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+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. "
+    ]
+  ]
+}
+```

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?

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?

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?

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."
+    ]
+  ]
+}
+```

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?

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?

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%."
+    ]
+  ]
+}
+```

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?

qasper-0078/instruction.md

@@ -0,0 +1,3 @@
+Name of Paper: Generative Adversarial Nets for Multiple Text Corpora
+
+Question: Which GAN do they use?

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%)."
+    ]
+  ]
+}
+```

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%)."
+    ]
+  ]
+}
+```

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."
+    ]
+  ]
+}
+```

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?

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?

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?

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."
+    ]
+  ]
+}
+```

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?

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?

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?

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."
+    ]
+  ]
+}
+```

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?

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?

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?

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."
+    ]
+  ]
+}
+```

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."
+    ]
+  ]
+}
+```

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?

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?

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?

qasper-0217/instruction.md

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+Name of Paper: Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
+
+Question: What are state-of-the art models for this task?

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.",
+      ""
+    ]
+  ]
+}
+```

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?

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?

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."
+    ]
+  ]
+}
+```

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."
+    ]
+  ]
+}
+```

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?

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?

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?

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."
+    ]
+  ]
+}
+```

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?

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?

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?

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?

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?

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) "
+    ]
+  ]
+}
+```

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?

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?

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)."
+    ]
+  ]
+}
+```

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)."
+    ]
+  ]
+}
+```

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."
+    ]
+  ]
+}
+```