apply an additional loss at training time which is minimized when the batchwise mask and the threshold mask are identical. DISPLAYFORM0 " + ], + [ + "The attention mechanism described in GNMT BIBREF3 involves a learned \u201cAttention Function\" INLINEFORM0 which takes a \u201csource vector\" INLINEFORM1 and a \u201ctarget vector\" INLINEFORM2 , and must be computed for every source time step INLINEFORM3 and target time step INLINEFORM4 . In GNMT, the attention function is implemented as a feed forward neural network with a hidden layer of size INLINEFORM5 . It can be expressed as: DISPLAYFORM0 ", + "Where INLINEFORM0 and INLINEFORM1 are trainable weight matrices and INLINEFORM2 is a trainable weight vector.", + "For performance reasons, in our models, we used a slightly different attention function: DISPLAYFORM0 ", + "With our attention function, we can simultaneously compute the attention function on multiple source time steps and multiple target time steps using optimized matrix multiplications. We found little difference in quality between the two functions." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-1104/instruction.md b/qasper-1104/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..8404212bb2baccac340f5cf8b7775e871791d8f2 --- /dev/null +++ b/qasper-1104/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer + +Question: What improvement does the MOE model make over the SOTA on language modelling? \ No newline at end of file diff --git a/qasper-1132/instruction.md b/qasper-1132/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..57af26908e25dc08b6a81c54ef1ee132b25bd0a5 --- /dev/null +++ b/qasper-1132/instruction.md @@ -0,0 +1,65 @@ +Name of Paper: STransE: a novel embedding model of entities and relationships in knowledge bases + +Question: What scoring function does the model use to score triples? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Our approach", + "Related work", + "Experiments", + "Task and evaluation protocol", + "Main results", + "Conclusion and future work", + "Acknowledgments" + ], + "paragraphs": [ + [ + "Knowledge bases (KBs), such as WordNet BIBREF0 , YAGO BIBREF1 , Freebase BIBREF2 and DBpedia BIBREF3 , represent relationships between entities as triples $(\\mathrm {head\\ entity, relation, tail\\ entity})$ . Even very large knowledge bases are still far from complete BIBREF4 , BIBREF5 . Link prediction or knowledge base completion systems BIBREF6 predict which triples not in a knowledge base are likely to be true BIBREF7 , BIBREF8 . A variety of different kinds of information is potentially useful here, including information extracted from external corpora BIBREF9 , BIBREF10 and the other relationships that hold between the entities BIBREF11 , BIBREF12 . For example, toutanova-EtAl:2015:EMNLP used information from the external ClueWeb-12 corpus to significantly enhance performance.", + "While integrating a wide variety of information sources can produce excellent results BIBREF13 , there are several reasons for studying simpler models that directly optimize a score function for the triples in a knowledge base, such as the one presented here. First, additional information sources might not be available, e.g., for knowledge bases for specialized domains. Second, models that don't exploit external resources are simpler and thus typically much faster to train than the more complex models using additional information. Third, the more complex models that exploit external information are typically extensions of these simpler models, and are often initialized with parameters estimated by such simpler models, so improvements to the simpler models should yield corresponding improvements to the more complex models as well.", + "Embedding models for KB completion associate entities and/or relations with dense feature vectors or matrices. Such models obtain state-of-the-art performance BIBREF14 , BIBREF8 , BIBREF15 , BIBREF16 , BIBREF4 , BIBREF17 , BIBREF18 and generalize to large KBs BIBREF19 . Table 1 summarizes a number of prominent embedding models for KB completion.", + "Let $(h, r, t)$ represent a triple. In all of the models discussed here, the head entity $h$ and the tail entity $t$ are represented by vectors $\\textbf {h}$ and $\\textbf {t}\\in \\mathbb {R}^{k}$ respectively. The Unstructured model BIBREF15 assumes that $\\textbf {h} \\approx \\textbf {t}$ . As the Unstructured model does not take the relationship $r$ into account, it cannot distinguish different relation types. The Structured Embedding (SE) model BIBREF8 extends the unstructured model by assuming that $h$ and $t$ are similar only in a relation-dependent subspace. It represents each relation $r$ with two matrices $h$0 and $h$1 , which are chosen so that $h$2 . The TransE model BIBREF16 is inspired by models such as Word2Vec BIBREF20 where relationships between words often correspond to translations in latent feature space. The TransE model represents each relation $h$3 by a translation vector r $h$4 , which is chosen so that $h$5 .", + "The primary contribution of this paper is that two very simple relation-prediction models, SE and TransE, can be combined into a single model, which we call STransE. Specifically, we use relation-specific matrices $\\textbf {W}_{r,1}$ and $\\textbf {W}_{r,2}$ as in the SE model to identify the relation-dependent aspects of both $h$ and $t$ , and use a vector $\\textbf {r}$ as in the TransE model to describe the relationship between $h$ and $t$ in this subspace. Specifically, our new KB completion model STransE chooses $\\textbf {W}_{r,1}$ , $\\textbf {W}_{r,2}$ and $\\textbf {r}$ so that $\\textbf {W}_{r,2}$0 . That is, a TransE-style relationship holds in some relation-dependent subspace, and crucially, this subspace may involve very different projections of the head $\\textbf {W}_{r,2}$1 and tail $\\textbf {W}_{r,2}$2 . So $\\textbf {W}_{r,2}$3 and $\\textbf {W}_{r,2}$4 can highlight, suppress, or even change the sign of, relation-specific attributes of $\\textbf {W}_{r,2}$5 and $\\textbf {W}_{r,2}$6 . For example, for the \u201cpurchases\u201d relationship, certain attributes of individuals $\\textbf {W}_{r,2}$7 (e.g., age, gender, marital status) are presumably strongly correlated with very different attributes of objects $\\textbf {W}_{r,2}$8 (e.g., sports car, washing machine and the like).", + "As we show below, STransE performs better than the SE and TransE models and other state-of-the-art link prediction models on two standard link prediction datasets WN18 and FB15k, so it can serve as a new baseline for KB completion. We expect that the STransE will also be able to serve as the basis for extended models that exploit a wider variety of information sources, just as TransE does." + ], + [ + "Let $\\mathcal {E}$ denote the set of entities and $\\mathcal {R}$ the set of relation types. For each triple $(h, r, t)$ , where $h, t \\in \\mathcal {E}$ and $r \\in \\mathcal {R}$ , the STransE model defines a score function $f_r(h, t)$ of its implausibility. Our goal is to choose $f$ such that the score $f_r(h,t)$ of a plausible triple $(h,r,t)$ is smaller than the score $f_{r^{\\prime }}(h^{\\prime },t^{\\prime })$ of an implausible triple $\\mathcal {R}$0 . We define the STransE score function $\\mathcal {R}$1 as follows:", + " $\nf_r(h, t) & = & \\Vert \\textbf {W}_{r,1}\\textbf {h} + \\textbf {r} - \\textbf {W}_{r,2}\\textbf {t}\\Vert _{\\ell _{1/2}}\n$ ", + "using either the $\\ell _1$ or the $\\ell _2$ -norm (the choice is made using validation data; in our experiments we found that the $\\ell _1$ norm gave slightly better results). To learn the vectors and matrices we minimize the following margin-based objective function: $\n\\mathcal {L} & = & \\sum _{\\begin{array}{c}(h,r,t) \\in \\mathcal {G} \\\\ (h^{\\prime },r,t^{\\prime }) \\in \\mathcal {G}^{\\prime }_{(h, r, t)}\\end{array}} [\\gamma + f_r(h, t) - f_r(h^{\\prime }, t^{\\prime })]_+\n$ ", + "where $[x]_+ = \\max (0, x)$ , $\\gamma $ is the margin hyper-parameter, $\\mathcal {G}$ is the training set consisting of correct triples, and $\\mathcal {G}^{\\prime }_{(h, r, t)} = \\lbrace (h^{\\prime }, r, t) \\mid h^{\\prime } \\in \\mathcal {E}, (h^{\\prime }, r, t) \\notin \\mathcal {G} \\rbrace \\cup \\lbrace (h, r,\nt^{\\prime }) \\mid t^{\\prime } \\in \\mathcal {E}, (h, r, t^{\\prime }) \\notin \\mathcal {G} \\rbrace $ is the set of incorrect triples generated by corrupting a correct triple $(h, r, t)\\in \\mathcal {G}$ .", + "We use Stochastic Gradient Descent (SGD) to minimize $\\mathcal {L}$ , and impose the following constraints during training: $\\Vert \\textbf {h}\\Vert _2 \\leqslant 1$ , $\\Vert \\textbf {r}\\Vert _2 \\leqslant 1$ , $\\Vert \\textbf {t}\\Vert _2 \\leqslant 1$ , $\\Vert \\textbf {W}_{r,1}\\textbf {h}\\Vert _2\n\\leqslant 1$ and $\\Vert \\textbf {W}_{r,2}\\textbf {t}\\Vert _2 \\leqslant 1$ ." + ], + [ + "Table 1 summarizes related embedding models for link prediction and KB completion. The models differ in the score functions $f_r(h, t)$ and the algorithms used to optimize the margin-based objective function, e.g., SGD, AdaGrad BIBREF21 , AdaDelta BIBREF22 and L-BFGS BIBREF23 .", + "DISTMULT BIBREF24 is based on a Bilinear model BIBREF14 , BIBREF15 , BIBREF25 where each relation is represented by a diagonal rather than a full matrix. The neural tensor network (NTN) model BIBREF4 uses a bilinear tensor operator to represent each relation while ProjE BIBREF26 could be viewed as a simplified version of NTN with diagonal matrices. Similar quadratic forms are used to model entities and relations in KG2E BIBREF27 , ComplEx BIBREF28 , TATEC BIBREF29 and RSTE BIBREF30 . In addition, HolE BIBREF31 uses circular correlation\u2014a compositional operator\u2014which could be interpreted as a compression of the tensor product.", + "The TransH model BIBREF17 associates each relation with a relation-specific hyperplane and uses a projection vector to project entity vectors onto that hyperplane. TransD BIBREF32 and TransR/CTransR BIBREF33 extend the TransH model using two projection vectors and a matrix to project entity vectors into a relation-specific space, respectively. TransD learns a relation-role specific mapping just as STransE, but represents this mapping by projection vectors rather than full matrices, as in STransE. The lppTransD model BIBREF34 extends TransD to additionally use two projection vectors for representing each relation. In fact, our STransE model and TranSparse BIBREF35 can be viewed as direct extensions of the TransR model, where head and tail entities are associated with their own projection matrices, rather than using the same matrix for both, as in TransR and CTransR.", + "Recently, several authors have shown that relation paths between entities in KBs provide richer information and improve the relationship prediction BIBREF36 , BIBREF37 , BIBREF18 , BIBREF38 , BIBREF39 , BIBREF40 , BIBREF41 , BIBREF42 , BIBREF43 , BIBREF44 . In addition, NickelMTG15 reviews other approaches for learning from KBs and multi-relational data." + ], + [ + "For link prediction evaluation, we conduct experiments and compare the performance of our STransE model with published results on the benchmark WN18 and FB15k datasets BIBREF16 . Information about these datasets is given in Table 2 ." + ], + [ + "The link prediction task BIBREF8 , BIBREF15 , BIBREF16 predicts the head or tail entity given the relation type and the other entity, i.e. predicting $h$ given $(?, r, t)$ or predicting $t$ given $(h, r, ?)$ where $?$ denotes the missing element. The results are evaluated using the ranking induced by the score function $f_r(h,t)$ on test triples.", + "For each test triple $(h, r, t)$ , we corrupted it by replacing either $h$ or $t$ by each of the possible entities in turn, and then rank these candidates in ascending order of their implausibility value computed by the score function. This is called as the \u201cRaw\u201d setting protocol. For the \u201cFiltered\u201d setting protocol described in BIBREF16 , we removed any corrupted triples that appear in the knowledge base, to avoid cases where a correct corrupted triple might be ranked higher than the test triple. The \u201cFiltered\u201d setting thus provides a clearer view on the ranking performance. Following BIBREF16 , we report the mean rank and the Hits@10 (i.e., the proportion of test triples in which the target entity was ranked in the top 10 predictions) for each model. In addition, we report the mean reciprocal rank, which is commonly used in information retrieval. In both \u201cRaw\u201d and \u201cFiltered\u201d settings, lower mean rank, higher mean reciprocal rank or higher Hits@10 indicates better link prediction performance.", + "Following TransR BIBREF33 , TransD BIBREF32 , rTransE BIBREF37 , PTransE BIBREF36 , TATEC BIBREF29 and TranSparse BIBREF35 , we used the entity and relation vectors produced by TransE BIBREF16 to initialize the entity and relation vectors in STransE, and we initialized the relation matrices with identity matrices. We applied the \u201cBernoulli\u201d trick used also in previous work for generating head or tail entities when sampling incorrect triples BIBREF17 , BIBREF33 , BIBREF27 , BIBREF32 , BIBREF36 , BIBREF34 , BIBREF35 . We ran SGD for 2,000 epochs to estimate the model parameters. Following NIPS20135071 we used a grid search on validation set to choose either the $l_1$ or $l_2$ norm in the score function $f$ , as well as to set the SGD learning rate $\\lambda \\in \\lbrace 0.0001, 0.0005, 0.001, 0.005, 0.01 \\rbrace $ , the margin hyper-parameter $\\gamma \\in \\lbrace 1, 3, 5 \\rbrace $ and the vector size $k\\in \\lbrace 50, 100 \\rbrace $ . The lowest filtered mean rank on the validation set was obtained when using the $l_1$ norm in $f$ on both WN18 and FB15k, and when $\\lambda = 0.0005, \\gamma = 5,\n\\text{ and } k = 50$ for WN18, and $\\lambda = 0.0001, \\gamma = 1,\n\\text{ and } k = 100$ for FB15k." + ], + [ + "Table 3 compares the link prediction results of our STransE model with results reported in prior work, using the same experimental setup. The first 15 rows report the performance of the models that do not exploit information about alternative paths between head and tail entities. The next 5 rows report results of the models that exploit information about relation paths. The last 3 rows present results for the models which make use of textual mentions derived from a large external corpus.", + "It is clear that the models with the additional external corpus information obtained best results. In future work we plan to extend the STransE model to incorporate such additional information. Table 3 also shows that the models employing path information generally achieve better results than models that do not use such information. In terms of models not exploiting path information or external information, the STransE model produces the highest filtered mean rank on WN18 and the highest filtered Hits@10 and mean reciprocal rank on FB15k. Compared to the closely related models SE, TransE, TransR, CTransR, TransD and TranSparse, our STransE model does better than these models on both WN18 and FB15k.", + "Following NIPS20135071, Table 4 analyzes Hits@10 results on FB15k with respect to the relation categories defined as follows: for each relation type $r$ , we computed the averaged number $a_h$ of heads $h$ for a pair $(r, t)$ and the averaged number $a_t$ of tails $t$ for a pair $(h, r)$ . If $a_h < 1.5$ and $a_t\n< 1.5$ , then $r$ is labeled 1-1. If $a_h$0 and $a_h$1 , then $a_h$2 is labeled M-1. If $a_h$3 and $a_h$4 , then $a_h$5 is labeled as 1-M. If $a_h$6 and $a_h$7 , then $a_h$8 is labeled as M-M. 1.4%, 8.9%, 14.6% and 75.1% of the test triples belong to a relation type classified as 1-1, 1-M, M-1 and M-M, respectively.", + "Table 4 shows that in comparison to prior models not using path information, STransE obtains the second highest Hits@10 result for M-M relation category at $(80.1\\% + 83.1\\%) / 2 = 81.6\\%$ which is 0.5% smaller than the Hits@10 result of TranSparse for M-M. However, STransE obtains 2.5% higher Hits@10 result than TranSparse for M-1. In addition, STransE also performs better than TransD for 1-M and M-1 relation categories. We believe the improved performance of the STransE model is due to its use of full matrices, rather than just projection vectors as in TransD. This permits STransE to model diverse and complex relation categories (such as 1-M, M-1 and especially M-M) better than TransD and other similiar models. However, STransE is not as good as TransD for the 1-1 relations. Perhaps the extra parameters in STransE hurt performance in this case (note that 1-1 relations are relatively rare, so STransE does better overall)." + ], + [ + "This paper presented a new embedding model for link prediction and KB completion. Our STransE combines insights from several simpler embedding models, specifically the Structured Embedding model BIBREF8 and the TransE model BIBREF16 , by using a low-dimensional vector and two projection matrices to represent each relation. STransE, while being conceptually simple, produces highly competitive results on standard link prediction evaluations, and scores better than the embedding-based models it builds on. Thus it is a suitable candidate for serving as future baseline for more complex models in the link prediction task.", + "In future work we plan to extend STransE to exploit relation path information in knowledge bases, in a manner similar to lin-EtAl:2015:EMNLP1, guu-miller-liang:2015:EMNLP or NguyenCoNLL2016." + ], + [ + "This research was supported by a Google award through the Natural Language Understanding Focused Program, and under the Australian Research Council's Discovery Projects funding scheme (project number DP160102156).", + "NICTA is funded by the Australian Government through the Department of Communications and the Australian Research Council through the ICT Centre of Excellence Program. The first author is supported by an International Postgraduate Research Scholarship and a NICTA NRPA Top-Up Scholarship." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-1135/instruction.md b/qasper-1135/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..1f4a3868aed6fd629831c2ef25c87f481d572f26 --- /dev/null +++ b/qasper-1135/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Doc2Vec on the PubMed corpus: study of a new approach to generate related articles + +Question: How better are results for pmra algorithm than Doc2Vec in human evaluation? \ No newline at end of file diff --git a/qasper-1150/instruction.md b/qasper-1150/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..84e8a915047517f6c007658315d355af7c1562f6 --- /dev/null +++ b/qasper-1150/instruction.md @@ -0,0 +1,170 @@ +Name of Paper: Logician: A Unified End-to-End Neural Approach for Open-Domain Information Extraction + +Question: How is Logician different from traditional seq2seq models? