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null | false
| null |
Classify the following foods as Indian or Japanese: Sushi, Sashimi, Ramen, Naan, Chicken Tikka Masala, Samosa
|
Sushi: Japanese
Sashimi: Japanese
Ramen: Japanese
Naan: Indian
Chicken Tikka Masala: Indian
Samosa: Indian
|
|
null | false
| null |
What is a fixed-rate mortgage (FRM)?
|
A fixed-rate mortgage (FRM) is a mortgage loan where the interest rate on the note remains the same through the term of the loan, as opposed to loans where the interest rate may adjust or "float". As a result, payment amounts and the duration of the loan are fixed and the person who is responsible for paying back the loan benefits from a consistent, single payment and the ability to plan a budget based on this fixed cost.
|
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null | false
| null |
Black tie is a semi-formal Western dress code for evening events, originating in British and American conventions for attire in the 19th century. In British English, the dress code is often referred to synecdochically by its principal element for men, the dinner suit or dinner jacket. In American English, the equivalent term tuxedo (or tux) is common. The dinner suit is a black, midnight blue or white two- or three-piece suit, distinguished by satin or grosgrain jacket lapels and similar stripes along the outseam of the trousers. It is worn with a white dress shirt with standing or turndown collar and link cuffs, a black bow tie, typically an evening waistcoat or a cummerbund, and black patent leather dress shoes or court pumps. Accessories may include a semi-formal homburg, bowler, or boater hat. For women, an evening gown or other fashionable evening attire may be worn.
|
What is black tie dress code?
|
Black tie is a dress code for events, typically weddings and other formal ceremonies. The dress code roughly translates to a tuxedo or dinner suit for men and an evening gown for women.
|
null | false
| null |
What are options for materials to build a house?
|
Depending on your geographic location, different materials will be available to you. Typical construction materials include brick, cement, concrete, gravel, steel and lumber. In the United States, it is very common to see houses built with lumber since this material is readily available, however in other regions in the world such as Europe and Latin America, it is very common to see use of brick, concrete and steel for a typical house.
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null | false
| null |
Group these font types based on their style of typefaces: Times New Roman, Monaco, Garamond, Helvetica, Courier, Futura
|
These fonts can be categories as follow;
Serif fonts: Times New Roman, Garamond
San-serif fonts: Helvetica, Futura
Monospaced fonts: Monaco, Courier
|
|
null | false
| null |
Identify which instrument is string or woodwind: Cimboa, Sipsi
|
Sipsi is woodwind, Cimboa is string.
|
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null | false
| 162
|
Through the successive MP iterations, it could be argued that MPAD implicitly captures some soft notion of the hierarchical structure of documents (words $\rightarrow $ bigrams $\rightarrow $ compositions of bigrams, etc.). However, it might be beneficial to explicitly capture document hierarchy. Hierarchical architectures have brought significant improvements to many NLP tasks, such as language modeling and generation BIBREF24, BIBREF25, sentiment and topic classification BIBREF26, BIBREF27, and spoken language understanding BIBREF28, BIBREF29. Inspired by this line of research, we propose several hierarchical variants of MPAD, detailed in what follows. In all of them, we represent each sentence in the document as a word co-occurrence network, and obtain an embedding for it by applying MPAD as previously described.
MPAD-sentence-att. Here, the sentence embeddings are simply combined through self-attention.
MPAD-clique. In this variant, we build a complete graph where each node represents a sentence. We then feed that graph to MPAD, where the feature vectors of the nodes are initialized with the sentence embeddings previously obtained.
MPAD-path. This variant is similar to the clique one, except that instead of a complete graph, we build a path according to the natural flow of the text. That is, two nodes are linked by a directed edge if the two sentences they represent follow each other in the document.
Through the successive MP iterations, it could be argued that MPAD implicitly captures some soft notion of the hierarchical structure of documents (words t bigrams t compositions of bigrams, etc.). However, it might be beneficial to explicitly capture document hierarchy. Hierarchical architectures have brought significant improvements to many NLP tasks, such as language modeling and generation (Lin et al. 2015; Li, Luong, and Jurafsky 2015), sentiment and topic classification (Tang, Qin, and Liu 2015;Yang et al. 2016), and spoken language understanding (Ra- heja and Tetreault 2019; Shang et al. 2019). Inspired by this line of research, we propose several hierarchical variants of MPAD, detailed in what follows.
|
Why is the hierarchical structure of documents still needed for MPAD?
|
It is because capturing document hierarchy benefits the performance of many NLP tasks and they thought it may work for MPAD as well.
|
null | false
| null |
Jelly Roll is married to Bunnie DeFord aka Bunnie XO and has two children from a previous relationship.
|
By what other name does Bunnie DeFord go by?
|
Bunnie XO
|
null | false
| null |
Kartavya Path was called in the name of
|
Kingsway
|
|
1610.08597
| false
| null |
In our previous work BIBREF9 , we curated what may be the largest set of gang member profiles to study how gang member Twitter profiles can be automatically identified based on the content they share online. A data collection process involving location neutral keywords used by gang members, with an expanded search of their retweet, friends and follower networks, led to identifying 400 authentic gang member profiles on Twitter. Our study discovered that the text in their tweets and profile descriptions, their emoji use, their profile images, and music interests embodied by links to YouTube music videos, can help a classifier distinguish between gang and non-gang member profiles. While a very promising INLINEFORM0 measure with low false positive rate was achieved, we hypothesize that the diverse kinds and the multitude of features employed (e.g. unigrams of tweet text) could be amenable to an improved representation for classification. We thus explore the possibility of mapping these features into a considerably smaller feature space through the use of word embeddings.
A data collection process involving location neutral keywords used by gang members, with an expanded search of their retweet, friends and follower networks, led to identifying 400 authentic gang member profiles on Twitter. Our study discovered that the text in their tweets and profile descriptions, their emoji use, their profile images, and music interests embodied by links to YouTube music videos, can help a classifier distinguish between gang and non-gang member profiles.
|
How is the ground truth of gang membership established in this dataset?
|
The answers are shown as follows:
* text in their tweets and profile descriptions, their emoji use, their profile images, and music interests embodied by links to YouTube music videos, can help a classifier distinguish between gang and non-gang member profiles
|
null | false
| null |
State Route 49 (SR 49) is a 122.8-mile-long (197.6 km) state highway that travels southwest-to-northeast through portions of Terrell, Sumter, Macon, Peach, Houston, Bibb, Jones, and Baldwin counties, mainly in the central part of the U.S. state of Georgia. The highway connects SR 45 north of Dawson to SR 22/SR 24 in Milledgeville.
The segment from Fort Valley to Byron is part of the Fall Line Freeway, a highway that connects Columbia to Augusta. It may also be incorporated into the proposed eastern extension of Interstate 14 (I-14), which is currently entirely in Central Texas, and may be extended into Augusta.
|
How long is the Georgia State Route 49
|
The Georgia State Route 49 (SR 49) is a 122.8-mile-long (197.6 km) state highway that travels southwest-to-northeast through portions of Terrell, Sumter, Macon, Peach, Houston, Bibb, Jones, and Baldwin counties, mainly in the central part of the U.S. state of Georgia. The highway connects SR 45 north of Dawson to SR 22/SR 24 in Milledgeville.