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "SAOKE Format: Symbol Aided Open Knowledge Expression", + "Completeness", + "Accurateness", + "Atomicity", + "Compactness", + "SAOKE Data Set", + "Logician", + "Attention based Sequence-to-sequence Learning ", + "Restricted Copy Mechanism", + "Coverage Mechanism", + "Gated Dependency Attention", + "Post processing", + "Experimental Design ", + "Evaluating Components' Utilities", + "Comparison with Existing Approaches", + "Results Analysis", + "Extraction Error Analysis of Logician", + "Knowledge Expressions", + "Relation Extraction", + "Language to Logic ", + "Facts to Language", + "Duality between Knowledge and Language", + "Conclusion" + ], + "paragraphs": [ + [ + "Semantic applications typically work on the basis of intermediate structures derived from sentences. Traditional word-level intermediate structures, such as POS-tags, dependency trees and semantic role labels, have been widely applied. Recently, entity and relation level intermediate structures attract increasingly more attentions.", + "In general, knowledge based applications require entity and relation level information. For instance, in BIBREF0 , the lexicalized dependency path between two entity mentions was taken as the surface pattern facts. In distant supervision BIBREF1 , the word sequence and dependency path between two entity mentions were taken as evidence of certain relation. In Probase BIBREF2 , candidates of taxonomies were extracted by Hearst patterns BIBREF3 . The surface patterns of relations extracted by Open Information Extraction (OIE) systems BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 worked as the source of question answering systems BIBREF9 , BIBREF10 . In addition, entity and relation level intermediate structures have been proven effective in many other tasks such as text summarization BIBREF11 , BIBREF12 , BIBREF13 , text comprehension, word similarity, word analogy BIBREF14 , and more.", + "The task of entity/relation level mediate structure extraction studies how facts about entities and relations are expressed by natural language in sentences, and then expresses these facts in an intermediate (and convenient) format. Although entity/relation level intermediate structures have been utilized in many applications, the study of learning these structures is still in an early stage.", + "Firstly, the problem of extracting different types of entity/relation level intermediate structures has not been considered in a unified fashion. Applications generally need to construct their own handcrafted heuristics to extract required entity/relation level intermediate structures, rather than consulting a commonly available NLP component, as they do for word level intermediate structures. Open IE-v4 system (http://knowitall.github.io/openie/) attempted to build such components by developing two sub-systems, with each extracting one type of intermediate structures, i.e., SRLIE BIBREF15 for verb based relations, and ReNoun BIBREF16 , BIBREF17 for nominal attributes. However, important information about descriptive tags for entities and concept-instance relations between entities were not considered.", + "Secondly, existing solutions to the task either used pattern matching technique BIBREF2 , BIBREF4 , BIBREF6 , BIBREF7 , or were trained in a self-supervised manner on the data set automatically generated by heuristic patterns or info-box matching BIBREF7 , BIBREF4 , BIBREF8 . It is well-understood that pattern matching typically does not generalize well and the automatically generated samples may contain lots of noises.", + "This paper aims at tackling some of the well-known challenging problems in OIE systems, in a supervised end-to-end deep learning paradigm. Our contribution can be summarized as three major components: SAOKE format, SAOKE data set, and Logician.", + "Symbol Aided Open Knowledge Expression (SAOKE) is a knowledge expression form with several desirable properties: (i) SAOKE is literally honest and open-domain. Following the philosophy of OIE systems, SAOKE uses words in the original sentence to express knowledge. (ii) SAOKE provides a unified view over four common types of knowledge: relation, attribute, description and concept. (iii) SAOKE is an accurate expression. With the aid of symbolic system, SAOKE is able to accurately express facts with separated relation phrases, missing information, hidden information, etc.", + "SAOKE Data Set is a human annotated data set containing 48,248 Chinese sentences and corresponding facts in the SAOKE form. We publish the data set for research purpose. To the best of our knowledge, this is the largest publicly available human annotated data set for open-domain information extraction tasks.", + "Logician is a supervised end-to-end neural learning algorithm which transforms natural language sentences into facts in the SAOKE form. Logician is trained under the attention-based sequence-to-sequence paradigm, with three mechanisms: restricted copy mechanism to ensure literally honestness, coverage mechanism to alleviate the under extraction and over extraction problem, and gated dependency attention mechanism to incorporate dependency information. Experimental results on four types of open information extraction tasks reveal the superiority of the Logician algorithm.", + "Our work will demonstrate that SAOKE format is suitable for expressing various types of knowledge and is friendly to end-to-end learning algorithms. Particularly, we will focus on showing that the supervised end-to-end learning is promising for OIE tasks, to extract entity and relation level intermediate structures.", + "The rest of this paper is organized as follows. Section \"SAOKE Format: Symbol Aided Open Knowledge Expression\" presents the details of SAOKE. Section \"SAOKE Data Set\" describes the human labeled SAOKE data set. Section \"Logician\" describes the Logician algorithm and Section \"Empirical Evaluation\" evaluates the Logician algorithm and compares its performance with the state-of-the-art algorithms on four OIE tasks. Section \"Related Works\" discusses the related work and Section \"Conclusion\" concludes the paper." + ], + [ + "When reading a sentence in natural language, humans are able to recognize the facts involved in the sentence and accurately express them. In this paper, Symbolic Aided Open Knowledge Expression (SAOKE) is proposed as the form for honestly recording these facts. SAOKE expresses the primary information of sentences in n-ary tuples $(subject,predicate,object_{1},\\cdots ,object_{N})$ , and (in this paper) neglects some auxiliary information. In the design of SAOKE, we take four requirements into consideration: completeness, accurateness, atomicity and compactness." + ], + [ + "After having analyzed a large number of sentences, we observe that the majority of facts can be classified into the following classes:", + "Relation: Verb/preposition based n-ary relations between entity mentions BIBREF15 , BIBREF6 ;", + "Attribute:Nominal attributes for entity mentions BIBREF16 , BIBREF17 ;", + "Description: Descriptive phrases of entity mentions BIBREF18 ;", + "Concept: Hyponymy and synonym relations among concepts and instances BIBREF19 .", + "SAOKE is designed to express all these four types of facts. Table 1 presents an example sentence and the involved facts of these four classes in the SAOKE form. We should mention that the sentences and facts in English are directly translated from the corresponding Chinese sentences and facts, and the facts in English may not be the desired outputs of OIE algorithms for those English sentences due to the differences between Chinese and English languages." + ], + [ + "SAOKE adopts the ideology of \u201cliterally honest\u201d. That is, as much as possible, it uses the words in the original sentences to express the facts. SAOKE follows the philosophy of OIE systems to express various relations without relying on any predefined schema system. There are, however, exceptional situations which are beyond the expression ability of this format. Extra symbols will be introduced to handle these situations, which are explained as follows.", + "Separated relation phrase: In some languages such as Chinese, relation phrases may be divided into several parts residing in discontinued locations of the sentences. To accurately express these relation phrases, we add placeholders ( $X$ , $Y$ , $Z$ , etc) to build continuous and complete expressions. UTF8gbsn \u201c\u6df1\u53d7X\u5f71\u54cd\u201d (\u201cdeeply influenced by X\u201d in English) in the example of Table 1 is an instance of relation phrase after such processing.", + "Abbreviated expression: We explicitly express the information in abbreviated expressions by introducing symbolic predicates. For example, the expression of \u201cPerson (birth date - death date)\u201d is transformed into facts: (Person, BIRTH, birth date) (Person, DEATH, death date), and the synonym fact involved in \u201cNBA (National Basketball Association)\u201d is expressed in the form of (NBA, = , National Basketball Association) .", + "Hidden information: Description of an entity and hyponymy relation between entities are in general expressed implicitly in sentences, and are expressed by symbolic predicates \u201cDESC\u201d and \u201cISA\u201d respectively, as in Table 1 . Another source of hidden information is the address expression. For example, UTF8gbsn \u201c\u6cd5\u56fd\u5df4\u9ece\u201d (\u201cParis, France\u201d in English) implies the fact UTF8gbsn (\u5df4\u9ece, LOC, \u6cd5\u56fd) ((Paris, LOC, France) in English), where the symbol \u201cLOC\u201d means \u201clocation\u201d.", + "Missing information: A sentence may not tell us the exact relation between two entities, or the exact subject/objects of a relation, which are required to be inferred from the context. We use placeholders like \u201c $X,Y,Z$ \u201d to denote the missing subjects/objects, and \u201c $P$ \u201d to denote the missing predicates." + ], + [ + "Atomicity is introduced to eliminate the ambiguity of knowledge expressions. In SAOKE format, each fact is required to be atomic, which means that: (i) it is self-contained for an accurate expression; (ii) it cannot be decomposed into multiple valid facts. We provide examples in Table 2 to help understand these two criteria.", + "Note that the second criterion implies that any logical connections (including nested expressions) between facts are neglected (e.g., the third case in Table 2 ). This problem of expression relations between facts will be considered in the future version of SAOKE." + ], + [ + "Natural language may express several facts in a compact form. For example, in a sentence UTF8gbsn \u201c\u674e\u767d\u7231\u996e\u9152\u4f5c\u8bd7\u201d (\u201cLi Bai loved to drink and write poetry\u201d in English ), according to atomicity, two facts should be extracted: UTF8gbsn (\u674e\u767d, \u7231, \u996e\u9152)(\u674e\u767d, \u7231, \u4f5c\u8bd7) ( (Li Bai, loved to, drink)(Li Bai, loved to, write poetry) in English ). In this situation, SAOKE adopts a compact expression to merge these two facts into one expression: UTF8gbsn (\u674e\u767d, \u7231, [\u996e\u9152|\u4f5c\u8bd7]) ( (Li Bai, loved to, [drink| write poetry]) in English ).", + "The compactness of expressions is introduced to fulfill, but not to violate the rule of \u201cliterally honest\u201d. SAOKE does not allow merging facts if facts are not expressed compactly in original sentences. By this means, the differences between the sentences and the corresponding knowledge expressions are reduced, which may help reduce the complexity of learning from data in SAOKE form.", + "With the above designs, SAOKE is able to express various kinds of facts, with each historically considered by different open information extraction algorithms, for example, verb based relations in SRLIE BIBREF15 and nominal attributes in ReNoun BIBREF16 , BIBREF17 , descriptive phrases for entities in EntityTagger BIBREF18 , and hypernyms in HypeNet BIBREF19 . SAOKE introduces the atomicity to eliminate the ambiguity of knowledge expressions, and achieves better accuracy and compactness with the aid of the symbolic expressions." + ], + [ + "We randomly collect sentences from Baidu Baike (http://baike.baidu.com), and send those sentences to a crowd sourcing company to label the involved facts. The workers are trained with labeling examples and tested with exams. Then the workers with high exam scores are asked to read and understand the facts in the sentences, and express the facts in the SAOKE format. During the procedure, one sentence is only labeled by one worker. Finally, more than forty thousand sentences with about one hundred thousand facts are returned to us. The manual evaluation results on 100 randomly selected sentences show that the fact level precision and recall is 89.5% and 92.2% respectively. Table 3 shows the proportions of four types of facts (described in Section \"SAOKE Data Set\" ) contained in the data set. Note that the facts with missing predicates represented by \u201cP\u201d are classified into \u201cUnknown\u201d. We publicize the data set at https://ai.baidu.com/broad/subordinate?dataset=saoke.", + "Prior to the SAOKE data set, an annotated data set for OIE tasks with 3,200 sentences in 2 domains was released in BIBREF20 to evaluate OIE algorithms, in which the data set was said BIBREF20 \u201c13 times larger than the previous largest annotated Open IE corpus\u201d. The SAOKE data set is 16 times larger than the data set in BIBREF20 . To the best of our knowledge, SAOKE data set is the largest publicly available human labeled data set for OIE tasks. Furthermore, the data set released in BIBREF20 was generated from a QA-SRL data set BIBREF21 , which indicates that the data set only contains facts that can be discovered by SRL (Semantic Role Labeling) algorithms, and thus is biased, whereas the SAOKE data set is not biased to an algorithm. Finally, the SAOKE data set contains sentences and facts from a large number of domains." + ], + [ + "Given a sentence $S$ and a set of expected facts (with all the possible types of facts) $\\mathbb {F}=\\lbrace F_{1},\\cdots ,F_{n}\\rbrace $ in SAOKE form, we join all the facts in the order that annotators wrote them into a char sequence $F$ as the expected output. We build Logician under the attention-based sequence-to-sequence learning paradigm, to transform $S$ into $F$ , together with the restricted copy mechanism, the coverage mechanism and the gated dependency mechanism." + ], + [ + "The attention-based sequence-to-sequence learning BIBREF22 have been successfully applied to the task of generating text and patterns. Given an input sentence $S=[w_{1}^{S},\\cdots ,w_{N_{S}}^{S}]$ , the target sequence $F=[w_{1}^{F},\\cdots ,w_{N_{F}}^{F}]$ and a vocabulary $V$ (including the symbols introduced in Section \"SAOKE Format: Symbol Aided Open Knowledge Expression\" and the OOV (out of vocabulary) tag ) with size $N_{v}$ , the words $w_{i}^{S}$ and $w_{j}^{F}$ can be represented as one-hot vectors $v_{i}^{S}$ and $v_{j}^{F}$ with dimension $N_{v}$ , and transformed into $N_{e}$ -dimensional distributed representation vectors by an embedding transform $F=[w_{1}^{F},\\cdots ,w_{N_{F}}^{F}]$0 and $F=[w_{1}^{F},\\cdots ,w_{N_{F}}^{F}]$1 respectively, where $F=[w_{1}^{F},\\cdots ,w_{N_{F}}^{F}]$2 . Then the sequence of $F=[w_{1}^{F},\\cdots ,w_{N_{F}}^{F}]$3 is transformed into a sequence of $F=[w_{1}^{F},\\cdots ,w_{N_{F}}^{F}]$4 -dimensional hidden states $F=[w_{1}^{F},\\cdots ,w_{N_{F}}^{F}]$5 using bi-directional GRU (Gated Recurrent Units) network BIBREF23 , and the sequence of $F=[w_{1}^{F},\\cdots ,w_{N_{F}}^{F}]$6 is transformed into a sequence of $F=[w_{1}^{F},\\cdots ,w_{N_{F}}^{F}]$7 -dimensional hidden states $F=[w_{1}^{F},\\cdots ,w_{N_{F}}^{F}]$8 using GRU network.", + "For each position $t$ in the target sequence, the decoder learns a dynamic context vector $c_{t}$ to focus attention on specific location $l$ in the input hidden states $H^{S}$ , then computes the probability of generated words by $p(w_{t}^{F}|\\lbrace w_{1}^{F},\\cdots ,w_{t-1}^{F}\\rbrace ,c_{t})=g(h_{t-1}^{F},s_{t},c_{t})$ , where $s_{t}$ is the hidden state of the GRU decoder, $g$ is the word selection model (details could be found in BIBREF22 ), and $c_{t}$ is computed as $c_{t}=\\sum _{j=1}^{N_{S}}\\alpha _{tj}h_{j},$ where $\\alpha _{tj}=\\frac{\\exp (e_{tj})}{\\sum _{k=1}^{N_{S}}\\exp (e_{tk})}$ and $c_{t}$0 is the alignment model to measure the strength of focus on the $c_{t}$1 -th location. $c_{t}$2 , $c_{t}$3 , and $c_{t}$4 are weight matrices." + ], + [ + "The word selection model employed in BIBREF22 selects words from the whole vocabulary $V$ , which evidently violates the \u201cliteral honest\u201d requirement of SAOKE. We propose a restricted version of copy mechanism BIBREF24 as the word selection model for Logician:", + "We collect the symbols introduced in Section \"SAOKE Format: Symbol Aided Open Knowledge Expression\" into a keyword set $K=\\lbrace $ \u201c $ISA$ \u201d, \u201c $DESC$ \u201d, \u201c $LOC$ \u201d, \u201c $BIRTH$ \u201d, \u201c $DEATH$ \u201d, \u201c $=$ \u201d, \u201c $($ \u201d, \u201c)\u201d, \u201c $\\$$ \u201d,\u201c $[$ \u201d, \u201c $ISA$0 \u201d, \u201c $ISA$1 \u201d, \u201c $ISA$2 \u201d, \u201c $ISA$3 \u201d, \u201c $ISA$4 \u201d, \u201c $ISA$5 \u201d $ISA$6 where \u201c $ISA$7 \u201d is the separator of elements of fact tuples. \u201c $ISA$8 \u201d, \u201c $ISA$9 \u201d, \u201c $DESC$0 \u201d, \u201c $DESC$1 \u201d are placeholders . When the decoder is considering generating a word $DESC$2 , it can choose $DESC$3 from either $DESC$4 or $DESC$5 . ", + "$$p(w_{t}^{F}|w_{t-1}^{F},s_{t},c_{t})=p_{X}(w_{t}^{F}|w_{t-1}^{F},s_{t},c_{t})+p_{K}(w_{t}^{F}|w_{t-1}^{F},s_{t},c_{t}),$$ (Eq. 15) ", + "where $p_{X}$ is the probability of copying from $S$ and $p_{K}$ is the probability of selecting from $K$ . Since $S\\cap K=\\phi $ and there are no unknown words in this problem setting, we compute $p_{X}$ and $p_{K}$ in a simpler way than that in BIBREF24 , as follows: $\np_{X}(w_{t}^{F}=w_{j}^{S}) & = & \\frac{1}{Z}\\exp (\\sigma ((h_{j}^{S})^{T}W_{c})s_{t}),\\\\\np_{K}(w_{t}^{F}=k_{i}) & = & \\frac{1}{Z}\\exp (v_{i}^{T}W_{o}s_{t}),\n$ ", + "where the (generic) $Z$ is the normalization term, $k_{i}$ is one of keywords, $v_{i}$ is the one-hot indicator vector for $k_{i}$ , $W_{o}\\in \\mathbb {R}^{(|K|\\times N_{h})}$ , $W_{c}\\in \\mathbb {R}^{(N_{h}\\times N_{h})}$ , and $\\sigma $ is a nonlinear activation function." + ], + [ + "In practice, Logician may forget to extract some facts (under-extraction) or extract the same fact many times (over-extraction). We incorporate the coverage mechanism BIBREF25 into Logician to alleviate these problems. Formally, when the decoder considers generating a word $w_{t}^{F}$ , a coverage vector $m_{j}^{t}$ is introduced for each word $w_{j}^{S}$ , and updated as follows: $\nm_{j}^{t} & = & \\mu (m_{j}^{t-1},\\alpha _{tj},h_{j}^{S},s_{t-1})=(1-z_{i})\\circ m_{j}^{t-1}+z_{j}\\circ \\tilde{m}_{j}^{t},\\\\\n\\tilde{m}_{j}^{t} & = & \\tanh (W_{h}h_{j}^{S}+u_{\\alpha }\\alpha _{tj}+W_{s}s_{t-1}+U_{m}[r_{i}\\circ m_{j}^{t-1}]),\n$ ", + "where $\\circ $ is the element-wise multiplication operator. The update gate $z_{j}$ and the reset gate $r_{j}$ are defined as, respectively, $\nz_{j} & = & \\sigma (W_{h}^{z}h_{j}^{S}+u_{\\alpha }^{z}\\alpha _{tj}+W_{s}^{z}s_{t-1}+U_{m}^{z}m_{j}^{t-1}),\\\\\nr_{j} & = & \\sigma (W_{h}^{r}h_{j}^{S}+u_{\\alpha }^{r}\\alpha _{tj}+W_{s}^{r}s_{t-1}+U_{m}^{r}m_{j}^{t-1}),\n$ ", + "where $\\sigma $ is a logistic sigmoid function. The coverage vector $m_{j}^{t}$ contains the information about the historical attention focused on $w_{j}^{S}$ , and is helpful for deciding whether $w_{j}^{S}$ should be extracted or not. The alignment model is updated as follows BIBREF25 : $\ne_{tj}=a(s_{t-1},h_{j}^{S},m_{j}^{t-1})=v_{a}^{T}\\tanh (W_{a}s_{t-1}+U_{a}h_{j}^{S}+V_{a}m_{j}^{t-1}),\n$ ", + "where $V_{a}\\in \\mathbb {R}^{(N_{h}\\times N_{h})}$ ." + ], + [ + "The semantic relationship between candidate words and the previously decoded word is valuable to guide the decoder to select the correct word. We introduce the gated dependency attention mechanism to utilize such guidance.", + "For a sentence $S$ , we extract the dependency tree using NLP tools such as CoreNLP BIBREF26 for English and LTP BIBREF27 for Chinese, and convert the tree into a graph by adding reversed edges with a revised labels (for example, adding $w_{j}^{S}\\xrightarrow{}w_{i}^{S}$ for edge $w_{i}^{S}\\xrightarrow{}w_{j}^{S}$ in the dependency tree). Then for each pair of words $(w_{i}^{S},w_{j}^{S})$ , the shortest path with labels $L=[w_{1}^{L},\\cdots ,w_{N_{L}}^{L}]$ in the graph is computed and mapped into a sequence of $N_{e}$ -dimensional distributed representation vectors $[l_{1},\\cdots ,l_{N_{L}}]$ by the embedding operation. One can employ RNN network to convert this sequence of vectors into a feature vector, but RNN operation is time-consuming. We simply concatenate vectors in short paths ( $N_{L}\\le $ 3) into a $3N_{e}$ dimensional vector and feed the vector into a two-layer feed forward neural network to generate an $N_{h}$ -dimensional feature vector $w_{j}^{S}\\xrightarrow{}w_{i}^{S}$0 . For long paths with $w_{j}^{S}\\xrightarrow{}w_{i}^{S}$1 , $w_{j}^{S}\\xrightarrow{}w_{i}^{S}$2 is set to a zero vector. We define dependency attention vector $w_{j}^{S}\\xrightarrow{}w_{i}^{S}$3 , where $w_{j}^{S}\\xrightarrow{}w_{i}^{S}$4 is the sharpened probability $w_{j}^{S}\\xrightarrow{}w_{i}^{S}$5 defined in Equation ( 15 ). If $w_{j}^{S}\\xrightarrow{}w_{i}^{S}$6 , $w_{j}^{S}\\xrightarrow{}w_{i}^{S}$7 represents the semantic relationship between $w_{j}^{S}\\xrightarrow{}w_{i}^{S}$8 and $w_{j}^{S}\\xrightarrow{}w_{i}^{S}$9 . If $w_{i}^{S}\\xrightarrow{}w_{j}^{S}$0 , then $w_{i}^{S}\\xrightarrow{}w_{j}^{S}$1 is close to zero. To correctly guide the decoder, we need to gate $w_{i}^{S}\\xrightarrow{}w_{j}^{S}$2 to remember the previous attention vector sometimes (for example, when $w_{i}^{S}\\xrightarrow{}w_{j}^{S}$3 is selected), and to forget it sometimes (for example, when a new fact is started). Finally, we define $w_{i}^{S}\\xrightarrow{}w_{j}^{S}$4 $w_{i}^{S}\\xrightarrow{}w_{j}^{S}$5 ) as the gated dependency attention vector, where $w_{i}^{S}\\xrightarrow{}w_{j}^{S}$6 is the GRU gated function, and update the alignment model as follows: $w_{i}^{S}\\xrightarrow{}w_{j}^{S}$7 ", + "where $D_{a}\\in \\mathbb {R}^{(N_{h}\\times N_{h})}$ ." + ], + [ + "For each sequence generated by Logician, we parse it into a set of facts, remove tuples with illegal format or duplicated tuples. The resultant set is taken as the output of the Logician." + ], + [ + "We first measure the utility of various components in Logician to select the optimal model, and then compare this model to the state-of-the-art methods in four types of information extraction tasks: verb/preposition-based relation, nominal attribute, descriptive phrase and hyponymy relation. The SAOKE data set is split into training set, validating set and testing set with ratios of 80%, 10%, 10%, respectively. For all algorithms involved in the experiments, the training set can be used to train the model, the validating set can be used to select an optimal model, and the testing set is used to evaluate the performance.", + "For each instance pair $(S,F)$ in the test set, where $S$ is the input sentence and $F$ is the formatted string of ground truth of facts, we parse $F$ into a set of tuples $\\mathbb {F}=\\lbrace F_{i}\\rbrace _{j=1}^{M}$ . Given an open information extraction algorithm, it reads $S$ and produces a set of tuples $\\mathbb {G}=\\lbrace G_{i}\\rbrace _{j=1}^{N}$ . To evaluate how well the $\\mathbb {G}$ approximates $\\mathbb {F}$ , we need to match each $G_{i}$ to a ground truth fact $S$0 and check whether $S$1 tells the same fact as $S$2 . To conduct the match, we compute the similarity between each predicted fact in $S$3 and each ground truth fact in $S$4 , then find the optimal matching to maximize the sum of matched similarities by solving a linear assignment problem BIBREF28 . In the procedure, the similarity between two facts is defined as $S$5 ", + "where $G_{i}(l)$ and $F_{j}(l)$ denote the $l$ -th element of tuple $G_{i}$ and $F_{j}$ respectively, $\\mathbf {g}(\\cdot ,\\cdot )$ denotes the gestalt pattern matching BIBREF29 measure for two strings and $\\mathbf {n}(\\text{$\\cdot $)}$ returns the length of the tuple.", + "Given a matched pair of $G_{i}$ and $F_{j}$ , we propose an automatic approach to judge whether they tell the same fact. They are judged as telling the same fact if one of the following two conditions is satisfied:", + " $\\mathbf {n}(G_{i})=\\mathbf {n}(F_{j})$ , and $\\mathbf {g}(G_{i}(l),F_{j}(l))\\ge 0.85,l=1,\\cdots ,\\mathbf {n}(G_{i})$ ;", + " $\\mathbf {n}(G_{i})=\\mathbf {n}(F_{j})$ , and $\\mathbf {g}(\\mathcal {S}(G_{i}),\\mathcal {S}(F_{j})\\ge 0.85$ ;", + "where $\\mathcal {S}$ is a function formatting a fact into a string by filling the arguments into the placeholders of the predicate.", + "With the automatic judgment, the precision ( $P$ ), recall ( $R$ ) and $F_{1}$ -score over a test set can be computed. By defining a confidence measure and ordering the facts by their confidences, a precision-recall curve can be drawn to illustrate the overall performance of the algorithm. For Logician, the confidence of a fact is computed as the average of log probabilities over all words in that fact.", + "Beyond the automatic judgment, human evaluation is also employed. Given an algorithm and the corresponding fact confidence measure, we find a threshold that produces approximately 10% recall (measured by automatic judgment) on the validation set of SAOKE data set. A certain number of sentences (200 for verb/preposition based relation extraction task, and 1000 for other three tasks) are randomly chosen from the testing set of SAOKE data set, and the facts extracted from these sentences are filtered with that threshold. Then we invite three volunteers to manually refine the labeled set of facts for each sentence and vote to decide whether each filtered fact is correctly involved in the sentence. The standard precision, recall and $F_{1}$ -score are reported as the human evaluation results.", + "For each instance pair $(S,F)$ in the training set of SAOKE data set, we split $S$ and $F$ into words using LTP toolset BIBREF27 , and words appearing in more than 2 sentences are added to the vocabulary. By adding the OOV (out of vocabulary) tag, we finally obtain a vocabulary $V$ with size $N_{V}=65,293$ . The dimension of all embedding vectors is set to $N_{e}=200$ , and the dimension of hidden states is set to $N_{h}=256$ . We use a three-layer bi-directional GRU with dimension 128 to encode $\\lbrace x_{i}\\rbrace _{i=1}^{N_{S}}$ into hidden states $\\lbrace h_{i}^{S}\\rbrace _{i=1}^{N_{S}}$ , and a two-layer GRU with hidden-dimension 256 to encode the sequence of $\\lbrace y_{j}\\rbrace _{j=1}^{N_{F}}$ into hidden states $S$0 . Finally, the Logician network is constructed as stated in Section \"Logician\" . The Logician is then trained using stochastic gradient descent (SGD) with RMSPROP BIBREF30 strategy for 20 epochs with batch size 10 on the training set of SAOKE data set. The model with best $S$1 -score by automatic judgment on the validation set is selected as the trained model. When the model is trained, given a sentence, we employ the greedy search procedure to produce the fact sequences." + ], + [ + "In this section, we analyze the effects of components involved in Logician: restricted copy, coverage, and gated dependency. Since the restricted copy mechanism is the essential requirement of Logician in order to achieve the goal of literally honest, we take the Logician with only copy mechanism (denoted by $Copy$ ) as the baseline, and analyze the effeteness of coverage mechanism (denoted by $Copy+Coverage$ ), gated dependency mechanism (denoted by $Copy+GatedDep$ ) and both (denoted by $All$ ). Furthermore, there is another option of whether or not to involve shallow semantic information such as POS-tag and NER-tag into the model. For models involving such information, the POS-tag and NER-tag of each word in sentence $S$ are annotated using LTP. For each word in $F$ that is not any keyword in $K$ , the POS-tag and NER-tag are copied from the corresponding original word in $S$ . For each keyword in $K$ , a unique POS-tag and a unique NER-tag are assigned to it. Finally, for each word in $S$ or $Copy+Coverage$0 , the POS-tag and NER-tag are mapped into $Copy+Coverage$1 -dimensional distributed representation vectors and are concatenated into $Copy+Coverage$2 or $Copy+Coverage$3 to attend the training.", + "All models are trained using the same settings described in above section, and the default output facts (without any confidence filtering) are evaluated by the automatic judgment. The results are reported in Table 4 . From the results, we can see that the model involving all the components and shallow tag information archives the best performance. We use that model to attend the comparisons with existing approaches." + ], + [ + "In the task of extracting verb/preposition based facts, we compare our Logician with the following state-of-the-art Chinese OIE algorithms:", + "SRLIE: our implementation of SRLIE BIBREF15 for the Chinese language, which first uses LTP tool set to extract the semantic role labels, and converts the results into fact tuples using heuristic rules. The confidence of each fact is computed as the ratio of the number of words in the fact to the number of words in the shortest fragment of source sentence that contains all words in the fact.", + "ZORE : the Chinese Open Relation Extraction system BIBREF31 , which builds a set of patterns by bootstrapping based on dependency parsing results, and uses the patterns to extract relations. We used the program provided by the author of ZORE system BIBREF31 to generate the extraction results in XML format, and developed an algorithm to transform the facts into n-ary tuples, where auxiliary information extracted by ZORE is removed. The confidence measure for ZORE is the same as that for SRLIE.", + "SRL $_{\\text{SAOKE}}$ : our implementation of the states-of-the-art SRL algorithm proposed in BIBREF32 with modifications to fit OIE tasks. $\\text{SRL}_{\\text{SAOKE}}$ extracts facts in two steps: (i) Predicate head word detection: detects head word for predicate of each possible fact, where head word of a predicate is the last word in the predicate depending on words outside the predicate in the dependency tree. (ii) Element phrase detection: For each detected head word, detects the subject phrase, predicate phrase and object phrases by tagging the sentence with an extended BIOE tagging scheme, which tags the word neighboring the separation point of the phrase by \u201cM\u201d to cope with the separated phrase. We modify the code provided by the author of BIBREF32 to implement above strategy, and then train a model with the same parameter setting in BIBREF32 on the training set of SAOKE data set. The confidence measure for $\\text{SRL}_{\\text{SAOKE}}$ is computed as the average of log probabilities over all tags of words in facts. Note that $\\text{SRL}_{\\text{SAOKE}}$ can extract both verb/preposition based relation and nominal attributes, but in this section, we only evaluate the results of the former type of facts.", + "The precision-recall curves of Logician and above three comparison algorithms are shown in Figure 1 , and the human evaluation results are shown in the first section of Table 5 .", + "The state-of-the-art", + "nominal attribute extraction method is ReNoun BIBREF16 , BIBREF17 . However, it relies on a pre-constructed English attribute schema system BIBREF33 which is not available for Chinese, so it is not an available baseline for Chinese. Since $\\text{SRL}_{\\text{SAOKE}}$ can extract nominal attributes, we compare Logician with $\\text{SRL}_{\\text{SAOKE}}$ on this task. The precision-recall curves of Logician and $\\text{SRL}_{\\text{SAOKE}}$ on the nominal attribute extraction task are shown in Figure 1 , and the human evaluation results are shown in the second section of Table 5 .", + "Descriptive phrase extraction has been considered in BIBREF18 , in which domain names are required to develop patterns to extract candidates for descriptive phrases, so this method is not applicable to open domain tasks. We develop a baseline algorithm (called Semantic Dependency Description Extractor, SDDE) to extract descriptive phrase. It extracts semantic dependency relation between words using LTP toolset, and for each noun $w_n$ which is the parent of some semantic \u201cDesc\u201d relations, identifies a noun phrase $N$ with $w_n$ as its heading word, assembles a descriptive phrase $D$ containing all words with \u201cDesc\u201d relation to $w_n$ , and finally outputs the fact \u201c( $N$ , $DESC$ , $D$ )\u201d. The confidence of fact in SDDE is computed as the ratio of the number of adverbs and adjectives in $D$ to the number of words in $D$ . The precision-recall curves of Logician and SDDE on the descriptive phrase extraction task are shown in Figure 1 , and the human evaluation results are shown in the third section of Table 5 .", + "HypeNet BIBREF19 is the state-of-the-art algorithm recommended for hyponymy extraction BIBREF34 , which judges whether hyponymy relation exists between two given words. To make it capable of judging hyponymy relation between two phrases, we replace the word embedding vector component in HypeNet by an LSTM network. Two modified HypeNet models are built using different training data sets: (i) $\\text{HypeNet}_{\\text{Phrase}}$ : using the pairs of phrases with ISA relation in the training set of SAOKE data set (9,407 pairs after the compact expression expansion); (ii) $\\text{HypeNet}_{\\text{Phrase}}^{\\text{Extra}}$ : besides the training set for $\\text{HypeNet}_{\\text{Phrase}}$ , adding two Chinese hyponymy data sets (1.4 million pair of words in total in hyponymy relation): Tongyici Cilin (Extended) (CilinE for short) BIBREF27 and cleaned Wikipedia Category data BIBREF35 . In both cases, the sentences from both Chinese Wikipedia pages and training set of SAOKE data set are taken as the background corpus for the HypeNet algorithm. In the testing phase, the trained models are used to predict whether the hyponymy relation exists for each pair of noun phrases/words in sentences of the testing set of SAOKE data set. The confidence of a judgment is the predicted probability of the existence of hyponymy relation. The precision-recall curves of Logician, $\\text{HypeNet}_{\\text{Phrase}}$ and $\\text{HypeNet}_{\\text{Phrase}}^{\\text{Extra}}$ are shown in Figure 1 , and the human evaluation results in the fourth section of Table 5 ." + ], + [ + "The experimental results reveal that, Logician outperforms the comparison methods with large margin in first three tasks. For hyponymy detection tasks, Logician overwhelms the $\\text{HypeNet}_{\\text{Phrase}}$ using the same training data, and produces comparable results to $\\text{HypeNet}_{\\text{Phrase}}^{\\text{Extra}}$ with much less training data. Table 6 exhibits several example sentences and the facts extracted by these algorithms.", + "The poor performance of pattern-based methods is plausibly due to the noise in SAOKE data set. The sentences in SAOKE data set are randomly selected from a web encyclopedia, with free and casual writing style, are thus more noisy than the training data of NLP toolset used by these methods. In this situation, the NLP toolset may produce poor results, so do the pattern-based methods.", + "Models learned from the SAOKE data set archive much better performance. Nevertheless, $\\text{SRL}_{\\text{SAOKE}}$ extracts each fact without knowing whether a candidate word has been used in other facts, which results in the misleading overlap of the word UTF8gbsn\u201c\u5b66\u201d (\u201cLearn\u201d in English) between two facts in the first case of Table 6 . Similarly, $\\text{HypeNet}_{\\text{Phrase}}$ and $\\text{HypeNet}_{\\text{Phrase}}^{\\text{Extra}}$ focus on the semantic vectors of pairs of phrases and their dependency paths in the background corpus. They extract each fact independently from other facts and hence do not know whether there have been any other relations extracted about these two phrases. In other words, for those comparison methods, an important source of information is neglected and a global optimization for all facts involved in sentences is absent.", + "On the contrary, Logician performs global optimization over the facts involved in each sentence by the sequence-to-sequence learning paradigm with the help of the coverage mechanism, in which facts compete each other to attract the attention of words, but also cooperate to share words. Valuable information is shared between these multiple tasks, which makes Logician consistently superior to other algorithms in these tasks.", + "Furthermore, $\\text{SRL}_{\\text{SAOKE}}$ and $\\text{HypeNet}$ methods suffer from the OOV problem, such as unfamiliar words/phrases like the person name and school name in the last case of Table 6 . In this situation they may fail to produce a reasonable result. Logician is able to cope with unfamiliar words/phrases by exploiting the context information using deep RNN network with the help of copy mechanism." + ], + [ + "We do a preliminary analysis for the results produced by the Logician model. The most notable problem is that it is unable to recall some facts for long or complex sentences. The last case in Table 6 exhibits such situation, where the fact UTF8gbsn(\u8521\u7ade,ISA,\u7ecf\u6d4e\u5b66\u535a\u58eb)((Cai Jing, ISA, Ph. D. in economics) in English) is not recalled. This phenomenon indicates that the coverage mechanism may lose effectiveness in this situation. The second class of error is incomplete extraction, as exhibited in the third case in Table 6 . Due to the incomplete extraction, the left parts may interfere the generation of other facts, and result in nonsense results, which is the third class of error. We believe it is helpful to introduce extra rewards into the learning procedure of Logician to overcome these problems. For example, the reward could be the amount of remaining information left after the fact extraction, or the completeness of extracted facts. Developing such rewards and reinforcement learning algorithms using those rewards to refine Logician belongs to our future works." + ], + [ + "Tuple is the most common knowledge expression format for OIE systems to express n-ary relation between subject and objects. Beyond such information, ClausIE BIBREF36 extracts extra information in the tuples: a complement, and one or more adverbials, and OLLIE BIBREF6 extracts additional context information. SAOKE is able to express n-ary relations, and can be easily extended to support the knowledge extracted by ClausIE, but needs to be redesigned to support context information, which belongs to our future work.", + "However, there is a fundamental difference between SAOKE and tuples in traditional OIE systems. In traditional OIE systems, knowledge expression is generally not directly related to the extraction algorithm. It is a tool to reorganize the extracted knowledge into a form for further easy reading/storing/computing. However, SAOKE is proposed to act as the direct learning target of the end-to-end Logician model. In such end-to-end framework, knowledge representation is the core of the system, which decides what information would be extracted and how complex the learning algorithm would be. To our knowledge, SAOKE is the first attempt to design a knowledge expression friendly to the end-to-end learning algorithm for OIE tasks. Efforts are still needed to make SAOKE more powerful in order to express more complex knowledge such as events." + ], + [ + "Relation extraction is the task to identify semantic connections between entities. Major existing relation extraction algorithms can be classified into two classes: closed-domain and open-domain. Closed-domain algorithms are learnt to identify a fixed and finite set of relations, using supervised methods BIBREF37 , BIBREF38 , BIBREF39 , BIBREF40 or weakly supervised methods BIBREF1 , BIBREF41 , while the open-domain algorithms, represented by aforementioned OIE systems, discover open-domain relations without predefined schema. Beyond these two classes, methods like universal schema BIBREF42 are able to learn from both data with fixed and finite set of relations, such as relations in Freebase, and data with open-domain surface relations produced by heuristic patterns or OIE systems.", + "Logician can be used as an OIE system to extract open-domain relation between entities, and act as sub-systems for knowledge base construction/completion with the help of schema mapping BIBREF43 . Compared with existing OIE systems, which are pattern-based or self-supervised by labeling samples using patterns BIBREF13 , to our knowledge Logician is the first model trained in a supervised end-to-end approach for OIE task, which has exhibited powerful ability in our experiments. There are some neural based end-to-end systems BIBREF39 , BIBREF40 , BIBREF41 proposed for relation extraction, but they all aim to solve the close-domain problem.", + "However, Logician is not limited to relation extraction task. First, Logician extracts more information beyond relations. Second, Logician focuses on examining how natural languages express facts BIBREF5 , and producing helpful intermediate structures for high level tasks." + ], + [ + "Efforts had been made to map natural language sentences into logical form. Some approaches such as BIBREF44 , BIBREF45 , BIBREF46 , BIBREF47 learn the mapping under the supervision of manually labeled logical forms, while others BIBREF48 , BIBREF49 are indirectly supervised by distant information, system rewards, etc. However, all previous works rely on a pre-defined, domain specific logical system, which limits their ability to learn facts out of the pre-defined logical system.", + "Logician can be viewed as a system that maps language to natural logic, in which the majority of information is expressed by natural phrase. Other than systems mentioned above which aim at execution using the logical form, Logician focuses on understanding how the fact and logic are expressed by natural language. Further mapping to domain-specific logical system or even executor can be built on the basis of Logician's output, and we believe that, with the help of Logician, the work would be easier and the overall performance of the system may be improved." + ], + [ + "The problem of generating sentences from a set of facts has attracted a lot of attentions BIBREF50 , BIBREF51 , BIBREF52 , BIBREF53 . These models focus on facts with a predefined schema from a specific problem domain, such as people biographies and basketball game records, but could not work on open domain. The SAOKE data set provides an opportunity to extend the ability of these models into open domain." + ], + [ + "As mentioned in above sections, the SAOKE data set provides examples of dual mapping between facts and sentences. Duality has been verified to be useful to promote the performance of agents in many NLP tasks, such as back-and-forth translation BIBREF54 , and question-answering BIBREF55 . It is a promising approach to use the duality between knowledge and language to improve the performance of Logician." + ], + [ + "In this paper, we consider the open information extraction (OIE) problem for a variety of types of facts in a unified view. Our solution consists of three components: SAOKE format, SAOKE data set, and Logician. SAOKE form is designed to express different types of facts in a unified manner. We publicly release the largest manually labeled data set for OIE tasks in SAOKE form. Using the labeled SAOKE data set, we train an end-to-end neural sequence-to-sequence model, called Logician, to transform sentences in natural language into facts. The experiments reveal the superiority of Logician in various open-domain information extraction tasks to the state-of-the-art algorithms.", + "Regarding future work, there are at least three promising directions. Firstly, one can investigate knowledge expression methods to extend SAOKE to express more complex knowledge, for tasks such as event extraction. Secondly, one can develop novel learning strategies to improve the performance of Logician and adapt the algorithm to the extended future version of SAOKE. Thirdly, one can extend SAOKE format and Logician algorithm in other languages." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-1157/instruction.md b/qasper-1157/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..4049aa3dc634dc31819f1f5b63968043b585337c --- /dev/null +++ b/qasper-1157/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: ISS-MULT: Intelligent Sample Selection for Multi-Task Learning in Question Answering + +Question: How do they transfer the model? \ No newline at end of file diff --git a/qasper-1159/instruction.md b/qasper-1159/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..2365dc0b5c47ab52b9d0eebb81e0bbad8c08752d --- /dev/null +++ b/qasper-1159/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Improving Span-based Question Answering Systems with Coarsely Labeled Data + +Question: What is the underlying question answering algorithm? \ No newline at end of file diff --git a/qasper-1161/instruction.md b/qasper-1161/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..7a03ee557e930ed5cba64694274ba44b1185f4a4 --- /dev/null +++ b/qasper-1161/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: AandP: Utilizing Prolog for converting between active sentence and passive sentence with three-steps conversion + +Question: Is there a machine learning approach that tries to solve same problem? \ No newline at end of file diff --git a/qasper-1166/instruction.md b/qasper-1166/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..c44ca1bc52d75f839bc2a9be682f591c4c1aee68 --- /dev/null +++ b/qasper-1166/instruction.md @@ -0,0 +1,148 @@ +Name of Paper: AandP: Utilizing Prolog for converting between active sentence and passive sentence with three-steps conversion + +Question: Are there some experiments performed in the paper? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Analysis and Discussion ::: Cases to be solved", + "Analysis and Discussion ::: Representation and Inference", + "Design and Implementation ::: Scenario for user interaction", + "Design and Implementation ::: Auxiliary-based solution to handle 12 English tenses", + "Design and Implementation ::: Three-steps conversion", + "Design and Implementation ::: Others", + "Results", + "Conclusion" + ], + "paragraphs": [ + [ + "Language plays a vital role in the human life. A language is a structured system of communication BIBREF2. There are various language systems in the world with the estimated number being between 5,000 and 7,000 BIBREF3. Natural Language Processing (NLP) which we commonly hear is a subfield of linguistics. NLP aims to provide interactions between computers and human languages. The performance of NLP is evaluated by how computers can process and analyze large amounts of natural language data BIBREF4. In terms of language processing, we cannot but mention Computational Linguistics BIBREF5. Computational Linguistics is the scientific study of language from a computational perspective, and thus an interdisciplinary field, involving linguistics, computer science, mathematics, logic, cognitive science, and cognitive psychology.", + "One of the most useful tools for studying computational linguistics is Prolog programming language BIBREF0. Prolog is a logic programming language associated with artificial intelligence and computational linguistics. Prolog can help deal with issues related to not only logic puzzle (Cryptoarithmetic puzzles, Zebra Puzzle, etc.) but also natural language processing. In this work, I utilized Prolog along with Definite Clause Grammars (DCG) BIBREF1 to solve one of the critical aspects of English grammar, active sentence and passive sentence. DCG proves the efficiency in handling the grammar of the sentence. Basically, a sentence is built out of noun phrase and verb phrase, so the structure of sentence, noun phrase, and verb phrase will be both covered in this work.", + "In terms of English grammar, we have lots of content to solve as shown in Figure FIGREF1. For example, there are 12 tenses in English such as the simple past tense, the simple present tense, the perfect present tense, etc. We also have more than three types of conditional clause, more than three types of comparative clause, and so on. This work covers the contents of active sentence and passive sentence. For instance, if an active sentence is \u201ca man buys an apple in the supermarket\", its corresponding passive sentence will be \u201can apple is bought by a man in the supermarket\". The basic rules for rewriting an active sentence to passive sentence are shown clearly in Figure FIGREF2.", + "As shown in Figure FIGREF2, basic rules are:", + "The object of the active sentence becomes the subject of the passive sentence;", + "The subject of the active sentence becomes the object of the passive sentence;", + "The finite form of the verb is changed to \u201cto be + past participle\".", + "As my best understanding so far, there are only a few works mentioning the problem of active sentence and passive sentence in terms of language processing and computational linguistics. The conversion between active sentence and passive sentence was early mentioned in BIBREF6 by using a transformation rule to express the relationship between active and passive sentences. According to this rule, a parse tree is produced to represent the deep structure and determine whether the given sentence is active or passive. Similarly, BIBREF7 also used a tree-to-tree mapping to represent the active/passive transformation rule. However, these works just stopped in introducing how to transform an active sentence to passive sentence and did not solve many cases of them. Actually, there are many cases of active and passive sentences, leading to extra rules for converting between them. It is not easy to handle all these cases, and this is the main challenge of this work. My contributions are shown as follows:", + "As far as I know, this may be the first work utilizing Prolog and DCG to solve a variety of cases of converting between active sentence and passive sentence such as 12 English tenses, modal verbs, negative form, etc.", + "I proposed a compact version of the representation of the sentence as shown in Figure FIGREF48 and Figure FIGREF50.", + "In order to deal with 12 tenses in English, I proposed an auxiliary-based solution (is presented in Section SECREF67) for dividing 12 tenses into 4 groups. This is a very nice solution that reduces the workload of defining DCG rules.", + "I also proposed a three-steps conversion (is presented in Section SECREF73) for doing the conversion between active sentence and passive sentence." + ], + [ + "The main challenge of this work is how much it can handle cases. There are a variety of cases in terms of active sentence and passive sentence. The cases that I solved in this work are shown as follows.", + "The possibility of the conversion: the prerequisite to convert an active sentence to a passive sentence is that the active sentence must have the object. For instance:", + "The sentence \u201cthe man buys an apple\" is converted to the passive form being \u201can apple is bought by the man\";", + "However, the sentence \u201cthe man goes to school\" cannot be converted to the passive form because of the lack of object.", + "The tenses of the sentence: there are 12 tenses in English such as simple present tense, continuous past tense, perfect present tense, perfect continuous future tense, etc. With each tense, there is a specific way for converting between active sentence and passive sentence. For example (from active form to passive form):", + "In the simple present tense: \u201cthe man buys an apple\" is converted to \u201can apple is bought by the man\";", + "In the perfect present tense: \u201cthe man has bought an apple\" is converted to \u201can apple has been bought by the man\".", + "This work handles all these 12 tenses.", + "The form of past participle: commonly, a verb is converted to past participle form by adding \u201ced\" at the end (example: \u201cadd\" becomes \u201cadded\", \u201clook\" becomes \u201clooked\"). However, there are some exceptions such as \u201cbuy\" becomes \u201cbought\", \u201csee\" becomes \u201cseen\", etc.", + "The case of negative sentence. For example, the negative form of \u201cthe man buys an apple\" is \u201cthe man does not buy an apple\", and the corresponding passive sentence is \u201can apple is not bought by the man\".", + "The case of modal verb: modal verbs (also called modals, modal auxiliary verbs, modal auxiliaries) are special verbs which behave irregularly in English. They are different from normal verbs like \u201cwork\", \u201cplay\", \u201cvisit\", etc. Modal verbs are always followed by an infinitive without \u201cto\". For example, the sentence \u201cthe boy should bring a pen to the class\" is converted to the passive form being \u201ca pen should be brought by the boy to the class\" (Figure FIGREF2).", + "Moreover, this work also handles the cases of singular/plural, subject pronoun/object pronoun, etc. For instance, the pronoun \u201che\" is used for the subject as \u201che\" but is used for the object as \u201chim\"." + ], + [ + "The objective of this work is sentences: active sentence and passive sentence, so I need to determine the representation of both active sentence and passive sentence.", + "An active sentence is built out of a noun phrase and a verb phrase. Therefore basically, the representation of an active sentence is s(NP,VP). The noun phrase or verb phrase is built out of fundamental elements such as determiner, noun, adjective, verb, etc. Simply, the representation of fundamental elements are shown as follows:", + "Determiner: det(X). Example: det(a), det(an), det(the), etc.", + "Noun: n(X). Example: n(man), n(woman), n(apple), etc.", + "Pronoun: pro(X). Example: pro(he), pro(she), pro(him), etc.", + "Adjective: adj(X). Example: adj(small), adj(big), adj(beautiful), etc.", + "Verb: v(X). Example: v(play), v(like), v(love), etc.", + "Preposition: pre(X). Example: pre(on), pre(in), pre(by), etc.", + "Auxiliary verb: aux(X). Example: aux(do), aux(does), aux(is), aux(be), etc. Actually, there are three types of auxiliary verbs are used in this work. For example, the sentence \u201cyou will have been loving them\" (perfect continuous future tense) has three auxiliary verbs are \u201cwill\", \u201chave\", \u201cbeen\" which are determined by three predicates aux/5, aux1/4, aux2/4 as shown in the source code (convertible.pl), respectively.", + "Auxiliary verb for tense in the passive form: auxTense(X). There are three groups of auxTense:", + "Group 1: including only simple future tense: auxTense(be). Example: \u201can apple will be bought buy the man\".", + "Group 2: consisting of continuous past tense, continuous present tense, continuous future tense, perfect continuous past tense, perfect continuous present tense, and perfect continuous future tense: auxTense(being). Example: \u201can apple was being bought by a man\", \u201can apple will be being bought by him\".", + "Group 3: including perfect past tense, perfect present tense, and perfect future tense: auxTense(been). Example: \u201can apple has been bought by the man\", \u201can apple will have been bought by the man\".", + "Modal verb: modal(X). Example: modal(should), modal(can), modal(may), etc.", + "Moreover, this work also uses pol(not) for the negative form and agent(by) for the passive form.", + "With a noun phrase, there are some ways to build the noun phrase such as:", + "A noun phrase is built out of a determiner and a noun, so its representation is np(DET,N). Example: noun phrase \u201cthe man\" has the representation is np(det(the),n(man)).", + "A noun phrase is built out of pronoun such as \u201che\", \u201cshe\", \u201cwe\", etc. In this case, the representation of the noun phrase is simply np(PRO). For example: np(pro(he)).", + "A noun phrase is built out of a determiner, adjectives, and a noun. In this case, the representation of the noun phrase is np(DET,ADJ,N). For example, the noun phrase \u201ca small beautiful girl\" has the representation is np(det(a),adi([small, beautiful]), n(girl)).", + "A noun phrase is built out of a noun phrase and a prepositional phrase. The representation of the noun phrase in this case is np(DET,N,PP), np(PRO,PP), or np(DET,ADJ,N,PP). For example, the noun phrase \u201ca cat on the big table\" has the representation is", + "np(det(a),n(cat),pp(pre(on),det(the),adj([big]),n(table))).", + "With a verb phrase, there are two ways to build the verb phrase:", + "A verb phrase is built out of a verb and a noun phrase. In this case, the presentation of the verb phrase is vp(V,NP). For example, the verb phrase \u201clove a beautiful woman\" has the representation is vp(v(love), np(det(a), adj([beautiful]), n(woman))).", + "A verb phrase is built out of only a verb, so its representation is simply vp(V). Example: vp(v(love)) or vp(v(eat)). In fact, as presented above, in order to be able to convert from an active sentence to a passive sentence, the active sentence has to have the object. Therefore, the case of verb phrase vp(V) will not be considered in this work.", + "After having the representation of noun phrase and verb phrase, the representation of the sentence could be obtained.", + "Originally, the active sentence \u201che buys an apple\" has the representation is", + "s(np(pro(he)),vp(v(buys),np(det(an),n(apple)))).", + "However, as presented above, this work only considers the case of verb phrase vp(V,NP), so I proposed a compact version of the representation of the sentence as shown in Figure FIGREF48.", + "Therefore, the active sentence \u201che buys an apple\" has the representation is", + "s(np(pro(he)), v(buys), np(det(an), n(apple))).", + "The passive sentence \u201can apple is bought by him\" has the representation is", + "s(np(det(an), n(apple)), aux(is), v(bought), agent(by), np(pro(", + "him))).", + "As introduced in the DCG BIBREF1, the representation of the sentence is represented by \u201cparse tree\" as illustrated in Figure FIGREF48 (active sentence) and Figure FIGREF50 (passive sentence). Parse tree could be found with the help of advanced techniques like extra arguments and extra goals.", + "\u201cInference\" is the conversion between a sentence and its representation, or even the conversion between an active sentence and a passive sentence:", + "Given a sentence, \u201cinference\" is the process of getting the representation of that sentence;", + "Given a representation of a sentence, \u201cinference\" is the process of getting that sentence.", + "The final purpose of this work is that:", + "Given an active sentence, we will get the respective passive sentence; and vice versa,", + "Given a passive sentence, we will get the respective active sentence." + ], + [ + "User interacts with the program by posing the query with the form (Figure FIGREF56):", + "convert(ActiveS, ActiveRe, PassiveS, PassiveRe).", + "Where:", + "ActiveS: the active sentence represented by a list where each element of the list corresponds to each word of the sentence. Example: [he,buys,an,apple].", + "ActiveRe: the representation of the active sentence ActiveS.", + "Example: s(np(pro(he)),v(buys),np(det(an),n(apple))).", + "PassiveS: the passive sentence represented by a list where each element of the list corresponds to each word of the sentence. Example: [an,apple,is,bought,by,him].", + "PassiveRe: the representation of the passive sentence PassiveS. Example:", + "s(np(det(an),n(apple)),aux(is),v(bought),agent(by),np(pro(him))).", + "Input will be either ActiveS or PassiveS for the case of converting from an active sentence to a passive sentence and the case of converting from a passive sentence to an active sentence, respectively.", + "There are several cases of output:", + "If the input is ActiveS and it is able to convert to the passive sentence, the outputs will be ActiveRe, PassiveS, and PassiveRe.", + "If the input is PassiveS and it is able to convert to the active sentence, the outputs will be ActiveS, ActiveRe, and PassiveRe.", + "If the input is either ActiveS or PassiveS but it is not able to convert to passive/active sentence, the output will be \u2018false\u2019. There are some cases which cannot be converted:", + "ActiveS is the active sentence but is typed as a passive sentence;", + "PassiveS is the passive sentence but is typed as an active sentence;", + "ActiveS is an active sentence having no object. Example: the sentence \u201che goes\" cannot be converted to the passive sentence.", + "Especially, we can pose the query with no input, and the program will generate all possible cases of the active sentence and passive sentence. Some examples to make user interaction more clear will be presented in Section SECREF4." + ], + [ + "There are 12 tenses in English. Each tense has a specific structure for the sentence. If each tense is handled individually, it will be quite long and be not an optimal solution. Therefore, as my best observation, I found a solution which divides 12 English tenses into 4 groups (same color means same group) based on the number of auxiliary verbs in the active sentence. This solution is summarized in Figure FIGREF72, consisting of:", + "Group 1: the number of auxiliary verbs in the active sentence is equal to 0. This group consists of the simple past tense and the simple present tense;", + "Group 2: the number of auxiliary verbs in the active sentence is equal to 1. We have 5 tenses in this group, those are the simple future tense, the continuous past tense, the continuous present tense, the perfect past tense, and the perfect present tense;", + "Group 3: the number of auxiliary verbs in the active sentence is equal to 2. This group consists of the continuous future tense, the perfect future tense, the perfect continuous past tense, and the perfect continuous present tense;", + "Group 4: the number of auxiliary verbs in the active sentence is equal to 3. This group has only one tense which is the perfect continuous future tense.", + "As we can easily see in Figure FIGREF72, tenses in the same group has the same structure of representation. For example, DCG rules for active sentence and passive sentence of group 3 are implemented as follows." + ], + [ + "The three-steps conversion consists of three steps:", + "From the input sentence fed as a list, the program first finds the representation of the sentence.", + "From the representation of active or passive sentence, the program then finds the representation of passive or active sentence, respectively.", + "From the representation achieved in the 2nd step, the program returns the converted sentence as a list.", + "The implementation of the three-steps conversion (written in convert.pl) is shown as follows.", + "The 1st and 3rd steps are done by using DCG rules (implemented in convertible.pl). The 2nd step is easily done by the rule like:", + "As you can see above, the 2nd step is easily done by doing the conversion between corresponding elements. More details for other groups are shown in convert.pl." + ], + [ + "All implementations above are for the positive form of the sentence. The negative form of the sentence can be easily done by inheriting the rules that are defined for the positive form. DCG rule for the negative form is implemented as follows.", + "DCG rules for the negative form is almost similar to those of the positive form, except from pol/1 predicate. However, in the 2nd step for the negative form, it completely utilizes the rule for the positive form as follows.", + "However, there is an exception of the 2nd step for group 1, it needs an extra rule like:", + "As we can see above, the negative form of group 1 needs the extra rule lex(AUX_POL,pol,Tense", + ",Qs) because, in this negative form, an extra auxiliary verb is needed. For example, the positive sentence is \u201che buys an apple\", but the corresponding negative sentence is \u201che does not buy an apple\". Other implementations such as lexicon, modal verbs, etc. are carefully written in the source code." + ], + [ + "This work has been already done with three files:", + "convertible.pl: implementing DCG rules for 1st and 3rd steps in the three-steps conversion, as well as other rules including lexicon.", + "convert.pl: implementing the three-steps conversion and its 2nd step.", + "testSuite.pl: providing commands for user interaction. Users do not need to type the input sentence as a list (like [the, man, buys, an, apple]) but can type the sentence in the common way (directly type: the man buys an apple) by using two commands: active and passive. Moreover, users can easily check the correctness of the program by using two test suite commands: activeTestSuite and passiveTestSuite.", + "Some execution examples are shown as follows.", + "It should be noted that if users use active or passive commands, everything they type has to be defined in the lexicon or users have to define them in the lexicon (implemented in convertible.pl)." + ], + [ + "I introduced an effort to solve the problem of active and passive sentences using Prolog in terms of computation linguistics. By observing the possibility of converting an active sentence to passive sentence, I proposed a compact version of the representation of the sentence (Figure FIGREF48 and Figure FIGREF50). I also introduced a solution called auxiliary-based solution (Section SECREF67) to deal with 12 tenses in English. The auxiliary-based solution helps to reduce the workload of defining DCG rules. Finally, I proposed the three-steps conversion (Section SECREF73) for converting between active sentence and passive sentence. In the future, this work should consider solving other cases of active and passive sentences as much as possible." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-1168/instruction.md b/qasper-1168/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..0490878f3d239387f3daf20c94daf985294d2ce1 --- /dev/null +++ b/qasper-1168/instruction.md @@ -0,0 +1,126 @@ +Name of Paper: Revealing the Dark Secrets of BERT + +Question: In which certain heads was attention disabled in experiments? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related work", + "Methodology", + "Experiments", + "Experiments ::: BERT's self-attention patterns", + "Experiments ::: BERT's self-attention patterns ::: Results", + "Experiments ::: Relation-specific heads in BERT", + "Experiments ::: Relation-specific heads in BERT ::: Results", + "Experiments ::: Change in self-attention patterns after fine-tuning", + "Experiments ::: Change in self-attention patterns after fine-tuning ::: Results", + "Experiments ::: Attention to linguistic features", + "Experiments ::: Attention to linguistic features ::: Results", + "Experiments ::: Token-to-token attention", + "Experiments ::: Token-to-token attention ::: Results", + "Experiments ::: Disabling self-attention heads", + "Experiments ::: Disabling self-attention heads ::: Results", + "Discussion", + "Conclusion" + ], + "paragraphs": [ + [ + "Over the past year, models based on the Transformer architecture BIBREF0 have become the de-facto standard for state-of-the-art performance on many natural language processing (NLP) tasks BIBREF1, BIBREF2. Their key feature is the self-attention mechanism that provides an alternative to conventionally used recurrent neural networks (RNN).", + "One of the most popular Transformer-based models is BERT, which learns text representations using a bi-directional Transformer encoder pre-trained on the language modeling task BIBREF2. BERT-based architectures have produced new state-of-the-art performance on a range of NLP tasks of different nature, domain, and complexity, including question answering, sequence tagging, sentiment analysis, and inference. State-of-the-art performance is usually obtained by fine-tuning the pre-trained model on the specific task. In particular, BERT-based models are currently dominating the leaderboards for SQuAD BIBREF3 and GLUE benchmarks BIBREF4.", + "However, the exact mechanisms that contribute to the BERT's outstanding performance still remain unclear. We address this problem through selecting a set of linguistic features of interest and conducting a series of experiments that aim to provide insights about how well these features are captured by BERT. This paper makes the following contributions:", + "We propose the methodology and offer the first detailed analysis of BERT's capacity to capture different kinds of linguistic information by encoding it in its self-attention weights.", + "We present the evidence of BERT's overparametrization and suggest a counter-intuitive yet frustratingly simple way of improving its performance, showing absolute gains of up to 3.2%." + ], + [ + "There have been several recent attempts to assess BERT's ability to capture structural properties of language. BIBREF5 demonstrated that BERT consistently assigns higher scores to the correct verb forms as opposed to the incorrect one in a masked language modeling task, suggesting some ability to model subject-verb agreement. BIBREF6 extended this work to using multiple layers and tasks, supporting the claim that BERT's intermediate layers capture rich linguistic information. On the other hand, BIBREF7 concluded that LSTMs generalize to longer sequences better, and are more robust with respect to agreement distractors, compared to Transformers.", + "BIBREF8 investigated the transferability of contextualized word representations to a number of probing tasks requiring linguistic knowledge. Their findings suggest that (a) the middle layers of Transformer-based architectures are the most transferable to other tasks, and (b) higher layers of Transformers are not as task specific as the ones of RNNs. BIBREF9 argued that models using self-attention outperform CNN- and RNN-based models on a word sense disambiguation task due to their ability to extract semantic features from text.", + "Our work contributes to the above discussion, but rather than examining representations extracted from different layers, we focus on the understanding of the self-attention mechanism itself, since it is the key feature of Transformer-based models.", + "Another research direction that is relevant to our work is neural network pruning. BIBREF10 showed that widely used complex architectures suffer from overparameterization, and can be significantly reduced in size without a loss in performance. BIBREF5 observed that the smaller version of BERT achieves better scores on a number of syntax-testing experiments than the larger one. BIBREF11 questioned the necessity of computation-heavy neural networks, proving that a simple yet carefully tuned BiLSTM without attention achieves the best or at least competitive results compared to more complex architectures on the document classification task. BIBREF12 presented more evidence of unnecessary complexity of the self-attention mechanism, and proposed a more lightweight and scalable dynamic convolution-based architecture that outperforms the self-attention baseline. These studies suggest a potential direction for future research, and are in good accordance with our observations." + ], + [ + "We pose the following research questions:", + "What are the common attention patterns, how do they change during fine-tuning, and how does that impact the performance on a given task? (Sec. SECREF17, SECREF30)", + "What linguistic knowledge is encoded in self-attention weights of the fine-tuned models and what portion of it comes from the pre-trained BERT? (Sec. SECREF25, SECREF34, SECREF36)", + "How different are the self-attention patterns of different heads, and how important are they for a given task? (Sec. SECREF39)", + "The answers to these questions come from a series of experiments with the basic pre-trained or the fine-tuned BERT models, as will be discussed below. All the experiments with the pre-trained BERT were conducted using the model provided with the PyTorch implementation of BERT (bert-base-uncased, 12-layer, 768-hidden, 12-heads, 110M parameters). We chose this smaller version of BERT because it shows competitive, if not better, performance while having fewer layers and heads, which makes it more interpretable.", + "We use the following subset of GLUE tasks BIBREF4 for fine-tuning:", + "MRPC: the Microsoft Research Paraphrase Corpus BIBREF13", + "STS-B: the Semantic Textual Similarity Benchmark BIBREF14", + "SST-2: the Stanford Sentiment Treebank, two-way classification BIBREF15", + "QQP: the Quora Question Pairs dataset", + "RTE: the Recognizing Textual Entailment datasets", + "QNLI: Question-answering NLI based on the Stanford Question Answering Dataset BIBREF3", + "MNLI: the Multi-Genre Natural Language Inference Corpus, matched section BIBREF16", + "Please refer to the original GLUE paper for details on the QQP and RTE datasets BIBREF4. We excluded two tasks: CoLa and the Winograd Schema Challenge. The latter is excluded due to the small size of the dataset. As for CoLa (the task of predicting linguistic acceptability judgments), GLUE authors report that the human performance is only 66.4, which is explained by the problems with the underlying methodology BIBREF17. Note also that CoLa is not included in the upcoming version of GLUE BIBREF18. All fine-tuning experiments follow the parameters reported in the original study (a batch size of 32 and 3 epochs, see devlin2018bert).", + "In all these experiments, for a given input, we extract self-attention weights for each head in every layer. This results in a 2D float array of shape $L\\times L$, where $L$ is the length of an input sequence. We will refer to such arrays as self-attention maps. Analysis of individual self-attention maps allows us to determine which target tokens are attended to the most as the input is processed token by token. We use these experiments to analyze how BERT processes different kinds of linguistic information, including the processing of different parts of speech (nouns, pronouns, and verbs), syntactic roles (objects, subjects), semantic relations, and negation tokens." + ], + [ + "In this section, we present the experiments conducted to address the above research questions." + ], + [ + "Manual inspection of self-attention maps for both basic pre-trained and fine-tuned BERT models suggested that there is a limited set of self-attention maps types that are repeatedly encoded across different heads. Consistently with previous observations, we identified five frequently occurring patterns, examples of which are shown in fig:atttypes:", + "Vertical: mainly corresponds to attention to special BERT tokens [CLS] and [SEP];", + "Diagonal: formed by the attention to the previous/following tokens;", + "Vertical+Diagonal: a mix of the previous two types,", + "Block: intra-sentence attention for the tasks with two distinct sentences (such as, for example, RTE or MRPC),", + "Heterogeneous: highly variable depending on the specific input and cannot be characterized by a distinct structure.", + "Whereas the attention to the special tokens is important for cross-sentence reasoning, and the attention to the previous/following token comes from language model pre-training, we hypothesize that the last of the listed types is more likely to capture interpretable linguistic features, necessary for language understanding.", + "To get a rough estimate of the percentage of attention heads that may capture linguistically interpretable information, we manually annotated around 400 sample self-attention maps as belonging to one of the five classes. The self-attention maps were obtained by feeding random input examples from selected tasks into the corresponding fine-tuned BERT model. This produced a somewhat unbalanced dataset, in which the \u201cVertical\u201d class accounted for 30% of all samples. We then trained a convolutional neural network with 8 convolutional layers and ReLU activation functions to classify input maps into one of these classes. This model achieved the F1 score of 0.86 on the annotated dataset. We used this classifier to estimate the proportion of different self-attention patterns for the target GLUE tasks using up to 1000 examples (where available) from each validation set." + ], + [ + "fig:attentionbydataset shows that the self-attention map types described above are consistently repeated across different heads and tasks. While a large portion of encoded information corresponds to attention to the previous/following token, to the special tokens, or a mixture of the two (the first three classes), the estimated upper bound on all heads in the \u201cHeterogeneous\u201d category (i.e. the ones that could be informative) varies from 32% (MRPC) to 61% (QQP) depending on the task.", + "We would like to emphasize that this only gives the upper bound on the percentage of attention heads that could potentially capture meaningful structural information beyond adjacency and separator tokens." + ], + [ + "In this experiment, our goal was to understand whether different syntactic and semantic relations are captured by self-attention patterns. While a large number of such relations could be investigated, we chose to examine semantic role relations defined in frame semantics, since they can be viewed as being at the intersection of syntax and semantics. Specifically, we focused on whether BERT captures FrameNet's relations between frame-evoking lexical units (predicates) and core frame elements BIBREF19, and whether the links between them produce higher attention weights in certain specific heads. We used pre-trained BERT in these experiments.", + "The data for this experiment comes from FrameNet BIBREF19, a database that contains frame annotations for example sentences for different lexical units. Frame elements correspond to semantic roles for a given frame, for example, \u201cbuyer\", \u201cseller\", and \u201cgoods\u201d for the \u201cCommercial_transaction\" frame evoked by the words \u201csell\u201d and \u201cspend\u201d or \u201ctopic\u201d and \u201ctext\u201d for the \u201cScrutiny\u201d semantic frame evoked by the verb \u201caddress\u201d. fig:framenet shows an example of such annotation.", + "We extracted sample sentences for every lexical unit in the database and identified the corresponding core frame elements. Annotated elements in FrameNet may be rather long, so we considered only the sentences with frame elements of 3 tokens or less. Since each sentences is annotated only for one frame, semantic links from other frames can exist between unmarked elements. We therefore filter out all the sentences longer than 12 tokens, since shorter sentences are less likely to evoke multiple frames.", + "To establish whether BERT attention captures semantic relations that do not simply correspond to the previous/following token, we exclude sentences where the linked objects are less than two tokens apart. This leaves us with 473 annotated sentences.", + "For each of these sentences, we obtain pre-trained BERT's attention weights for each of the 144 heads. For every head, we return the maximum absolute attention weight among those token pairs that correspond to the annotated semantic link contained within a given sentence. We then average the derived scores over all the collected examples. This strategy allows us to identify the heads that prioritize the features correlated with frame-semantic relations within a sentence." + ], + [ + "The heatmap of averaged attention scores over all collected examples (fig:framenetresults) suggests that 2 out of 144 heads tend to attend to the parts of the sentence that FrameNet annotators identified as core elements of the same frame. fig:framenetresults shows an example of this attention pattern for these two heads. Both show high attention weight for \u201che\u201d while processing \u201cagitated\u201d in the sentence \u201cHe was becoming agitated\" (the frame \u201cEmotion_directed\u201d)." + ], + [ + "Fine-tuning has a huge effect on performance, and this section attempts to find out why. To study how attention per head changes on average for each of the target GLUE tasks, we calculate cosine similarity between pre-trained and fine-tuned BERT's flattened arrays of attention weights. We average the derived similarities over all the development set examples. To evaluate contribution of pre-trained BERT to overall performance on the tasks, we consider two configurations of weights initialization, namely, pre-trained BERT weights and weights randomly sampled from normal distribution." + ], + [ + "fig:cosine shows that for all the tasks except QQP, it is the last two layers that undergo the largest changes compared to the pre-trained BERT model. At the same time, tab:glue-results shows that fine-tuned BERT outperforms pre-trained BERT by a significant margin on all the tasks (with an average of 35.9 points of absolute difference). This leads us to conclude that the last two layers encode task-specific features that are attributed to the gain of scores, while earlier layers capture more fundamental and low-level information used in fine-tuned models. Randomly initialized BERT consistently produces lower scores than the ones achieved with pre-trained BERT. In fact, for some tasks (STS-B and QNLI), initialization with random weights gives worse performance that that of pre-trained BERT alone without fine-tuning. This suggests that pre-trained BERT does indeed contain linguistic knowledge that is helpful for solving these GLUE tasks. These results are consistent with similar studies, e.g., BIBREF20's results on fine-tuning a convolutional neural network pre-trained on ImageNet or BIBREF21's results on transfer learning for medical natural language inference." + ], + [ + "In this experiment, we investigate whether fine-tuning BERT for a given task creates self-attention patterns which emphasize specific linguistic features. In this case, certain kinds of tokens may get high attention weights from all the other tokens in the sentence, producing vertical stripes on the corresponding attention maps (fig:atttypes).", + "We tested this hypothesis by checking whether there are vertical stripe patterns corresponding to certain linguistically interpretable features, and to what extent such features are relevant for solving a given task. In particular, we investigated attention to nouns, verbs, pronouns, subjects, objects, and negation words, and special BERT tokens across the tasks.", + "For every head, we compute the sum of self-attention weights assigned to the token of interest from each input token. Since the weights depend on the number of tokens in the input sequence, this sum is normalized by sequence length. This allows us to aggregate the weights for this feature across different examples. If there are multiple tokens of the same type (e.g. several nouns or negations), we take the maximum value. We disregard input sentences that do not contain a given feature.", + "For each investigated feature, we calculate this aggregated attention score for each head in every layer and build a map in order to detect the heads potentially responsible for this feature. We then compare the obtained maps to the ones derived using the pre-trained BERT model. This comparison enables us to determine if a particular feature is important for a specific task and whether it contributes to some tasks more than to others." + ], + [ + "Contrary to our initial hypothesis that the vertical attention pattern may be motivated by linguistically meaningful features, we found that it is associated predominantly, if not exclusively, with attention to [CLS] and [SEP] tokens (see Figure FIGREF32. Note that the absolute [SEP] weights for the SST-2 sentiment analysis task are greater than for other tasks, which is explained by the fact that there is only one sentence in the model inputs, i.e. only one [SEP] token instead of two. There is also a clear tendency for earlier layers to pay attention to [CLS] and for later layers to [SEP], and this trend is consistent across all the tasks. We did detect heads that paid increased (compared to the pre-trained BERT) attention to nouns and direct objects of the main predicates (on the MRPC, RTE and QQP tasks), and negation tokens (on the QNLI task), but the attention weights of such tokens were negligible compared to [CLS] and [SEP]. Therefore, we believe that the striped attention maps generally come from BERT pre-training tasks rather than from task-specific linguistic reasoning." + ], + [ + "To complement the experiments in Sec. SECREF34 and SECREF25, in this section, we investigate the attention patterns between tokens in the same sentence, i.e. whether any of the tokens are particularly important while a given token is being processed. We were interested specifically in the verb-subject relation and the noun-pronoun relation. Also, since BERT uses the representation of the [CLS] token in the last layer to make the prediction, we used the features from the experiment in Sec. SECREF34 in order to check if they get higher attention weights while the model is processing the [CLS] token." + ], + [ + "Our token-to-token attention experiments for detecting heads that prioritize noun-pronoun and verb-subject links resulted in a set of potential head candidates that coincided with diagonally structured attention maps. We believe that this happened due to the inherent property of English syntax where the dependent elements frequently appear close to each other, so it is difficult to distinguish such relations from the previous/following token attention coming from language model pre-training.", + "Our investigation of attention distribution for the [CLS] token in the output layer suggests that for most tasks, with the exception of STS-B, RTE and QNLI, the [SEP] gets attended the most, as shown in fig:cls. Based on manual inspection, for the mentioned remaining tasks, the greatest attention weights correspond to the punctuation tokens, which are in a sense similar to [SEP]." + ], + [ + "Since there does seem to be a certain degree of specialization for different heads, we investigated the effects of disabling different heads in BERT and the resulting effects on task performance. Since BERT relies heavily on the learned attention weights, we define disabling a head as modifying the attention values of a head to be constant $a = \\frac{1}{L}$ for every token in the input sentence, where $L$ is the length of the sentence. Thus, every token receives the same attention, effectively disabling the learned attention patterns while maintaining the information flow of the original model. Note that by using this framework, we can disable an arbitrary number of heads, ranging from a single head per model to the whole layer or multiple layers." + ], + [ + "Our experiments suggest that certain heads have a detrimental effect on the overall performance of BERT, and this trend holds for all the chosen tasks. Unexpectedly, disabling some heads leads not to a drop in accuracy, as one would expect, but to an increase in performance. This is effect is different across tasks and datasets. While disabling some heads improves the results, disabling the others hurts the results. However, it is important to note that across all tasks and datasets, disabling some heads leads to an increase in performance. The gain from disabling a single head is different for different tasks, ranging from the minimum absolute gain of 0.1% for STS-B, to the maximum of 1.2% for MRPC (see fig:disableheadsall). In fact, for some tasks, such as MRPC and RTE, disabling a random head gives, on average, an increase in performance. Furthermore, disabling a whole layer, that is, all 12 heads in a given layer, also improves the results. fig:disablelayers shows the resulting model performance on the target GLUE tasks when different layers are disabled. Notably, disabling the first layer in the RTE task gives a significant boost, resulting in an absolute performance gain of 3.2%. However, effects of this operation vary across tasks, and for QNLI and MNLI, it produces a performance drop of up to -0.2%." + ], + [ + "In general, our results suggest that even the smaller base BERT model is significantly overparametrized. This is supported by the discovery of repeated self-attention patterns in different heads, as well as the fact that disabling both single and multiple heads is not detrimental to model performance and in some cases even improves it.", + "We found no evidence that attention patterns that are mappable onto core frame-semantic relations actually improve BERT's performance. 