The segment from Fort Valley to Byron is part of the Fall Line Freeway, a highway that connects Columbia to Augusta. It may also be incorporated into the proposed eastern extension of Interstate 14 (I-14), which is currently entirely in Central Texas, and may be extended into Augusta.
|
1801.02243
| false
| null |
To evaluate if the sentiment feature improves the prediction accuracy, a baseline model is defined. The baseline applies linear logistic regression to a set of stock technical signals to predict the following day’s stock return sign (+/‐). No sentiment features are provided to the baseline model.
The baseline applies linear logistic regression to a set of stock technical signals to predict the following day’s stock return sign (+/‐). No sentiment features are provided to the baseline model.
|
What is the baseline machine learning prediction approach?
|
The answers are shown as follows:
* linear logistic regression to a set of stock technical signals
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null | false
| 21
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Many reinforcement learning algorithms are designed for relatively small discrete or continuous action spaces and so have trouble scaling. Text-adventure games—or interaction fictions—are simulations in which both an agents' state and action spaces are in textual natural language. An example of a one turn agent interaction in the popular text-game Zork1 can be seen in Fig. FIGREF1. Text-adventure games provide us with multiple challenges in the form of partial observability, commonsense reasoning, and a combinatorially-sized state-action space. Text-adventure games are structured as long puzzles or quests, interspersed with bottlenecks. The quests can usually be completed through multiple branching paths. However, games can also feature one or more bottlenecks. Bottlenecks are areas that an agent must pass through in order to progress to the next section of the game regardless of what path the agent has taken to complete that section of the quest BIBREF0. In this work, we focus on more effectively exploring this space and surpassing these bottlenecks—building on prior work that focuses on tackling the other problems.
Formally, we use the definition of text-adventure games as seen in BIBREF1 and BIBREF2. These games are partially observable Markov decision processes (POMDPs), represented as a 7-tuple of $\langle S,T,A,\Omega , O,R, \gamma \rangle $ representing the set of environment states, mostly deterministic conditional transition probabilities between states, the vocabulary or words used to compose text commands, observations returned by the game, observation conditional probabilities, reward function, and the discount factor respectively. For our purposes, understanding the exact state and action spaces we use in this work is critical and so we define each of these in relative depth.
Action-Space. To solve Zork1, the cannonical text-adventure games, requires the generation of actions consisting of up to five-words from a relatively modest vocabulary of 697 words recognized by the game’s parser. This results in $\mathcal {O}(697^5)={1.64e14}$ possible actions at every step. To facilitate text-adventure game playing, BIBREF2 introduce Jericho, a framework for interacting with text-games. They propose a template-based action space in which the agent first selects a template, consisting of an action verb and preposition, and then filling that in with relevant entities $($e.g. $[get]$ $ [from] $ $)$. Zork1 has 237 templates, each with up to two blanks, yielding a template-action space of size $\mathcal {O}(237 \times 697^2)={1.15e8}$. This space is still far larger than most used by previous approaches applying reinforcement learning to text-based games.
State-Representation. Prior work has shown that knowledge graphs are effective in terms of dealing with the challenges of partial observability $($BIBREF3 BIBREF3; BIBREF4$)$. A knowledge graph is a set of 3-tuples of the form $\langle subject, relation, object \rangle $. These triples are extracted from the observations using Stanford's Open Information Extraction (OpenIE) BIBREF5. Human-made text-adventure games often contain relatively complex semi-structured information that OpenIE is not designed to parse and so they add additional rules to ensure that the correct information is parsed. The graph itself is more or less a map of the world, with information about objects' affordances and attributes linked to the rooms that they are place in a map. The graph also makes a distinction with respect to items that are in the agent's possession or in their immediate surrounding environment. An example of what the knowledge graph looks like and specific implementation details can be found in Appendix SECREF14.
BIBREF6 introduce the KG-A2C, which uses a knowledge graph based state-representation to aid in the section of actions in a combinatorially-sized action-space—specifically they use the knowledge graph to constrain the kinds of entities that can be filled in the blanks in the template action-space. They test their approach on Zork1, showing the combination of the knowledge graph and template action selection resulted in improvements over existing methods. They note that their approach reaches a score of 40 which corresponds to a bottleneck in Zork1 where the player is eaten by a “grue” (resulting in negative reward) if the player has not first lit a lamp. The lamp must be lit many steps after first being encountered, in a different section of the game; this action is necessary to continue exploring but doesn’t immediately produce any positive reward. That is, there is a long term dependency between actions that is not immediately rewarded, as seen in Figure FIGREF1. Others using artificially constrained action spaces also report an inability to pass through this bottleneck BIBREF7, BIBREF8. They pose a significant challenge for these methods because the agent does not see the correct action sequence to pass the bottleneck enough times. This is in part due to the fact that for that sequence to be reinforced, the agent needs to reach the next possible reward beyond the bottleneck.
More efficient exploration strategies are required to pass bottlenecks. Our contributions are two-fold. We first introduce a method that detects bottlenecks in text-games using the overall reward gained and the knowledge graph state. This method freezes the policy used to reach the bottleneck and restarts the training from there on out, additionally conducting a backtracking search to ensure that a sub-optimal policy has not been frozen. The second contribution explore how to leverage knowledge graphs to improve existing exploration algorithms for dealing with combinatorial action-spaces such as Go-Explore BIBREF9. We additionally present a comparative ablation study analyzing the performance of these methods on the popular text-game Zork1.
Bottlenecks are areas that an agent must pass through in order to progress to the next section of the game regardless of what path the agent has taken to complete that section of the quest (Stolle & Precup, 2002).
|
What are the bottlenecks in text-adventure games?
|
Bottlenecks are areas that an agent must pass through in order to progress to the next section of the game regardless of what path the agent has taken to complete that section of the quest.
|
null | false
| null |
Best Airline in the world
|
1. Quatar airway
2. Singapore Airlines
3. Emirates
|
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null | false
| null |
Which of the following are considered golf clubs: woods, irons, jellies, sliders, hybrids, wedges, driver, squeaker, putters
|
woods, irons, hybrids, wedges, driver, putters
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null | false
| null |
Why is my electrical outlet not working?
|
Assuming that outlet has been working fine and there were not recent catastrophic events or construction that has taken place, you likely need to reset the circuit.
First check to see if there is an ongoing regional power outage. An easy way to check for this is to see if the power is working elsewhere in your building or neighboring buildings.
If the outage is localized to a portion of your building, you may want to check the following:
If the outlet is a GFCI, you will need to find the main outlet in the circuit and push the reset button on it. GFCI (or GFI) outlets are common in locations where water is present, for example in kitchens and bathrooms.
If the outlet is not a GFCI outlet or if resetting the GFCI outlet had no effect, locate the circuit breaker for your building and check if any of the circuits have been tripped. You can identify a tripped circuit when you open the panel by looking for the one of the circuits in the panel that is not in the same orientation as the others. Fix it by flipping the switch to match the others.
|
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null | false
| 2
|
There has been significant progress on Named Entity Recognition (NER) in recent years using models based on machine learning algorithms BIBREF0 , BIBREF1 , BIBREF2 . As with other Natural Language Processing (NLP) tasks, building NER systems typically requires a massive amount of labeled training data which are annotated by experts. In real applications, we often need to consider new types of entities in new domains where we do not have existing annotated data. For such new types of entities, however, it is very hard to find experts to annotate the data within short time limits and hiring experts is costly and non-scalable, both in terms of time and money.