2 out of 144 heads that seem to be \u201cresponsible\" for these relations (see Section SECREF25) do not appear to be important in any of the GLUE tasks: disabling of either one does not lead to a drop of accuracy. This implies that fine-tuned BERT does not rely on this piece of semantic information and prioritizes other features instead. For instance, we noticed that both STS-B and RTE fine-tuned models rely on attention in the same pair of heads (head 1 in the fourth layer, and head 12 in the second layer), as shown in Figure FIGREF37. We manually checked the attention maps in those heads for a set of random inputs, and established that both of them have high weights for words that appear in both sentences of the input examples. This most likely means that word-by-word comparison of the two sentences provides a solid strategy of making a classification prediction for STS-B and RTE. Unfortunately, we were not able to provide a conceptually similar interpretation of heads important for other tasks." + ], + [ + "In this work, we proposed a set of methods for analyzing self-attention mechanisms of BERT, comparing attention patterns for the pre-trained and fine-tuned versions of BERT.", + "Our most surprising finding is that, although attention is the key BERT's underlying mechanism, the model can benefit from attention \"disabling\". Moreover, we demonstrated that there is redundancy in the information encoded by different heads and the same patterns get consistently repeated regardless of the target task. We believe that these two findings together suggest a further direction for research on BERT interpretation, namely, model pruning and finding an optimal sub-architecture reducing data repetition.", + "Another direction for future work is to study self-attention patterns in a different language. We think that it would allow to disentangle attention maps potentially encoding linguistic information and heads that use simple heuristics like attending to the following/previous tokens." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-1192/instruction.md b/qasper-1192/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..cadb455e3f4a40b830ad0183e8ef80a6c9b65ddd --- /dev/null +++ b/qasper-1192/instruction.md @@ -0,0 +1,197 @@ +Name of Paper: The emergent algebraic structure of RNNs and embeddings in NLP + +Question: What text classification task is considered? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Summary of results", + "Intuition and motivation", + "Data and methods", + "Hyperparameters and model accuracy", + "Algebraic properties", + "Linear combination search", + "Embedding structure", + "Interpretation of results", + "Proposal for class of recurrent-like networks", + "Proposal for new word embeddings", + "Closing remarks", + "Acknowledgements" + ], + "paragraphs": [ + [ + "Tremendous advances in natural language processing (NLP) have been enabled by novel deep neural network architectures and word embeddings. Historically, convolutional neural network (CNN) BIBREF0 , BIBREF1 and recurrent neural network (RNN) BIBREF2 , BIBREF3 topologies have competed to provide state-of-the-art results for NLP tasks, ranging from text classification to reading comprehension. CNNs identify and aggregate patterns with increasing feature sizes, reflecting our common practice of identifying patterns, literal or idiomatic, for understanding language; they are thus adept at tasks involving key phrase identification. RNNs instead construct a representation of sentences by successively updating their understanding of the sentence as they read new words, appealing to the formally sequential and rule-based construction of language. While both networks display great efficacy at certain tasks BIBREF4 , RNNs tend to be the more versatile, have emerged as the clear victor in, e.g., language translation BIBREF5 , BIBREF6 , BIBREF7 , and are typically more capable of identifying important contextual points through attention mechanisms for, e.g., reading comprehension BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 . With an interest in NLP, we thus turn to RNNs.", + "RNNs nominally aim to solve a general problem involving sequential inputs. For various more specified tasks, specialized and constrained implementations tend to perform better BIBREF12 , BIBREF13 , BIBREF14 , BIBREF7 , BIBREF15 , BIBREF16 , BIBREF17 , BIBREF10 , BIBREF11 , BIBREF8 , BIBREF9 . Often, the improvement simply mitigates the exploding/vanishing gradient problem BIBREF18 , BIBREF19 , but, for many tasks, the improvement is more capable of generalizing the network's training for that task. Understanding better how and why certain networks excel at certain NLP tasks can lead to more performant networks, and networks that solve new problems.", + "Advances in word embeddings have furnished the remainder of recent progress in NLP BIBREF20 , BIBREF21 , BIBREF22 , BIBREF23 , BIBREF24 , BIBREF25 . Although it is possible to train word embeddings end-to-end with the rest of a network, this is often either prohibitive due to exploding/vanishing gradients for long corpora, or results in poor embeddings for rare words BIBREF26 . Embeddings are thus typically constructed using powerful, but heuristically motivated, procedures to provide pre-trained vectors on top of which a network can be trained. As with the RNNs themselves, understanding better how and why optimal embeddings are constructed in, e.g., end-to-end training can provide the necessary insight to forge better embedding algorithms that can be deployed pre-network training.", + "Beyond improving technologies and ensuring deep learning advances at a breakneck pace, gaining a better understanding of how these systems function is crucial for allaying public concerns surrounding the often inscrutable nature of deep neural networks. This is particularly important for RNNs, since nothing comparable to DeepDream or Lucid exists for them BIBREF27 .", + "To these ends, the goal of this work is two fold. First, we wish to understand any emergent algebraic structure RNNs and word embeddings, trained end-to-end, may exhibit. Many algebraic structures are well understood, so any hints of structure would provide us with new perspectives from which and tools with which deep learning can be approached. Second, we wish to propose novel networks and word embedding schemes by appealing to any emergent structure, should it appear.", + "The paper is structured as follows. Methods and experimental results comprise the bulk of the paper, so, for faster reference, \u00a7 SECREF2 provides a convenient summary and intrepretation of the results, and outlines a new class of neural network and new word embedding scheme leveraging the results. \u00a7 SECREF3 motivates the investigation into algebraic structures and explains the experimental setup. \u00a7 SECREF4 Discusses the findings from each of the experiments. \u00a7 SECREF5 interprets the results, and motivates the proposed network class and word embeddings. \u00a7 SECREF6 provides closing remarks and discusses followup work, and \u00a7 SECREF7 gives acknowledgments.", + "To make a matter of notation clear going forward, we begin by referring to the space of words as INLINEFORM0 , and transition to INLINEFORM1 after analyzing the results in order to be consistent with notation in the literature on algebraic spaces." + ], + [ + "We embedded words as vectors and used a uni-directional GRU connected to a dense layer to classify the account from which tweets may have originated. The embeddings and simple network were trained end-to-end to avoid imposing any artificial or heuristic constraints on the system.", + "There are two primary takeaways from the work presented herein:", + "The first point follows since 1) words are embedded in a continuous space; 2) an identity word exists that causes the RNN to act trivially on a hidden state; 3) word inverses exist that cause the RNN to undo its action on a hidden state; 4) the successive action of the RNN using two words is equivalent to the action of the RNN with a single third word, implying the multiplicative closure of words; and 5) words are not manifestly closed under any other binary action.", + "The second point follows given that words embed on a manifold, sentences traces out paths on the manifold, and the difference equation the RNN solves bears a striking resemble to the first order equation for parallel transport, DISPLAYFORM0 ", + " where INLINEFORM0 is the INLINEFORM1 -th hidden state encountered when reading over a sentence and INLINEFORM2 is the RNN conditioned by the INLINEFORM3 -th word, INLINEFORM4 , acting on the hidden state. Since sentences trace out a path on the word manifold, and parallel transport operators for representations of the word manifold take values in the group, the RNN must parallel transport hidden states either on the group itself or on a base space, INLINEFORM5 , equipped with some word field, INLINEFORM6 , that connects the path in the base space to the path on the word manifold.", + "Leveraging these results, we propose two new technologies.", + "First, we propose a class of recurrent-like neural networks for NLP tasks that satisfy the differential equation DISPLAYFORM0 ", + "where DISPLAYFORM0 ", + " and where INLINEFORM0 and INLINEFORM1 are learned functions. INLINEFORM2 corresponds to traditional RNNs, with INLINEFORM3 . For INLINEFORM4 , this takes the form of RNN cells with either nested internal memories or dependencies that extend temporally beyond the immediately previous hidden state. In particular, using INLINEFORM5 for sentence generation is the topic of a manuscript presently in preparation.", + "Second, we propose embedding schemes that explicitly embed words as elements of a Lie group. In practice, these embedding schemes would involve representing words as constrained matrices, and optimizing the elements, subject to the constraints, according to a loss function constructed from invariants of the matrices, and then applying the matrix log to obtain Lie vectors. A prototypical implementation, in which the words are assumed to be in the fundamental representation of the special orthogonal group, INLINEFORM0 , and are conditioned on losses sensitive to the relative actions of words, is the subject of another manuscript presently in preparation.", + "The proposals are only briefly discussed herein, as they are the focus of followup work; the focus of the present work is on the experimental evidence for the emergent algebraic structure of RNNs and embeddings in NLP." + ], + [ + "We provide two points to motivate examining the potential algebraic properties of RNNs and their space of inputs in the context of NLP.", + "First, a RNN provides a function, INLINEFORM0 , that successively updates a hidden memory vector, INLINEFORM1 , characterizing the information contained in a sequence of input vectors, INLINEFORM2 , as it reads over elements of the sequence. Explicitly, INLINEFORM3 . At face value, INLINEFORM4 takes the same form as a (nonlinear) representation of some general algebraic structure, INLINEFORM5 , with at least a binary action, INLINEFORM6 , on the vector space INLINEFORM7 . While demanding much structure on INLINEFORM8 generally places a strong constraint on the network's behavior, it would be fortuitous for such structure to emerge. Generally, constrained systems still capable of performing a required task will perform the task better, or, at least, generalize more reliably BIBREF28 , BIBREF29 , BIBREF30 , BIBREF31 , BIBREF32 , BIBREF33 . To this end, the suggestive form RNNs assume invites further examination to determine if there exist any reasonable constraints that may be placed on the network. To highlight the suggestiveness of this form in what follows, we represent the INLINEFORM9 argument of INLINEFORM10 as a subscript and the INLINEFORM11 argument by treating INLINEFORM12 as a left action on INLINEFORM13 , adopting the notation INLINEFORM14 . Since, in this paper, we consider RNNs vis-\u00e0-vis NLP, we take INLINEFORM15 as the (continuous) set of words.", + "Second, in the massive exploration of hyperparameters presented in BIBREF5 , it was noted that, for a given word embedding dimension, the network's performance on a seq2seq task was largely insensitive to the hidden dimension of the RNN above a threshold ( INLINEFORM0 128). The dimension of admissible representations of a given algebraic structure is generally discrete and spaced out. Interpreting neurons as basis functions and the output of layers as elements of the span of the functions BIBREF34 , BIBREF35 , BIBREF36 , we would expect a network's performance to improve until an admissible dimension for the representation is found, after which the addition of hidden neurons would simply contribute to better learning the components of the proper representation by appearing in linear combinations with other neurons, and contribute minimally to improving the overall performance. In their hyperparameter search, a marginal improvement was found at a hidden dimension of 2024, suggesting a potentially better representation may have been found.", + "These motivating factors may hint at an underlying algebraic structure in language, at least when using RNNs, but they raise the question: what structures are worth investigating?", + "Groups present themselves as a candidate for consideration since they naturally appear in a variety of applications. Unitary weight matrices have already enjoyed much success in mitigating the exploding/vanishing gradients problem BIBREF13 , BIBREF14 , and RNNs even further constrained to act explicitly as nonlinear representations of unitary groups offer competitive results BIBREF15 . Moreover, intuitively, RNNs in NLP could plausibly behave as a group since: 1) the RNN must learn to ignore padding words used to square batches of training data, indicating an identity element of INLINEFORM0 must exist; 2) the existence of contractions, portmanteaus, and the Germanic tradition of representing sentences as singular words suggest INLINEFORM1 might be closed; and 3) the ability to backtrack and undo statements suggests language may admit natural inverses - that is, active, controlled \u201cforgetting\" in language may be tied to inversion. Indeed, groups seem reasonably promising.", + "It is also possible portmanteaus only make sense for a finite subset of pairs of words, so INLINEFORM0 may take on the structure of a groupoid instead; moreover, it is possible, at least in classification tasks, that information is lost through successive applications of INLINEFORM1 , suggesting an inverse may not actually exist, leaving INLINEFORM2 as either a monoid or category. INLINEFORM3 may also actually admit additional structure, or an additional binary operation, rendering it a ring or algebra.", + "To determine what, if any, algebraic structure INLINEFORM0 possesses, we tested if the following axiomatic properties of faithful representations of INLINEFORM1 hold:", + "(Identity) INLINEFORM0 such that INLINEFORM1 , INLINEFORM2 ", + "(Closure under multiplication) INLINEFORM0 , INLINEFORM1 such that INLINEFORM2 , INLINEFORM3 ", + "(Inverse) INLINEFORM0 , INLINEFORM1 such that INLINEFORM2 , INLINEFORM3 ", + "(Closure under Lie bracket) INLINEFORM0 , INLINEFORM1 such that INLINEFORM2 , INLINEFORM3 ", + "Closure under Lie bracket simultaneously checks for ring and Lie algebra structures.", + "Whatever structure, if any, INLINEFORM0 possesses, it must additionally be continuous since words are typically embedded in continuous spaces. This implies Lie groups (manifolds), Lie semigroups with an identity (also manifolds), and Lie algebras (vector spaces with a Lie bracket) are all plausible algebraic candidates." + ], + [ + "We trained word embeddings and a uni-directional GRU connected to a dense layer end-to-end for text classification on a set of scraped tweets using cross-entropy as the loss function. End-to-end training was selected to impose as few heuristic constraints on the system as possible. Each tweet was tokenized using NLTK TweetTokenizer and classified as one of 10 potential accounts from which it may have originated. The accounts were chosen based on the distinct topics each is known to typically tweet about. Tokens that occurred fewer than 5 times were disregarded in the model. The model was trained on 22106 tweets over 10 epochs, while 5526 were reserved for validation and testing sets (2763 each). The network demonstrated an insensitivity to the initialization of the hidden state, so, for algebraic considerations, INLINEFORM0 was chosen for hidden dimension of INLINEFORM1 . A graph of the network is shown in Fig.( FIGREF13 ).", + "Algebraic structures typically exhibit some relationship between the dimension of the structure and the dimension of admissible representations, so exploring the embedding and hidden dimensions for which certain algebraic properties hold is of interest. Additionally, beyond the present interest in algebraic properties, the network's insensitivity to the hidden dimension invites an investigation into its sensitivity to the word embedding dimension. To address both points of interest, we extend the hyperparameter search of BIBREF5 , and perform a comparative search over embedding dimensions and hidden dimensions to determine the impact of each on the network's performance and algebraic properties. Each dimension in the hyperparameter pair, INLINEFORM0 , runs from 20 to 280 by increments of 20.", + "After training the network for each hyperparameter pair, the GRU model parameters and embedding matrix were frozen to begin testing for emergent algebraic structure. To satisfy the common \u201c INLINEFORM0 \" requirement stated in \u00a7 SECREF6 , real hidden states encountered in the testing data were saved to be randomly sampled when testing the actions of the GRU on states. 7 tests were conducted for each hyperparameter pair with randomly selected states:", + "Identity (\u201carbitrary identity\")", + "Inverse of all words in corpus (\u201carbitrary inverse\")", + "Closure under multiplication of arbitrary pairs of words in total corpus (\u201carbitrary closure\")", + "Closure under commutation of arbitrary pairs of words in total corpus (\u201carbitrary commutativity\")", + "Closure under multiplication of random pairs of words from within each tweet (\u201cintra-sentence closure\")", + "Closure of composition of long sequences of words in each tweet (\u201ccomposite closure\")", + "Inverse of composition of long sequences of words in each tweet (\u201ccomposite inverse\")", + "Tests 6 and 7 were performed since, if closure is upheld, the composition of multiple words must also be upheld. These tests were done to ensure mathematical consistency.", + "To test for the existence of \u201cwords\" that satisfy these conditions, vectors were searched for that, when inserted into the GRU, minimized the ratio of the Euclidean norms of the difference between the \u201csearched\" hidden vector and the correct hidden vector. For concreteness, the loss function for each algebraic property from \u00a7 SECREF6 were defined as follows:", + "(Identity) DISPLAYFORM0 ", + "(Closure under multiplication) DISPLAYFORM0 ", + "(Inverse) DISPLAYFORM0 ", + "(Closure under Lie bracket) DISPLAYFORM0 ", + "where INLINEFORM0 are random, learned word vectors, INLINEFORM1 is a hidden state, and INLINEFORM2 is the model parameter trained to minimize the loss. We refer to Eqs.( SECREF12 ) as the \u201caxiomatic losses.\" It is worth noting that the non-zero hidden state initialization was chosen to prevent the denominators from vanishing when the initial state is selected as a candidate INLINEFORM3 in Eqs.( EQREF22 )&( EQREF26 ). The reported losses below are the average across all INLINEFORM4 's and INLINEFORM5 's that were examined. Optimization over the losses in Eqs.( SECREF12 ) was performed over 5000 epochs. For the associated condition to be satisfied, there must exist a word vector INLINEFORM6 that sufficiently minimizes the axiomatic losses.", + "If it is indeed the case that the GRU attempts to learn a representation of an algebraic structure and each neuron serves as a basis function, it is not necessary that each neuron individually satisfies the above constraints. For clarity, recall the second motivating point that the addition of neurons, once a representation is found, simply contributes to learning the representation better. Instead, only a linear combination of the neurons must. We consider this possibility for the most task-performant hyperparameter pair, and two other capricious pairs. The target dimension of the linear combination, INLINEFORM0 , which we refer to as the \u201clatent dimension,\" could generally be smaller than the hidden dimension, INLINEFORM1 . To compute the linear combination of the neurons, the outputs of the GRU were right-multiplied by a INLINEFORM2 matrix, INLINEFORM3 : DISPLAYFORM0 ", + " Since the linear combination is not \u00e0 priori known, INLINEFORM0 is treated as a model parameter.", + "The minimization task previously described was repeated with this combinatorial modification while scanning over latent dimensions, INLINEFORM0 , in steps of 20. The test was performed 10 times and the reported results averaged for each value of INLINEFORM1 to reduce fluctuations in the loss from differing local minima. INLINEFORM2 was trained to optimize various combinations of the algebraic axioms, the results of which were largely found to be redundant. In \u00a7 SECREF4 , we address the case in which INLINEFORM3 was only trained to assist in optimizing a single condition, and frozen in other axiomatic tests; the commutative closure condition, however, was given a separate linear combination matrix for reasons that will be discussed later.", + "Finally, the geometric structure of the resulting word vectors was explored, naively using the Euclidean metric. Sentences trace out (discrete) paths in the word embedding space, so it was natural to consider relationships between both word vectors and vectors \u201ctangent\" to the sentences' paths. Explicitly, the angles and distances between", + "random pairs of words", + "all words and the global average word vector", + "random pairs of co-occurring words", + "all words with a co-occurring word vector average", + "adjacent tangent vectors", + "tangent vectors with a co-occurring tangent vector average", + "were computed to determine how word vectors are geometrically distributed. Intuitively, similar words are expected to affect hidden states similarly. To test this, and to gain insight into possible algebraic interpretations of word embeddings, the ratio of the Euclidean norm of the difference between hidden states produced by acting on a hidden state with two different words to the Euclidean norm of the original hidden state was computed as a function of the popular cosine similarity metric and distance between embeddings. This fractional difference, cosine similarity, and word distance were computed as, DISPLAYFORM0 ", + " where Einstein summation is applied to the (contravariant) vector indices.", + "High-level descriptions of the methods will be briefly revisited in each subsection of \u00a7 SECREF4 so that they are more self-contained and pedagogical." + ], + [ + "We performed hyperparameter tuning over the word embedding dimension and the GRU hidden dimension to optimize the classifier's accuracy. Each dimension ran from 20 to 280 in increments of 20. A contour plot of the hyperparameter search is shown in Fig.( FIGREF39 ).", + "For comparison, using pretrained, 50 dimensional GloVe vectors with this network architecture typically yielded accuracies on the order of INLINEFORM0 on this data set, even for more performant hidden dimensions. Thus, training the embeddings end-to-end is clearly advantageous for short text classification. It is worth noting that training them end-to-end is viable primarily because of the short length of tweets; for longer documents, exploding/vanishing gradients typically prohibits such training.", + "The average Fisher information of each hyperparameter dimension over the searched region was computed to determine the relative sensitivities of the model to the hyperparameters. The Fisher information for the hidden dimension was INLINEFORM0 ; the Fisher information for the embedding dimension was INLINEFORM1 . Evidently, by this metric, the model was, on average in this region of parameter space, 1.76 times more sensitive to the hidden dimension than the embedding dimension. Nevertheless, a larger word embedding dimension was critical for the network to realize its full potential.", + "The model performance generally behaved as expected across the hyperparameter search. Indeed, higher embedding and hidden dimensions tended to yield better results. Given time and resource constraints, the results are not averaged over many search attempts. Consequently, it is unclear if the pockets of entropy are indicative of anything deeper, or merely incidental fluctuations. It would be worthwhile to revisit this search in future work." + ], + [ + "Seven tests were conducted for each hyperparameter pair to explore any emergent algebraic structure the GRU and word embeddings may exhibit. Specifically, the tests searched for 1) the existence of an identity element, 2) existence of an inverse word for each word, 3) multiplicative closure for arbitrary pairs of words, 4) commutative closure for arbitrary pairs of words, 5) multiplicative closure of pairs of words that co-occur within a tweet, 6) multiplicative closure of all sequences of words that appear in tweets, and 7) the existence of an inverse for all sequences of words that appear in tweets. The tests optimized the axiomatic losses defined in Eqs.( SECREF12 ).", + "In what follows, we have chosen INLINEFORM0 (or, INLINEFORM1 error) as the criterion by which we declare a condition \u201csatisfied.\"", + "The tests can be broken roughly into two classes: 1) arbitrary solitary words and pairs of words, and 2) pairs and sequences of words co-occurring within a tweet. The results for class 1 are shown in Fig.( FIGREF41 ); the results for class 2 are shown in Fig.( FIGREF42 ).", + "The identity condition was clearly satisfied for virtually all embedding and hidden dimensions, with possible exceptions for small embedding dimensions and large hidden dimensions. Although we did not explicitly check, it is likely that even the possible exceptions would be viable in the linear combination search.", + "Arbitrary pairs of words were evidently not closed under multiplication without performing a linear combination search, with a minimum error of INLINEFORM0 across all dimensions. Moreover, the large entropy across the search does not suggest any fundamentally interesting or notable behavior, or any connections between the embedding dimension, hidden dimension, and closure property.", + "Arbitrary pairs of words were very badly not closed under commutation, and it is unfathomable that even a linear combination search could rescue the property. One might consider the possibility that specific pairs of words might have still closed under commutation, and that the exceptional error was due to a handful of words that commute outright since this would push the loss up with a near-vanishing denominator. As previously stated, the hidden states were not initialized to be zero states, and separate experiments confirm that the zero state was not in the orbit of any non-zero state, so there would have been no hope to negate the vanishing denominator. Thus, this concern is in principle possible. However, explicitly removing examples with exploding denominators (norm INLINEFORM0 ) from the loss when performing linear combination searches still resulted in unacceptable errors ( INLINEFORM1 ), so this possibility is not actually realized. We did not explicitly check for this closure in class 2 tests since class 2 is a subset of class 1, and such a flagrant violation of the condition would not be possible if successful closure in class 2 were averaged into class 1 results. Even though commutative closure is not satisfied, it is curious to note that the error exhibited a mostly well-behaved stratification.", + "The most interesting class 1 result was the arbitrary inverse. For embedding dimensions sufficiently large compared to the hidden dimension, inverses clearly existed even without a linear combination search. Even more remarkable was the well-behaved stratification of the axiomatic error, implying a very clear relationship between the embedding dimension, hidden dimension, and emergent algebraic structure of the model. It is not unreasonable to expect the inverse condition to be trivially satisfied in a linear combination search for a broad range of hyperparameter pairs.", + "The same behavior of the inverse property is immediately apparent in all class 2 results. The stratification of the error was virtually identical, and all of the tested properties have acceptable errors for sufficiently large embedding dimensions for given hidden dimensions, even without a linear combination search." + ], + [ + "The optimal hyperparameter pair for this single pass of tuning was INLINEFORM0 , which resulted in a model accuracy of INLINEFORM1 . This was not a statistically significant result since multiple searches were not averaged, so random variations in validation sets and optimization running to differing local minima may have lead to fluctuations in the test accuracies. However, the selection provided a reasonable injection point to investigate the algebraic properties of linear combinations of the output of the GRU's neurons. For comparison, we also considered INLINEFORM2 and INLINEFORM3 .", + "The tests were run with the linear combination matrix, INLINEFORM0 , trained to assist in optimizing the composite inverse. The learned INLINEFORM1 was then applied to the output hidden states for the other properties except for commutative closure, which was given its own linear combination matrix to determine if any existed that would render it an emergent property.", + "The combination was trained to optimize a single condition because, if there exists an optimal linear combination for one condition, and there indeed exists an underlying algebraic structure incorporating other conditions, the linear combination would be optimal for all other conditions.", + "Initial results for the INLINEFORM0 search is shown in Figs.( FIGREF45 )&( FIGREF46 ). Well-optimized properties are shown in Fig.( FIGREF45 ), while the expected poorly-optimized properties are shown in Fig.( FIGREF46 ).", + "The four conditions examined in Fig.( FIGREF45 ) are clearly satisfied for all latent dimensions. They all also reach a minimum error in the same region. Composite closure, intra-sentence closure, and arbitrary inverse are all optimized for INLINEFORM0 ; composite inverse is optimized for INLINEFORM1 , though the variation in the range INLINEFORM2 is small ( INLINEFORM3 variation around the mean, or an absolute variation of INLINEFORM4 in the error).", + "Arbitrary multiplicative closure and commutative closure are highly anti-correlated, and both conditions are badly violated. It is worth noting that the results in Fig.( FIGREF46 )(b) did not remove commutative pairs of words from the error, and yet the scale of the error in the linear combination search is virtually identical to what was separately observed with the commutative pairs removed. They both also exhibit a monotonic dependence on the latent dimension. Despite their violation, this dependence is well-behaved, and potentially indicative of some other structure.", + "Before discussing the linear combination searches for the other selected hyperparameter pairs, it is worthwhile noting that retraining the network and performing the linear combination search again can yield differing results. Figs.( FIGREF47 )&( FIGREF48 ) show the linear combination results after retraining the model for the same hyperparameter pair, with a different network performance of INLINEFORM0 .", + "Qualitatively, the results are mostly the same: there is a common minimizing region of INLINEFORM0 , and conditions are satisfied, at least in the common minimal region. However, the minimizing region starkly shifted down, and became sharper for composite closure, intra-sentence closure, and arbitrary inverse.", + "Once more, the results are mostly the same. Arbitrary closure error drastically increased, but both are still highly anti-correlated, and mostly monotonic, despite the erratic fluctuations in the arbitrary closure error.", + "Figs.( FIGREF49 )&( FIGREF50 ) show the linear combination search for INLINEFORM0 . The model was retrained, and achieved INLINEFORM1 for the displayed results.", + "Interestingly, the optimal latent dimension occurs significantly higher than for the other reported hyperparameter pairs. This result, however, is not true for all retrainings at this INLINEFORM0 pair.", + "The entropy in the arbitrary closure loss increased, and the commutative closure loss seemed to asymptote at higher latent dimension.", + "Figs.( FIGREF51 )&( FIGREF52 ) show the linear combination search for INLINEFORM0 . The model was retrained, and achieved INLINEFORM1 for the displayed results.", + "At lower dimensions, the optimal latent dimension was no longer shared between the satisfied conditions.", + "The unsatisfied conditions displayed mostly the same behavior at lower dimensions." + ], + [ + "To explore the geometric distribution of word vectors, the angles and distances between 1) random pairs of words, 2) all words and the global average word vector, 3) random pairs of co-occurring words, 4) all words with a co-occurring word vector average, 5) adjacent tangent vectors, 6) tangent vectors with a co-occurring tangent vector average were computed. The magnitudes of the average word vectors, average co-occurring word vectors, and average tangent vectors were also computed.", + "Additionally, the relative effect of words on states is computed verses their cosine similarities and relative distances, measured by Eqs.( EQREF37 )-().", + "In the figures that follow, there are, generally, three categories of word vectors explored: 1) random word vectors from the pool of all word vectors, 2) co-occurring word vectors, and 3) tangent vectors (the difference vector between adjacent words).", + "Fig.( FIGREF54 ) shows the distribution in the Euclidean norms of the average vectors that were investigated.", + "The tangent vectors and average word vectors had comparable norms. The non-zero value of the average word vector indicates that words do not perfectly distribute throughout space. The non-zero value of the average tangent vectors indicates that tweets in general progress in a preferred direction relative to the origin in embedding space; albeit, since the magnitudes are the smallest of the categories investigated, the preference is only slight. The norm of the average of co-occurring word vectors is significantly larger than the norms of others categories of vectors, indicating that the words in tweets typically occupy a more strongly preferred region of embedding space (e.g. in a cone, thus preventing component-wise cancellations when computing the average).", + "Fig.( FIGREF55 ) shows the distribution of the Euclidean cosine similarities of both pairs of vectors and vectors relative to the categorical averages.", + "The cosine similarity of pairs of random words and co-occurring words shared a very common distribution, albeit with the notable spikes are specific angles and a prominent spike at INLINEFORM0 for co-occurring pairs. The prominent spike could potentially be explained by the re-occurrence of punctuation within tweets, so it may not indicate anything of importance; the potential origin of the smaller spikes throughout the co-occurring distribution is unclear. Generally, the pairs strongly preferred to be orthogonal, which is unsurprising given recent investigations into the efficacy of orthogonal embeddings BIBREF37 . Adjacent pairs of tangent vectors, however, exhibited a very strong preference for obtuse relative angles, with a spike at INLINEFORM1 .", + "Words tended to have at most a very slightly positive cosine similarity to the global average, which is again indicative of the fact words did not spread out uniformly. Co-occurring words tended to form acute angles with respect to the co-occurring average. Meanwhile, tangent vectors strongly preferred to be orthogonal to the average.", + "The strong negative cosine similarity of adjacent tangent vectors, and the strong positive cosine similarity of words with their co-occurring average, indicate co-occurring words tended to form a grid structure in a cone. That is, adjacent words tended to be perpendicular to each other in the positive span of some set of word basis vectors. Of course, this was not strictly adhered to, but the preferred geometry is apparent.", + "Fig.( FIGREF56 ) shows the distribution of the Euclidean distances of both pairs of vectors and vectors relative to the categorical averages.", + "Distributions of random pairs of words and co-occurring words were virtually identical in both plots, indicating that most of the variation is attributable to the relative orientations of the vectors rather than the distances between them.", + "Fig.( FIGREF57 ) shows the correlation of the similarity of the action of pairs of words to their cosine similarity and distances apart.", + "Both plots confirm that the more similar words are, the more similar their actions on the hidden states are. The strongly linear, bi-modal dependence of the fractional difference on the distance between words indicates that word distance is a stronger predictor of the relative meaning of words than the popular cosine similarity." + ], + [ + "The important take-aways from the results are:", + "The GRU trivially learned an identity `word'.", + "The action of the GRU for any individual word admits an inverse for sufficiently large embedding dimension relative to the hidden dimension.", + "The successive action of the GRU for any arbitrary pair of words is not, generally, equivalent to the action of the GRU for any equivalent third `word'.", + "The commutation of successive actions of the GRU for any arbitrary pair of words is not equivalent to the action of the GRU for any equivalent third `word'.", + "The successive action of the GRU for any co-occurring pair of words is equivalent to the action of the GRU for an equivalent third `word' for sufficiently large embedding dimension relative to the hidden dimension.", + "The successive action of the GRU for any series of co-occuring words is equivalent to the action of the GRU for an equivalent `word' for sufficiently large embedding dimension relative to the hidden dimension.", + "The action of the GRU for any series of co-occurring words admits an inverse for sufficiently large embedding dimension relative to the hidden dimension.", + "Any condition satisfied for a sufficiently large embedding dimension relative to the hidden dimension is true for any pair of dimensions given an appropriate linear combination of the outputs of the GRU projected into an appropriate lower dimension (latent dimension).", + "The axiomatic errors for all satisfied conditions for the most performant models are minimized for specific, shared latent dimensions, and increases away from these latent dimensions; the optimal latent dimension is not shared for sufficiently small embedding dimensions.", + "Models with lower test performance tend to optimally satisfy these conditions for lower latent dimensions.", + "Co-occurring word vectors tend to be perpendicular to each other and occupy a cone in embedding space.", + "The difference of the action of two word vectors on a hidden state increases linearly with the distance between the two words, and follows a generally bi-modal trend.", + "Although there are still several outstanding points to consider, we offer an attempt to interpret these results in this section.", + "Identity, inverse, and closure properties for co-occurring words are satisfied, and in such a way that they are all related under some algebraic structure. Since closure is not satisfied for arbitrary pairs of words, there are, essentially, two possible explanations for the observed structure:", + "The union of all sets of co-occurring words is the Cartesian product of multiple Lie groups: DISPLAYFORM0 ", + "where INLINEFORM0 is the space of words, and INLINEFORM1 is a Lie group. Since multiplication between groups is not defined, the closure of arbitrary pairs of words is unsatisfied.", + "The GRU's inability to properly close pairs of words it has never encountered together is the result of the generalization problem, and all words consequently embed in a larger Lie group: DISPLAYFORM0 ", + "In either case, words can be considered elements of a Lie group. Since Lie groups are also manifolds, the word vector components can be interpreted as coordinates on this Lie group. Traditionally, Lie groups are practically handled by considering the Lie algebra that generates them, INLINEFORM0 . The components of the Lie vectors in INLINEFORM1 are then typically taken to be the coordinates on the Lie group. This hints at a connection between INLINEFORM2 and the word vectors, but this connection was not made clear by any of the experiments. Furthermore, RNNs learn a nonlinear representation of the group on some latent space spanned by the hidden layer.", + "Since sentences form paths on the embedding group, it's reasonable to attempt to form a more precise interpretation of the action of RNNs. We begin by considering their explicit action on hidden states as the path is traversed: DISPLAYFORM0 ", + " Eq.