In order to quickly obtain new training data, we can use crowdsourcing as one alternative way at lower cost in a short time. But as an exchange, crowd annotations from non-experts may be of lower quality than those from experts. It is one biggest challenge to build a powerful NER system on such a low quality annotated data. Although we can obtain high quality annotations for each input sentence by majority voting, it can be a waste of human labors to achieve such a goal, especially for some ambiguous sentences which may require a number of annotations to reach an agreement. Thus majority work directly build models on crowd annotations, trying to model the differences among annotators, for example, some of the annotators may be more trustful BIBREF3 , BIBREF4 .
Here we focus mainly on the Chinese NER, which is more difficult than NER for other languages such as English for the lack of morphological variations such as capitalization and in particular the uncertainty in word segmentation. The Chinese NE taggers trained on news domain often perform poor in other domains. Although we can alleviate the problem by using character-level tagging to resolve the problem of poor word segmentation performances BIBREF5 , still there exists a large gap when the target domain changes, especially for the texts of social media. Thus, in order to get a good tagger for new domains and also for the conditions of new entity types, we require large amounts of labeled data. Therefore, crowdsourcing is a reasonable solution for these situations.
In this paper, we propose an approach to training a Chinese NER system on the crowd-annotated data. Our goal is to extract additional annotator independent features by adversarial training, alleviating the annotation noises of non-experts. The idea of adversarial training in neural networks has been used successfully in several NLP tasks, such as cross-lingual POS tagging BIBREF6 and cross-domain POS tagging BIBREF7 . They use it to reduce the negative influences of the input divergences among different domains or languages, while we use adversarial training to reduce the negative influences brought by different crowd annotators. To our best knowledge, we are the first to apply adversarial training for crowd annotation learning.
In the learning framework, we perform adversarial training between the basic NER and an additional worker discriminator. We have a common Bi-LSTM for representing annotator-generic information and a private Bi-LSTM for representing annotator-specific information. We build another label Bi-LSTM by the crowd-annotated NE label sequence which reflects the mind of the crowd annotators who learn entity definitions by reading the annotation guidebook. The common and private Bi-LSTMs are used for NER, while the common and label Bi-LSTMs are used as inputs for the worker discriminator. The parameters of the common Bi-LSTM are learned by adversarial training, maximizing the worker discriminator loss and meanwhile minimizing the NER loss. Thus the resulting features of the common Bi-LSTM are worker invariant and NER sensitive.
For evaluation, we create two Chinese NER datasets in two domains: dialog and e-commerce. We require the crowd annotators to label the types of entities, including person, song, brand, product, and so on. Identifying these entities is useful for chatbot and e-commerce platforms BIBREF8 . Then we conduct experiments on the newly created datasets to verify the effectiveness of the proposed adversarial neural network model. The results show that our system outperforms very strong baseline systems. In summary, we make the following contributions:
For such new types of entities, however, it is very hard to find experts to annotate the data within short time limits and hiring experts is costly and non-scalable, both in terms of time and money.
|
What are the obstacles to new types of entities?
|
It is very hard to find experts to annotate the data within short time limits and hiring experts is costly and non-scalable, both in terms of time and money.
|
1811.02906
| false
| null |
FLOAT SELECTED: Table 2: Transfer learning performance (Task 1)
FLOAT SELECTED: Table 2: Transfer learning performance (Task 1)
|
By how much does transfer learning improve performance on this task?
|
In task 1 best transfer learning strategy improves F1 score by 4.4% and accuracy score by 3.3%, in task 2 best transfer learning strategy improves F1 score by 2.9% and accuracy score by 1.7%
|
null | false
| null |
Historically, what are the largest animals on earth? Please include dinosaurs
|
The largest animals to roam the earth were the dinosaurs. Of these, the Sauropods were the largest family of dinosaurs. Sauropods were herbivorous. The Diplodocus was the longest dinosaur found with a complete skeleton with a length of 26 metres or 85 feet.
Larger dinosaurs did exist but only individual bones have been found.
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null | false
| null |
Where can I go to find out what is on the ballot for any given election in Wisconsin?
|
The MyVote.wi.gov site has several resources to help Wisconsin voters. One resource allows you to enter in your address, and based on that address, it will show you what a sample ballot will look like. This helps voters to see what positions or questions will appear on the ballot so that they can research them prior to voting. Providing referendum questions in advance allows voters to read through them to make sure they understand what is being asked, as the language used in these questions is sometimes legalistic and easier to process outside of the pressure of the voting booth. This website is provided by the Wisconsin Elections Commission, which is a bipartisan commission charged with implementing Wisconsin's election statutes.
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null | false
| 109
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NLP research is gradually evolving from lexical to compositional semantics BIBREF10 through the adoption of novel meaning-preserving and context-aware paradigms such as convolutional networks BIBREF11 , recurrent belief networks BIBREF12 , statistical learning theory BIBREF13 , convolutional multiple kernel learning BIBREF14 , and commonsense reasoning BIBREF15 . But while other NLP tasks have been extensively investigated, sarcasm detection is a relatively new research topic which has gained increasing interest only recently, partly thanks to the rise of social media analytics and sentiment analysis. Sentiment analysis BIBREF16 and using multimodal information as a new trend BIBREF17 , BIBREF18 , BIBREF19 , BIBREF20 , BIBREF14 is a popular branch of NLP research that aims to understand sentiment of documents automatically using combination of various machine learning approaches BIBREF21 , BIBREF22 , BIBREF20 , BIBREF23 .
An early work in this field was done by BIBREF6 on a dataset of 6,600 manually annotated Amazon reviews using a kNN-classifier over punctuation-based and pattern-based features, i.e., ordered sequence of high frequency words. BIBREF1 used support vector machine (SVM) and logistic regression over a feature set of unigrams, dictionary-based lexical features and pragmatic features (e.g., emoticons) and compared the performance of the classifier with that of humans. BIBREF24 described a set of textual features for recognizing irony at a linguistic level, especially in short texts created via Twitter, and constructed a new model that was assessed along two dimensions: representativeness and relevance. BIBREF5 used the presence of a positive sentiment in close proximity of a negative situation phrase as a feature for sarcasm detection. BIBREF25 used the Balanced Window algorithm for classifying Dutch tweets as sarcastic vs. non-sarcastic; n-grams (uni, bi and tri) and intensifiers were used as features for classification.
BIBREF26 compared the performance of different classifiers on the Amazon review dataset using the imbalance between the sentiment expressed by the review and the user-given star rating. Features based on frequency (gap between rare and common words), written spoken gap (in terms of difference between usage), synonyms (based on the difference in frequency of synonyms) and ambiguity (number of words with many synonyms) were used by BIBREF3 for sarcasm detection in tweets. BIBREF9 proposed the use of implicit incongruity and explicit incongruity based features along with lexical and pragmatic features, such as emoticons and punctuation marks. Their method is very much similar to the method proposed by BIBREF5 except BIBREF9 used explicit incongruity features. Their method outperforms the approach by BIBREF5 on two datasets.
BIBREF8 compared the performance with different language-independent features and pre-processing techniques for classifying text as sarcastic and non-sarcastic. The comparison was done over three Twitter dataset in two different languages, two of these in English with a balanced and an imbalanced distribution and the third one in Czech. The feature set included n-grams, word-shape patterns, pointedness and punctuation-based features.
In this work, we use features extracted from a deep CNN for sarcasm detection. Some of the key differences between the proposed approach and existing methods include the use of a relatively smaller feature set, automatic feature extraction, the use of deep networks, and the adoption of pre-trained NLP models.
An early work in this field was done by (Tsur et al., 2010) on a dataset of 6,600 manually annotated Amazon reviews using a kNN-classifier over punctuation-based and pattern-based features, i.e., ordered sequence of high frequency words.
|
What classifier was used in the work of Tsur et al.?
|
A kNN-classifier.
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null | false
| null |
Which TV Show is about a zip code in Beverly Hills?
|
90210
|
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null | false
| null |
When to use stones in your landscape?
|
Stones or rocks are great to prevent weeds from growing. They are better than mulch in that they are low maintenance. You will never need to replace stones. Stones do not attract insects comparing to mulch. Stones also prevent soil erosion.
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null | false
| null |
how does the java virtual machine work?
|
the java virtual machine or JVM works by providing a virtual set of instructions and facilities that Java programs rely on to run. It then translates these virtual instructions run by the program into instructions that can be run by the underlying system or hardware. As one of the major design goals of Java from its origins was to provide portability of code, this capability allows all java programs to be compiled only for the specifications of the Java Virtual Machine, with the guarantee that those programs will run on any system or hardware platform that has an available Java Virtual Machine that runs on it.
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null | false
| 32
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Propaganda has been tackled mostly at the article level. BIBREF3 created a corpus of news articles labelled as propaganda, trusted, hoax, or satire. BIBREF4 experimented with a binarized version of that corpus: propaganda vs. the other three categories. BIBREF5 annotated a large binary corpus of propagandist vs. non-propagandist articles and proposed a feature-based system for discriminating between them. In all these cases, the labels were obtained using distant supervision, assuming that all articles from a given news outlet share the label of that outlet, which inevitably introduces noise BIBREF6.
A related field is that of computational argumentation which, among others, deals with some logical fallacies related to propaganda. BIBREF7 presented a corpus of Web forum discussions with instances of ad hominem fallacy. BIBREF8, BIBREF9 introduced Argotario, a game to educate people to recognize and create fallacies, a by-product of which is a corpus with $1.3k$ arguments annotated with five fallacies such as ad hominem, red herring and irrelevant authority, which directly relate to propaganda.
Unlike BIBREF8, BIBREF9, BIBREF7, our corpus uses 18 techniques annotated on the same set of news articles. Moreover, our annotations aim at identifying the minimal fragments related to a technique instead of flagging entire arguments.
The most relevant related work is our own, which is published in parallel to this paper at EMNLP-IJCNLP 2019 BIBREF10 and describes a corpus that is a subset of the one used for this shared task.
Moreover, our annotations aim at identifying the minimal fragments related to a technique instead of flagging entire arguments.
|
What do the annotations aim at?
|
Identifying the minimal fragments related to a technique instead of flagging entire arguments.
|
1601.06068
| false
| null |
In their traditional use, the latent states in L-PCFGs aim to capture syntactic information. We introduce here the use of an L-PCFG with two layers of latent states: one layer is intended to capture the usual syntactic information, and the other aims to capture semantic and topical information by using a large set of states with specific feature functions.
We introduce here the use of an L-PCFG with two layers of latent states: one layer is intended to capture the usual syntactic information, and the other aims to capture semantic and topical information by using a large set of states with specific feature functions.
|
What latent variables are modeled in the PCFG?
|
The answers are shown as follows:
* syntactic information
* semantic and topical information
|
null | false
| null |
What colors would you use for a futuristic logo?
|
Colors such as blue, silver, grey and white evoke a feel of futurism and would be well suited to a futuristic logo.
|
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null | false
| null |
What are the benefits of working with a coach?
|
Working with a coach can help you move quicker and effectively towards achieving your goals. A coach can help you set the right goals, facilitate creating a plan of action to work towards your goals and help you stay accountable to taking actions that will lead you towards achieving your goals.
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null | false
| 71
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We present an attention-based approach for the detection of harassment language in tweets and the detection of different types of harassment as well. Our approach is based on the Recurrent Neural Networks and particularly we are using a deep, classification specific attention mechanism. Moreover, we present a comparison between different variations of this attention-based approach and a few baseline methods. According to the results of our experiments and considering the F1 Score, the multi-attention method having a projected layer, achieved the highest performance. Also, we tackled the problem of the imbalance between the training, validation and test sets performing the technique of back-translation.
In the future, we would like to perform more experiments with this dataset applying different models using BERT BIBREF21. Also, we would like to apply the models presented in this work, in other datasets about hate speech in social media.
According to the results of our experiments and considering the F1 Score, the multi-attention method having a projected layer, achieved the highest performance.
|
Can a multi-attention approach with a projection layer achieve the highest performance?
|
Yes.
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null | false
| null |
Identify which instrument is string or percussion: Ainu fiddle, Apito
|
Apito is percussion, Ainu fiddle is string.
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null | false
| null |
The first video game consoles were produced in the early 1970s. Ralph H. Baer devised the concept of playing simple, spot-based games on a television screen in 1966, which later became the basis of the Magnavox Odyssey in 1972. Inspired by the table tennis game on the Odyssey, Nolan Bushnell, Ted Dabney, and Allan Alcorn at Atari, Inc. developed the first successful arcade game, Pong, and looked to develop that into a home version, which was released in 1975. The first consoles were capable of playing only a very limited number of games built into the hardware. Programmable consoles using swappable ROM cartridges were introduced with the Fairchild Channel F in 1976, though popularized with the Atari 2600 released in 1977.
|
From the passage provided, when was the home version of Pong released?
|
The home version of Pong was released in 1975.
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null | false
| null |
What is a dam?
|
A dam is a barrier that stops the flow of water of a river and creates an artificial lake. Dams are used for different reasons. Some dams are used to gather water for industrial, agricultural needs or water supply to civil aqueducts. Another usage can be for power generation employing hydroelectric turbines. Some other times, it is mean of water flow control aimed at flood prevention or land reclamation.
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null | false
| null |
Can a neutral wire in a home electrical circuit give me an electric shock
|
Ideally the answer would be no. The neutral and the ground are always connected at the main panel. Hence the voltage difference between ground and neutral is only a few volts. However, if a neutral wire in a circuit is unspliced or disconnected, the neutral does get loaded and can give a person a nasty jolt.
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null | false
| null |
What is the value of x if x^2 = 4?
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x is either equal to 2 or -2
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How much wood would a woodchuck chuck if a woodchuck could chuck wood?
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This phrase is not meant to have an answer because it's a tongue twister. Tongue twisters are meant to be difficult to say, especially when spoken quickly. It's regarded as a fun way for children to learn about alliteration, how to pronounce and enunciate similar sounding words with different meaning and supports a basis for spoken language development.
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A transcontinental railroad in the United States is any continuous rail line connecting a location on the U.S. Pacific coast with one or more of the railroads of the nation's eastern trunk line rail systems operating between the Missouri or Mississippi Rivers and the U.S. Atlantic coast. The first concrete plan for a transcontinental railroad in the United States was presented to Congress by Asa Whitney in 1845.
A series of transcontinental railroads built over the last third of the 19th century created a nationwide transportation network that united the country by rail. The first of these, the 3,103 km (1,928 mi) "Pacific Railroad", was built by the Central Pacific Railroad and Union Pacific Railroad to link the San Francisco Bay at Alameda, California, with the nation's existing eastern railroad network at Council Bluffs, Iowa/Omaha, Nebraska, thereby creating the world's first transcontinental railroad when it opened in 1869. Its construction was made possible by the US government under Pacific Railroad Acts of 1862, 1864, and 1867. Its original course was very close to current Interstate 80.