() takes the form of a difference equation. In particular, it looks very similar to the finite form of the differential equation governing the nonlinear parallel transport along a path, INLINEFORM0 , on a principal fibre bundle with base space INLINEFORM1 and group INLINEFORM2 . If the tangent vector at INLINEFORM3 is INLINEFORM4 , and the vector being transported at INLINEFORM5 is INLINEFORM6 then we have DISPLAYFORM0 ", + " where INLINEFORM0 is the (nonlinear) connection at INLINEFORM1 . If INLINEFORM2 were explicitly a function of INLINEFORM3 , Eq.( EQREF76 ) would take a more familiar form: DISPLAYFORM0 ", + " Given the striking resemblance between Eqs.( EQREF77 )&(), is it natural to consider either", + "The word embedding group serving as the base space, INLINEFORM0 , so that the path INLINEFORM1 corresponds explicitly to the sentence path.", + "A word field on the base space, INLINEFORM0 , so that there exists a mapping between INLINEFORM1 and the sentence path.", + "The second option is more general, but requires both a candidate for INLINEFORM0 and a compelling way to connect INLINEFORM1 and INLINEFORM2 . This is also more challenging, since, generally, parallel transport operators, while taking values in the group, are not closed. If the path were on INLINEFORM3 itself, closure would be guaranteed, since any parallel transport operator would be an element of the co-occurring subgroup, and closure arises from an equivalence class of paths.", + "To recapitulate the final interpretations of word embeddings and RNNs in NLP:", + "Words naturally embed as elements in a Lie group, INLINEFORM0 , and end-to-end word vectors may be related to the generating Lie algebra.", + "RNNs learn to parallel transport nonlinear representations of INLINEFORM0 either on the Lie group itself, or on a principal INLINEFORM1 -bundle." + ], + [ + "The geometric derivative along a path parameterized by INLINEFORM0 is defined as: DISPLAYFORM0 ", + "where INLINEFORM0 is the tangent vector at INLINEFORM1 , and INLINEFORM2 is the connection. This implies RNNs learn the solution of the first-order geometric differential equation: DISPLAYFORM0 ", + "It is natural, then, to consider neural network solutions to higher-order generalizations: DISPLAYFORM0 ", + "Networks that solve Eq.( EQREF85 ) are recurrent-like. Updates to a hidden state will generally depend on states beyond the immediately preceding one; often, this dependence can be captured by evolving on the phase space of the hidden states, rather than on the sequences of the hidden states themselves. The latter results in a nested RNN structure for the recurrent-like cell, similar to the structure proposed in BIBREF12 .", + "Applications of Eq.( EQREF85 ) are currently being explored. In particular, if no additional structure exists and RNNs parallel transport states along paths on the word embedding group itself (the first RNN interpretation), geodesics emerge as a natural candidate for sentence paths to lie on. Thus, sentence generation could potentially be modeled using the geodesic equation and a nonlinear adjoint representation: INLINEFORM0 , INLINEFORM1 in Eq.( EQREF85 ). This geodesic neural network (GeoNN) is the topic of a manuscript presently in preparation." + ], + [ + "The embeddings trained end-to-end in this work provided highly performant results. Unfortunately, training embeddings on end-tasks with longer documents is challenging, and the resulting embeddings are often poor for rare words. However, it would seem constructing pre-trained word embeddings by leveraging the emergent Lie group structure observed herein could provide competitive results without the need for end-to-end training.", + "Intuitively, it is unsurprising groups appear as a candidate to construct word embeddings. Evidently, the proximity of words is governed by their actions on hidden states, and groups are often the natural language to describe actions on vectors. Since groups are generally non-commutative, embedding words in a Lie group can additionally capture their order- and context-dependence. Lie groups are also generated by Lie algebras, so one group can act on the algebra of another group, and recursively form a hierarchical tower. Such an arrangement can explicitly capture the hierarchical structure language is expected to exhibit. E.g., the group structure in the first interpretation given by Eq.( EQREF72 ), DISPLAYFORM0 ", + "admits, for appropriately selected INLINEFORM0 , hierarchical representations of the form DISPLAYFORM0 ", + " where INLINEFORM0 . Such embedding schemes have the potential to generalize current attempts at capturing hierarchy, such as Poincar\u00e9 embeddings BIBREF22 . Indeed, hyperbolic geometries, such as the Poincar\u00e9 ball, owe their structure to their isometry groups. Indeed, it is well known that the hyperbolic INLINEFORM1 dimensional Minkowski space arises as a representation of INLINEFORM2 + translation symmetries.", + "In practice, Lie group embedding schemes would involve representing words as constrained matrices and optimizing the elements, subject to the constraints, according to a loss function constructed from invariants of the matrices, and then applying the matrix log to obtain Lie vectors. A prototypical implementation, dubbed \u201cLieGr,\" in which the words are assumed to be in the fundamental representation of the special orthogonal group, INLINEFORM0 , and are conditioned on losses sensitive to the relative actions of words, is the subject of another manuscript presently in preparation." + ], + [ + "The results presented herein offer insight into how RNNs and word embeddings naturally tend to structure themselves for text classification. Beyond elucidating the inner machinations of deep NLP, such results can be used to help construct novel network architectures and embeddings.", + "There is, however, much immediate followup work worth pursuing. In particular, the uniqueness of identities, inverses, and multiplicative closure was not addressed in this work, which is critical to better understand the observed emergent algebraic structure. The cause for the hyperparameter stratification of the error in, and a more complete exploration of, commutative closure remains outstanding. Additionally, the cause of the breakdown of the common optimal latent dimension for low embedding dimension is unclear, and the bi-model, linear relationship between the action of words on hidden states and the Euclidean distance between end-to-end word embeddings invites much investigation.", + "As a less critical, but still curious inquiry: is the additive relationship between words, e.g. \u201cking - man + woman = queen,\" preserved, or is it replaced by something new? In light of the Lie group structure words trained on end tasks seem to exhibit, it would not be surprising if a new relationship, such as the Baker-Campbell-Hausdorff formula, applied." + ], + [ + "The author would like to thank Robin Tully, Dr. John H. Cantrell, and Mark Laczin for providing useful discussions, of both linguistic and mathematical natures, as the work unfolded. Robin in particular provided essential feedback throughout the work, and helped explore the potential use of free groups in computational linguistics at the outset. John furnished many essential conversations that ensured the scientific and mathematical consistency of the experiments, and provided useful insights into the results. Mark prompted the investigation into potential emergent monoid structures since they appear frequently in state machines." + ] + ] +} +``` \ No newline at end of file diff --git a/qasper-1195/instruction.md b/qasper-1195/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..065cfb296099e7249397713d3c47bcee196c4ef3 --- /dev/null +++ b/qasper-1195/instruction.md @@ -0,0 +1,3 @@ +Name of Paper: Single headed attention based sequence-to-sequence model for state-of-the-art results on Switchboard-300 + +Question: How much bigger is Switchboard-2000 than Switchboard-300 database? \ No newline at end of file diff --git a/qasper-1211/instruction.md b/qasper-1211/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..a579dc8c482b79d811028a95cf5bbb472710dfe5 --- /dev/null +++ b/qasper-1211/instruction.md @@ -0,0 +1,186 @@ +Name of Paper: Ask the Right Questions: Active Question Reformulation with Reinforcement Learning + +Question: how are multiple answers from multiple reformulated questions aggregated? + +## Full Paper Text (JSON) + +```json +{ + "section_name": [ + "Introduction", + "Related work", + "Active Question Answering Model", + "Question-Answering Environment", + "Reformulation Model", + "Answer Selection Model", + "Question Answering Environment", + "Policy Gradient Training of the Reformulation Model", + "Answer Selection", + "Pretraining of the Reformulation Model", + "Question Answering Data and BiDAF training", + "Question Reformulator Training", + "Training the Answer Selector", + "Baselines and Benchmarks", + "Results", + "Analysis of the agent's language", + "General properties", + "Paraphrasing quality", + "Discussion", + "Conclusion", + "Acknowledgements", + "Reformulation Examples" + ], + "paragraphs": [ + [ + "Web and social media have become primary sources of information. Users' expectations and information seeking activities co-evolve with the increasing sophistication of these resources. Beyond navigation, document retrieval, and simple factual question answering, users seek direct answers to complex and compositional questions. Such search sessions may require multiple iterations, critical assessment, and synthesis BIBREF0 .", + "The productivity of natural language yields a myriad of ways to formulate a question BIBREF1 . In the face of complex information needs, humans overcome uncertainty by reformulating questions, issuing multiple searches, and aggregating responses. Inspired by humans' ability to ask the right questions, we present an agent that learns to carry out this process for the user. The agent sits between the user and a backend QA system that we refer to as `the environment'. We call the agent AQA, as it implements an active question answering strategy. AQA aims to maximize the chance of getting the correct answer by sending a reformulated question to the environment. The agent seeks to find the best answer by asking many questions and aggregating the returned evidence. The internals of the environment are not available to the agent, so it must learn to probe a black-box optimally using only question strings. The key component of the AQA agent is a sequence-to-sequence model trained with reinforcement learning (RL) using a reward based on the answer returned by the environment. The second component to AQA combines the evidence from interacting with the environment using a convolutional neural network to select an answer.", + "We evaluate on a dataset of Jeopardy! questions, SearchQA BIBREF2 . These questions are hard to answer by design because they use convoluted language, e.g., Travel doesn't seem to be an issue for this sorcerer & onetime surgeon; astral projection & teleportation are no prob (answer: Doctor Strange). Thus SearchQA tests the ability of AQA to reformulate questions such that the QA system has the best chance of returning the correct answer. AQA improves over the performance of a deep network built for QA, BiDAF BIBREF3 , which has produced state-of-the-art results on multiple tasks, by 11.4% absolute F1, a 32% relative F1 improvement. Additionally, AQA outperforms other competitive heuristic query reformulation benchmarks.", + "AQA defines an instance of machine-machine communication. One side of the conversation, the AQA agent, is trying to adapt its language to improve the response from the other side, the QA environment. To shed some light on this process we perform a qualitative analysis of the language generated by the AQA agent. By evaluating on MSCOCO BIBREF4 , we find that the agent's question reformulations diverge significantly from natural language paraphrases. Remarkably, though, the agent is able to learn non-trivial and transparent policies. In particular, the agent is able to discover classic IR query operations such as term re-weighting, resembling tf-idf, and morphological simplification/stemming. A possible reason being that current machine comprehension tasks involve the ranking of short textual snippets, thus incentivizing relevance, more than deep language understanding." + ], + [ + " BIBREF5 learned patterns of question variants by comparing dependency parsing trees. BIBREF6 showed that MT-based paraphrases can be useful in principle by providing significant headroom in oracle-based estimations of QA performance. Recently, BIBREF7 used paraphrasing to augment the training of a semantic parser by expanding through the paraphrases as a latent representation. Bilingual corpora and MT have been used to generate paraphrases by pivoting through a second language. Recent work uses neural translation models and multiple pivots BIBREF8 . In contrast, our approach does not use pivoting and is, to our knowledge, the first direct neural paraphrasing system. BIBREF9 propose phrase-based paraphrasing for query expansion. In contrast with this line of work, our goal is to generate full question reformulations while optimizing directly the end-to-end target performance metrics.", + "Reinforcement learning is gaining traction in natural language understanding across many problems. For example, BIBREF10 use RL to learn control policies for multi-user dungeon games where the state of the game is summarized by a textual description, and BIBREF11 use RL for dialogue generation. Policy gradient methods have been investigated recently for MT and other sequence-to-sequence problems. They alleviate limitations inherent to the word-level optimization of the cross-entropy loss, allowing the use of sequence-level reward functions, like BLEU. Reward functions based on language models and reconstruction errors are used to bootstrap MT with fewer resources BIBREF12 . RL training can also prevent exposure bias; an inconsistency between training and inference time stemming from the fact that the model never sees its own mistakes during training BIBREF13 . We also use policy gradient to optimize our agent, however, we use end-to-end question answering quality as the reward.", + "Uses of policy gradient for QA include BIBREF14 , who train a semantic parser to query a knowledge base, and BIBREF15 who propose query reduction networks that transform a query to answer questions that involve multi-hop common sense reasoning. The work of BIBREF16 is most related to ours. They identify a document containing an answer to a question by following links on a graph. Evaluating on a set of questions from the game Jeopardy!, they learn to walk the Wikipedia graph until they reach the predicted answer. In a follow-up, BIBREF17 improve document retrieval with an approach inspired by relevance feedback in combination with RL. They reformulate a query by adding terms from documents retrieved from a search engine for the original query. Our work differs in that we generate complete sequence reformulations rather than adding single terms, and we target question-answering rather than document retrieval.", + "Active QA is also related to recent research on fact-checking: BIBREF18 propose to perturb database queries in order to estimate the support of quantitative claims. In Active QA questions are perturbed semantically with a similar purpose, although directly at the surface natural language form." + ], + [ + "Figure 1 shows the Active Question Answering (AQA) agent-environment setup. The AQA model interacts with a black-box environment. AQA queries it with many versions of a question, and finally returns the best of the answers found. An episode starts with an original question $q_0$ . The agent then generates a set of reformulations $\\lbrace q_i\\rbrace _{i=1}^N$ . These are sent to the environment which returns answers $\\lbrace a_i\\rbrace _{i=1}^N$ . The selection model then picks the best from these candidates." + ], + [ + "For the QA environment, we use a competitive neural question answering model, BiDirectional Attention Flow (BiDAF) BIBREF3 . BiDAF is an extractive QA system, it selects answers from contiguous spans of a given document. Given a question, the environment returns an answer and, during training, a reward. The reward may be any quality metric for the returned answer, we use token-level F1 score. Note that the reward for each answer $a_i$ is computed against the original question $q_0$ . We assume that the environment is opaque; the agent has no access to its parameters, activations or gradients. This setting enables one, in principle, to also interact with other information sources, possibly providing feedback in different modes such as images and structured data from knowledge bases. However, without propagating gradients through the environment we lose information, feedback on the quality of the question reformulations is noisy, presenting a challenge for training." + ], + [ + "The reformulator is a sequence-to-sequence model, as is popular for neural machine translation. We build upon the implementation of BIBREF19 . The major departure from the standard MT setting is that our model reformulates utterances in the same language. Unlike in MT, there is little high quality training data available for monolingual paraphrasing. Effective training of highly parametrized neural networks relies on an abundance of data. We address this challenge by first pre-training on a related task, multilingual translation, and then using signals produced during the interaction with the environment for adaptation." + ], + [ + "During training, we have access to the reward for the answer returned for each reformulation $q_i$ . However, at test time we must predict the best answer $a^*$ . The selection model selects the best answer from the set $\\lbrace a_i\\rbrace _{i=1}^N$ observed during the interaction by predicting the difference of the F1 score to the average F1 of all variants. We use pre-trained embeddings for the tokens of query, rewrite, and answer. For each, we add a 1-dimensional CNN followed by max-pooling. The three resulting vectors are then concatenated and passed through a feed-forward network which produces the output." + ], + [ + "We train a model on the training set for the QA task at hand, see Section \"Baselines and Benchmarks\" for details. Afterwards, BiDAF becomes the black-box environment and its parameters are not updated further. In principle, we could train both the agent and the environment jointly to further improve performance. However, this is not our desired task: our aim is for the agent to learn to communicate using natural language with an environment over which is has no control." + ], + [ + "For a given question $q_0$ , we want to return the best possible answer $a^*$ , maximizing a reward $a^*=\\operatorname{argmax}_a R(a|q_0)$ . Typically, ${R}$ is the token level F1 score on the answer. The answer $a = f(q)$ is an unknown function of a question $q$ , computed by the environment. The reward is computed with respect to the original question $q_0$ while the answer is provided for $q$ . The question is generated according to a policy $\\pi _\\theta $ where $\\theta $ are the policy's parameters $a^*$0 . The policy, in this case, a sequence-to-sequence model, assigns a probability ", + "$$\\pi _\\theta (q|q_0) = \\prod _{t=1}^Tp(w_t|w_1,\\ldots ,w_{t-1},q_0)$$ (Eq. 7) ", + "to any possible question $q = w_1,\\ldots ,w_{T}$ , where $T$ is the length of $q$ with tokens $w_t \\in V$ from a fixed vocabulary $V$ . The goal is to maximize the expected reward of the answer returned under the policy, $\\mathbb {E}_{q\\sim \\pi _\\theta ({}\\cdot {}|q_0)}[{R}(f(q))]$ . We optimize the reward directly with respect to parameters of the policy using Policy Gradient methods BIBREF20 . The expected reward cannot be computed in closed form, so we compute an unbiased estimate with Monte Carlo sampling, ", + "$$\\mathbb {E}_{q\\sim \\pi _\\theta ({}\\cdot {}|q_0)}[{R}(f(q))]\n\\approx \\dfrac{1}{N} \\sum _{i=1}^N {R}(f(q_i)),\\quad q_i\\sim \\pi _\\theta ({}\\cdot {}|q_0)$$ (Eq. 8) ", + "To compute gradients for training we use REINFORCE BIBREF21 , ", + "$$\\nabla \\mathbb {E}_{q\\sim \\pi _\\theta ({}\\cdot {}|q_0)}[{R}(f(q))]\n&= \\mathbb {E}_{q\\sim \\pi _\\theta ({}\\cdot {}|q_0)}\\nabla _\\theta \\log (\\pi _\\theta (q|q_0))R(f(q))\\\\\n&\\approx \\dfrac{1}{N} \\sum _{i=1}^N\n\\nabla _\\theta \\log (\\pi (q_i|q_0))R(f(q_i)),\\quad q_i\\sim \\pi _\\theta ({}\\cdot {}|q_0)$$ (Eq. 9) ", + "This estimator is often found to have high variance, leading to unstable training BIBREF22 . We reduce the variance by subtracting the following baseline reward: $B(q_0)=\\mathbb {E}_{q\\sim \\pi _\\theta ({}\\cdot {}|q_0)}[R(f(q))]$ . This expectation is also computed by sampling from the policy given $q_0$ .", + "We often observed collapse onto a sub-optimal deterministic policy. To address this we use entropy regularization ", + "$$H[\\pi _{\\theta }(q|q_0)] = - \\sum _{t=1}^T \\sum _{w_t\\in V} p_{\\theta }(w_t|w_{