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Extract the names of the companies that built the Pacific Railroad
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The "Pacific Railroad" was built by the Central Pacific Railroad and Union Pacific Railroad.
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| 94
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In this paper, we examine the use case of general adversarial networks (GANs) in the field of marketing. In particular, we analyze how GAN models can replicate text patterns from successful product listings on Airbnb, a peer-to-peer online market for short-term apartment rentals. To do so, we define the Diehl-Martinez-Kamalu (DMK) loss function as a new class of functions that forces the model's generated output to include a set of user-defined keywords. This allows the general adversarial network to recommend a way of rewording the phrasing of a listing description to increase the likelihood that it is booked. Although we tailor our analysis to Airbnb data, we believe this framework establishes a more general model for how generative algorithms can be used to produce text samples for the purposes of marketing.
In this paper, we examine the use case of general adversarial networks (GANs) in the field of marketing.
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What field do they research?
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Marketing.
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Give me some examples sedimentary rocks.
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Sandstone and chalk are sedimentary rocks.
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What is the largest lake in Switzerland?
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The largest lake in Switzerland with an area of 218 km2 is Lake Neuchâtel. Lake Geneva is the largest freshwater lake in Central Europe, but is divided between Switzerland and France.
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Linus Torwalds invented and wrote what
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Linux computer operating system
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Which film holds the record for most Academy Award wins?
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Three films own the record for the most Academy Award wins. They are Titanic, Ben Hur, and Lord of the Rings: Return of the King
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Douglas Stuart Moore (August 10, 1893 – July 25, 1969) was an American composer, songwriter, organist, pianist, conductor, educator, actor, and author. A composer who mainly wrote works with an American subject, his music is generally characterized by lyricism in a popular or conservative style which generally eschewed the more experimental progressive trends of musical modernism. Composer Virgil Thomson described Moore as a neoromantic composer who was influenced by American folk music. While several of his works enjoyed popularity during his lifetime, only his folk opera The Ballad of Baby Doe (1956) has remained well known into the 21st century.
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What is Douglas Stuart Moore's best remembered for?
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The Ballad of Baby Doe (1956) as it has remained well known into the 21st century.
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Craigslist (stylized as craigslist) is a privately-held American company operating a classified advertisements website with sections devoted to jobs, housing, for sale, items wanted, services, community service, gigs, résumés, and discussion forums.
Craig Newmark began the service in 1995 as an email distribution list to friends, featuring local events in the San Francisco Bay Area. It became a web-based service in 1996 and expanded into other classified categories. It started expanding to other U.S. and Canadian cities in 2000, and now covers 70 countries.
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What year was Craigslist founded?
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Craigslist was founded in the year 1995 by Craig Newmark.
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What is the best way to teach your kids how to read?
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The best way to teach your kids how to read is to put them in school. You will also want to spend 20 minutes a day reading with them in the morning or 30 minutes before bed to augment and solidify what they learn at school. Teach your kids how to spell, sentence punctuation, sentence intonation, and how to sound out words they do not know. After reading ask questions about what was read to make sure your child understood what was read. Ask them inference type questions, and questions directly related to what happened in the story.
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What Is the Population of San Francisco and the Bay Area?
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The U.S. Census estimates the 2022 population of the City and County of San Francisco to be 808,437, representing a loss of 65,000 people and 7.5% compared to 2020. The city’s population dropped by 7.2% between 2020 and 2021, but only by 0.3% from 2021 to 2022.
A closer look at the Bay Area numbers tells a more dramatic story: The nine-county region has lost a quarter million residents between 2020 and 2022, or a 3.2% decline. The exodus left the 2022 population of the SF Bay Area at 7.5 million.
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What are the different kinds of rock climbing?
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The most popular types of climbing are:
- Bouldering: climbing low hanging challenges, likely with crash pads to fall on.
- Sport climbing (or indoor climbing): Climbing a route with fixed anchors
- Traditional climbing: Climbing with gear that is inserted and removed without fixed anchors
Once climbers gain experience, they are able to begin climbing more extreme outdoor terrain however many climbers may stick to bouldering or sport climbing.
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| 92
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To paraphrase the great bard BIBREF21, there is something rotten in the state of the art. We propose Human-And-Model-in-the-Loop Entailment Training (HAMLET), a training procedure to automatically mitigate problems with current dataset collection procedures (see Figure FIGREF1).
In our setup, our starting point is a base model, trained on NLI data. Rather than employing automated adversarial methods, here the model's “adversary” is a human annotator. Given a context (also often called a “premise” in NLI), and a desired target label, we ask the human writer to provide a hypothesis that fools the model into misclassifying the label. One can think of the writer as a “white hat” hacker, trying to identify vulnerabilities in the system. For each human-generated example that is misclassified, we also ask the writer to provide a reason why they believe it was misclassified.
For examples that the model misclassified, it is necessary to verify that they are actually correct —i.e., that the given context-hypothesis pairs genuinely have their specified target label. The best way to do this is to have them checked by another human. Hence, we provide the example to human verifiers. If two human verifiers agree with the writer, the example is considered a good example. If they disagree, we ask a third human verifier to break the tie. If there is still disagreement between the writer and the verifiers, the example is discarded. Occasionally, verifiers will overrule the original label of the writer.
Once data collection for the current round is finished, we construct a new training set from the collected data, with accompanying development and test sets. While the training set includes correctly classified examples, the development and tests sets are built solely from them. The test set was further restricted so as to: 1) include pairs from “exclusive” annotators that are never included in the training data; and 2) be balanced by label classes (and genres, where applicable). We subsequently train a new model on this and other existing data, and repeat the procedure three times.
In our setup, our starting point is a base model, trained on NLI data.
|
What is the starting point in the setup in this paper?
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The starting point is a base model, trained on NLI data.
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The movement of electric charge is known as an electric current, the intensity of which is usually measured in amperes. Current can consist of any moving charged particles; most commonly these are electrons, but any charge in motion constitutes a current. Electric current can flow through some things, electrical conductors, but will not flow through an electrical insulator.
By historical convention, a positive current is defined as having the same direction of flow as any positive charge it contains, or to flow from the most positive part of a circuit to the most negative part. Current defined in this manner is called conventional current. The motion of negatively charged electrons around an electric circuit, one of the most familiar forms of current, is thus deemed positive in the opposite direction to that of the electrons. However, depending on the conditions, an electric current can consist of a flow of charged particles in either direction, or even in both directions at once. The positive-to-negative convention is widely used to simplify this situation.
The process by which electric current passes through a material is termed electrical conduction, and its nature varies with that of the charged particles and the material through which they are travelling. Examples of electric currents include metallic conduction, where electrons flow through a conductor such as metal, and electrolysis, where ions (charged atoms) flow through liquids, or through plasmas such as electrical sparks. While the particles themselves can move quite slowly, sometimes with an average drift velocity only fractions of a millimetre per second, the electric field that drives them itself propagates at close to the speed of light, enabling electrical signals to pass rapidly along wires.
In engineering or household applications, current is often described as being either direct current (DC) or alternating current (AC). These terms refer to how the current varies in time. Direct current, as produced by example from a battery and required by most electronic devices, is a unidirectional flow from the positive part of a circuit to the negative. If, as is most common, this flow is carried by electrons, they will be travelling in the opposite direction. Alternating current is any current that reverses direction repeatedly; almost always this takes the form of a sine wave. Alternating current thus pulses back and forth within a conductor without the charge moving any net distance over time. The time-averaged value of an alternating current is zero, but it delivers energy in first one direction, and then the reverse. Alternating current is affected by electrical properties that are not observed under steady state direct current, such as inductance and capacitance. These properties however can become important when circuitry is subjected to transients, such as when first energised.
|
Based on the following passage what are positive and conventional current?
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By historical convention, a positive current is defined as having the same direction of flow as any positive charge it contains, or to flow from the most positive part of a circuit to the most negative part. Current defined in this manner is called conventional current.
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Here is a list of movies. Which of these are considered to be Action Movies. American Graffiti, Rain Man, Mission Impossible, Goldfinger, Speed, The Devil's Advocate, The Magnificent Seven, Dazed and Confused, Die Hard, Top Gun.
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Mission Impossible, Goldfinger, Speed, The Magnificent Seven, Die Hard, Top Gun are all considered to be Action Movies. American Graffiti, Rain Man, The Devil's Advocate, Dazed and Confused are not Action Movies.
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Why would someone want to ride a bike?
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Someone might want to ride a bike for a number of different reasons. Bikes, also known as bicycles, are used for recreation, fitness, and transportation. They are very common and used in nearly every city across the globe. There are also bike races across the world, particularly in Europe, where it is a pro sport.
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| 7
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This paper provided a taxonomy of stereotype-driven descriptions in the Flickr30K dataset. I have divided these descriptions into two classes: linguistic bias and unwarranted inferences. The former corresponds to the annotators' choice of words when confronted with an image that may or may not match their stereotypical expectancies. The latter corresponds to the tendency of annotators to go beyond what the physical data can tell us, and expand their descriptions based on their past experiences and knowledge of the world. Acknowledging these phenomena is important, because on the one hand it helps us think about what is learnable from the data, and on the other hand it serves as a warning: if we train and evaluate language models on this data, we are effectively teaching them to be biased.
I have also looked at methods to detect stereotype-driven descriptions, but due to the richness of language it is difficult to find an automated measure. Depending on whether your goal is production or interpretation, it may either be useful to suppress or to emphasize biases in human language. Finally, I have discussed stereotyping behavior as the addition of a contextual layer on top of a more basic description. This raises the question what kind of descriptions we would like our models to produce.
This paper provided a taxonomy of stereotype-driven descriptions in the Flickr30K dataset.
|
What does the paper provide?
|
A taxonomy of stereotype-driven descriptions in the Flickr30K dataset.
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Why do people like to ski?
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Skiing is a fun outdoor sport that can be enjoyed solo or with friends or family. Skiing is relatively low impact, so that people can ski for many years. It is a great all-ages activity. Most mountains have varied terrain, so you can choose how beginner or advanced you'd like to ski each time you go. People who ski love the exercise they get from it and the thrill of sliding down the mountain. Unlike most sledding, while skiing, you have a chair lift that takes you up the hill, so you don't have to waste time or energy hiking up for your next run.
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The ancestors of llamas are thought to have originated from the Great Plains of North America about 40 million years ago, and subsequently migrated to South America about three million years ago during the Great American Interchange. By the end of the last ice age (10,000–12,000 years ago), camelids were extinct in North America. As of 2007, there were over seven million llamas and alpacas in South America and over 158,000 llamas and 100,000 alpacas, descended from progenitors imported late in the 20th century, in the United States and Canada.
In Aymara mythology, llamas are important beings. The Heavenly Llama is said to drink water from the ocean and urinates as it rains. According to Aymara eschatology, llamas will return to the water springs and lagoons where they come from at the end of time.
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Provide a list of all numbers mentioned in the provided text and what they represent
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40 million: The number of years ago when the ancestors of llamas are thought to have originated from the Great Plains of North America
three million: The number of years ago the ancestors of llamas are thought to have migrated to South America during the Great American Interchange
10,000–12,000: The number of years ago camelids were extinct in North America
2007: As of this year, there were over seven million llamas and alpacas in South America and over 158,000 llamas and 100,000 alpacas, descended from progenitors imported late in the 20th century, in the United States and Canada
seven million: The number of llamas and alpacas in South America as of 2007
158,000: The number of llamas in the United States and Canada, that descended from progenitors imported late in the 20th century, as of 2007
100,000: The number of alpacas in the United States and Canada, that descended from progenitors imported late in the 20th century, as of 2007
20: The century where progenitors were imported to United States and Canada
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Which of the following as water bodies? Classify them as 'water' and 'not water'. Oceans, Lakes, Ponds, Trees, Mountains, Sky, Roads, Fire.
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'water', 'water', 'water', 'not water', 'not water', 'not water', 'not water', 'not water'.
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Why do people use a mobile phone?
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People use a mobile phones for different reasons. For example, they can use it to connect with friends and family by text messages or phone calls. Also, they can install other social apps such as Facebook, Instagram and Whatsapp that help them connect. Some might also use it for work, to communicate with colleagues and access work-related apps and email from anywhere. Also, mobile phones provide access to unlimited information through the Internet so that you can stay connected anytime from anywhere.
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What is the American show The Office based on?
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The Office is based on the original version of the Office, which was produced in England and is known as the British Office
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| 114
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Natural text generation, as a key task in NLP, has been advanced substantially thanks to the flourish of neural models BIBREF0 , BIBREF1 . Typical frameworks such as sequence-to-sequence (seq2seq) have been applied to various generation tasks, including machine translation BIBREF2 and dialogue generation BIBREF3 . The standard paradigm to train such neural models is maximum likelihood estimation (MLE), which maximizes the log-likelihood of observing each word in the text given the ground-truth proceeding context BIBREF4 .
Although widely used, MLE suffers from the exposure bias problem BIBREF5 , BIBREF6 : during test, the model sequentially predicts the next word conditioned on its previous generated words while during training conditioned on ground-truth words. To tackle this problem, generative adversarial networks (GAN) with reinforcement learning (RL) training approaches have been introduced to text generation tasks BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 , where the discriminator is trained to distinguish real and generated text samples to provide reward signals for the generator, and the generator is optimized via policy gradient BIBREF7 .
However, recent studies have shown that potential issues of training GANs on discrete data are more severe than exposure bias BIBREF14 , BIBREF15 . One of the fundamental issues when generating discrete text samples with GANs is training instability. Updating the generator with policy gradient always leads to an unstable training process because it's difficult for the generator to derive positive and stable reward signals from the discriminator even with careful pre-training BIBREF8 . As a result, the generator gets lost due to the high variance of reward signals and the training process may finally collapse BIBREF16 .
In this paper, we propose a novel adversarial training framework called Adversarial Reward Augmented Maximum Likelihood (ARAML) to deal with the instability issue of training GANs for text generation. At each iteration of adversarial training, we first train the discriminator to assign higher rewards to real data than to generated samples. Then, inspired by reward augmented maximum likelihood (RAML) BIBREF17 , the generator is updated on the samples acquired from a stationary distribution with maximum likelihood estimation (MLE), weighted by the discriminator's rewards. This stationary distribution is designed to guarantee that training samples are surrounding the real data, thus the exploration space of our generator is indeed restricted by the MLE training objective, resulting in more stable training. Compared to other text GANs with RL training techniques, our framework acquires samples from the stationary distribution rather than the generator's distribution, and uses RAML training paradigm to optimize the generator instead of policy gradient. Our contributions are mainly as follows:
In this paper, we propose a novel adversarial training framework called Adversarial Reward Augmented Maximum Likelihood (ARAML) to deal with the instability issue of training GANs for text generation.
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What is the Adversarial Reward Augmented Maximum Likelihood (REML) used for?
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The Adversarial Reward Augmented Maximum Likelihood (ARAML) is used to deal with the instability issue of training generative adversarial networks (GANs) for text generation.
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To which London club did Mycroft Holmes belong
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Diogones
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What are the four ingredients for beer?
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Water, Barley, Yeast and Hops
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How many kids does Apu have on the Simpsons?
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8
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Which of these countries is in North America: Canada, China, or Poland
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Canada is located in North America
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| 49
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We found that during decoding, the model would occasionally assign a high probability to words based on the target context alone, ignoring the source sentence. We speculate that this is an instance of the label bias problem BIBREF7 .
To mitigate this problem, we experiment with training separate models that produce the target text from right-to-left (r2l), and re-scoring the n-best lists that are produced by the main (left-to-right) models with these r2l models. Since the right-to-left model will see a complementary target context at each time step, we expect that the averaged probabilities will be more robust. In parallel to our experiments, this idea was published by liu2016.
We increase the size of the n-best-list to 50 for the reranking experiments.
A possible criticism of the l-r/r-l reranking approach is that the gains actually come from adding diversity to the ensemble, since we are now using two independent runs. However experiments in BIBREF8 show that a l-r/r-l reranking systems is stronger than an ensemble created from two independent l-r runs.
We found that during decoding, the model would occasionally assign a high probability to words based on the target context alone, ignoring the source sentence. We speculate that this is an instance of the label bias problem (Lafferty et al., 2001).
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What defect on the model is found during decoding?
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It occasionally ignores the source sentence when assigning a high probability to words.
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What is the difference between a banjo and a ukulele?
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People usually use a ukulele for accompaniment and solo. The banjo is generally used for peals. Another difference between the two instruments is in regards to how they are tuned. A ukulele is tuned in only two main configurations while a banjo can be tuned to chord based on the musical style of the musicians and what they prefer.
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Why a manager is needed for a team?
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1. A manager's work is to manage the team and delivery the work on time
2. Team member needs to be organized their work and a manager is needed to do so
3. A manager determine the timeline to finish any job
So, A manager is needed
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Which of the following are nuts: pecan, hazelnut, butternut squash, walnut, peanut, corn nut, macadamia nut.
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The following are nuts: pecan, hazelnut, walnut, peanut, macadamia nut.
The following are not nuts: butternut squash, corn nut.
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How do you replace a hole in the drywall?
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First you start by locating the nearest wooden studs and cut a square, larger than the hole and exposing up to one inch of each wooden stud. Each wooden stud is typically sixteen inches apart - center to center. And each wooden stud is typically two inches in width. Second step is to carefully measure the open space after you've cut the square. Take your time and measure as close to the existing drywall as possible. Take 4 measurements of 2 in length and 2 in width to ensure accuracy. Cut that square from your replacement drywall. Shave sides with sandpaper for tight fit. Once your replacement square fits, insert drywall screws into the sides of the drywall that will also go through the previously exposed wooden studs. Once the new square is secure, you can layer ontop the drywall paste to let dry. Sand the drywall paste over the seams of the square, and repeat as necessary until you have a uniform, smooth surface. Apply paint over the entire workspace and let dry.
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What is a recession?
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In economics, a recession is a business cycle contraction that occurs when there is a general decline in economic activity. Recessions generally occur when there is a widespread drop in spending and it may be triggered by various events such as a financial crisis, the bursting of an economic bubble or a large-scale anthropogenic or natural disaster (e.g. a pandemic). Governments usually respond to recessions by adopting expansionary macroeconomic policies such as increasing money supply, decreasing interest rates, increasing government spending or decreasing taxes.
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who is the worlds most popular cricketer?
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Virat kholi is the worlds most popular cricketer with over 242 million instagram followers
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| 74
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Event detection on microblogging platforms such as Twitter aims to detect events preemptively. A main task in event detection is detecting events of predetermined types BIBREF0, such as concerts or controversial events based on microposts matching specific event descriptions. This task has extensive applications ranging from cyber security BIBREF1, BIBREF2 to political elections BIBREF3 or public health BIBREF4, BIBREF5. Due to the high ambiguity and inconsistency of the terms used in microposts, event detection is generally performed though statistical machine learning models, which require a labeled dataset for model training. Data labeling is, however, a long, laborious, and usually costly process. For the case of micropost classification, though positive labels can be collected (e.g., using specific hashtags, or event-related date-time information), there is no straightforward way to generate negative labels useful for model training. To tackle this lack of negative labels and the significant manual efforts in data labeling, BIBREF1 (BIBREF1, BIBREF3) introduced a weak supervision based learning approach, which uses only positively labeled data, accompanied by unlabeled examples by filtering microposts that contain a certain keyword indicative of the event type under consideration (e.g., `hack' for cyber security). Another key technique in this context is expectation regularization BIBREF6, BIBREF7, BIBREF1. Here, the estimated proportion of relevant microposts in an unlabeled dataset containing a keyword is given as a keyword-specific expectation. This expectation is used in the regularization term of the model's objective function to constrain the posterior distribution of the model predictions. By doing so, the model is trained with an expectation on its prediction for microposts that contain the keyword. Such a method, however, suffers from two key problems:
Due to the unpredictability of event occurrences and the constantly changing dynamics of users' posting frequency BIBREF8, estimating the expectation associated with a keyword is a challenging task, even for domain experts;
The performance of the event detection model is constrained by the informativeness of the keyword used for model training. As of now, we lack a principled method for discovering new keywords and improve the model performance.
To address the above issues, we advocate a human-AI loop approach for discovering informative keywords and estimating their expectations reliably. Our approach iteratively leverages 1) crowd workers for estimating keyword-specific expectations, and 2) the disagreement between the model and the crowd for discovering new informative keywords. More specifically, at each iteration after we obtain a keyword-specific expectation from the crowd, we train the model using expectation regularization and select those keyword-related microposts for which the model's prediction disagrees the most with the crowd's expectation; such microposts are then presented to the crowd to identify new keywords that best explain the disagreement. By doing so, our approach identifies new keywords which convey more relevant information with respect to existing ones, thus effectively boosting model performance. By exploiting the disagreement between the model and the crowd, our approach can make efficient use of the crowd, which is of critical importance in a human-in-the-loop context BIBREF9, BIBREF10. An additional advantage of our approach is that by obtaining new keywords that improve model performance over time, we are able to gain insight into how the model learns for specific event detection tasks. Such an advantage is particularly useful for event detection using complex models, e.g., deep neural networks, which are intrinsically hard to understand BIBREF11, BIBREF12. An additional challenge in involving crowd workers is that their contributions are not fully reliable BIBREF13. In the crowdsourcing literature, this problem is usually tackled with probabilistic latent variable models BIBREF14, BIBREF15, BIBREF16, which are used to perform truth inference by aggregating a redundant set of crowd contributions. Our human-AI loop approach improves the inference of keyword expectation by aggregating contributions not only from the crowd but also from the model. This, however, comes with its own challenge as the model's predictions are further dependent on the results of expectation inference, which is used for model training. To address this problem, we introduce a unified probabilistic model that seamlessly integrates expectation inference and model training, thereby allowing the former to benefit from the latter while resolving the inter-dependency between the two.
To the best of our knowledge, we are the first to propose a human-AI loop approach that iteratively improves machine learning models for event detection. In summary, our work makes the following key contributions:
A novel human-AI loop approach for micropost event detection that jointly discovers informative keywords and estimates their expectation;
A unified probabilistic model that infers keyword expectation and simultaneously performs model training;
An extensive empirical evaluation of our approach on multiple real-world datasets demonstrating that our approach significantly improves the state of the art by an average of 24.3% AUC.
The rest of this paper is organized as follows. First, we present our human-AI loop approach in Section SECREF2. Subsequently, we introduce our proposed probabilistic model in Section SECREF3. The experimental setup and results are presented in Section SECREF4. Finally, we briefly cover related work in Section SECREF5 before concluding our work in Section SECREF6.
The experimental setup and results are presented in Section 4.
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What is been presented in section 4?
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Experimental setup and results.
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| 505
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Our implementation is in PyTorch and depends on the VISSL, MMClassification (Contributors, 2020), and Weights & Biases libraries. The code is attached and will be released for publication.
Stage 0 (source) We train residual networks with various depths (including 18, 50, 101), and initializations (ImageNet pretraining or Kaiming init when training from scratch). We optimize cross-entropy loss by SGD with an initial learning rate 0.1, momentum 0.9, weight decay 0.0001, batch size 256. We do not apply label smoothing except for SHOT, as it specifically includes it. We adopt the standard data augmentation pipeline from ImageNet training, such as random cropping, random flipping, and color jitter. We choose ImageNet statistics as the default input mean and variance for all models.
Stage 1 (teacher) We experiment with three types of teacher models: 1) source-only, 2) TENT-IM, 3) SHOT. To optimize this altered loss, we choose SGD with learning rate 0.0001, momentum 0.9, and weight decay 0.0001. In addition to batch normalization, we also update convolutional layers except the final classification layer. As for SHOT, We execute the authors' open-sourced codebase with the same hyper-parameters for various architectures , initialization (from scratch and ImageNet pretrain), and domain shifts (train to val/test splits on VisDA-C).
Stage 2 (student) We experiment with two designs as students: 1) source-only, 2) contrastive learning. Specifically, we leverage some of off-the-shelf contrastive learning methods to initialize target-domain representation, such as MoCo v2, SimSiam SwAV, Barlow Twins. Compared to their training recipes on ImageNet, we have more epochs on VisDA-C val/test with the same batch size, learning rate, data augmentation, and model architecture, to make the training procedure longer with the smaller amount of images.
Stage 3 (teacher-student) By default, the whole knowledge distillation consists of three phases, where each phase has 10 epochs to train the student model with the hard pseudo label. The student would be reset to the contrastive model to avoid error accumulation at the beginning of every phase. The teacher would be replaced with the latest student before starting the next phase, so that the quality of pseudo-labeling could be improved gradually. We utilize SGD with an initial learning rate 0.01, momentum 0.9, weight decay 0.0001, batch size 256, and cosine annealing scheduler.
Table 1: Each stage of our on-target adaptation improves target accuracy. Contrastive learning (stage 2), for fitting the representation on target data alone, helps whether or not the teacher is adapted (stage 1).
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Why in Table 1 some numbers are missing and X is drawn?
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We have revised Table 1 to focus on each stage and the contribution of our on-target representation from contrastive learning. The results with X did not include contrastive learning, as the state-of-the-art contrastive methods we experiment with had not yet been fully explored on these datasets.
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In geometry, a simplicial polytope is a polytope whose facets are all simplices. For example, a simplicial polyhedron in three dimensions contains only triangular faces and corresponds via Steinitz's theorem to a maximal planar graph.
They are topologically dual to simple polytopes. Polytopes which are both simple and simplicial are either simplices or two-dimensional polygons.
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Tell me what are aspects of polytopes from given text
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1. A simplicial polytope is a polytope in geometry where each facet is a simplice.
2. As an illustration, a simplicial polyhedron in three dimensions with only triangular faces corresponds to a maximum planar network according to Steinitz's theorem.
3. They are simple polytopes topological dual.
4. Two-dimensional polygons or simplices are polytopes that are both simple and simplicial.
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Assume the joint distribution of INLINEFORM0 has the following form, DISPLAYFORM0
where INLINEFORM0 is the true parameter, and INLINEFORM1 is independent of INLINEFORM2 . By selecting a subset of the training data, we are essentially choosing another distribution INLINEFORM3 so that the INLINEFORM4 pairs are drawn from INLINEFORM5
Statistical signal processing theory BIBREF5 states the following asymptotic distribution of INLINEFORM0 , DISPLAYFORM0
where INLINEFORM0 is the Fisher Information Matrix with respect to INLINEFORM1 . Using first order approximation at INLINEFORM2 , we have asymptotically, DISPLAYFORM0
Eq. ( EQREF7 ) indicates that to reduce INLINEFORM0 on test data, we need to minimize the expected variance INLINEFORM1 over the test set. This is called Fisher Information Ratio criteria in BIBREF6 , which itself is hard to optimize. An easier surrogate is to maximize INLINEFORM2 . Substituting Eq. ( EQREF5 ) into INLINEFORM3 , we have INLINEFORM4
which is equivalent to INLINEFORM0
A practical issue is that we do not know INLINEFORM0 in advance. We could instead substitute an estimate INLINEFORM1 from a pre-trained model, where it is reasonable to assume the INLINEFORM2 to be close to the true INLINEFORM3 . The batch selection then works by taking the samples that have largest gradient norms, DISPLAYFORM0
For RNNs, the gradients for each potential label can be obtained by back-propagation. Another practical issue is that EGL marginalizes over all possible labelings, but in speech recognition, the number of labelings scales exponentially in the number of timesteps. Therefore, we only marginalize over the INLINEFORM0 most probable labelings. They are obtained by beam search decoding, as in BIBREF7 . The EGL method in BIBREF3 is almost the same as Eq. ( EQREF8 ), except the gradient's norm is not squared in BIBREF3 .
Here we have provided a more formal characterization of EGL to complement its intuitive interpretation as “expected model change” in BIBREF3 . For notational convenience, we denote Eq. ( EQREF8 ) as EGL in subsequent sections.
Another practical issue is that EGL marginalizes over all possible labelings, but in speech recognition, the number of labelings scales exponentially in the number of timesteps. Therefore, we only marginalize over the K most probable labelings.
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Why do the authors only marginalize over the K most probable labelings?
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Because the EGL marginalizes over all possible labelings, but in speech recognition, the number of labelings scales exponentially in the number of timesteps.
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Can you offer tips for visiting Mexico City for fun?
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Located in the center of Mexico, Mexico City is in the high central plateau. It is the capital of Mexico and the 5th largest city in the world. The average temperature ranges from 54-61 degrees Fahrenheit and traffic is much more tolerable compared with the other large Metro Areas in the US like San Fransisco and Los Angeles. The city has lots of greenspace and has a “European” feel. Some practical tips when traveling to Mexico City, include 1. using the ride sharing app Didi, which is more popular than Uber or Lyft in Mexico City, to get around the city; 2. tipping is recommended and servers accept tips for small favors; 3.withdrawing money from ATM machines usually has high foreign transaction fees so it is better to exchange money at the airport or before arriving; 4. food is fantastic! Visitors will enjoy food at both upscale restaurants and low-key food trucks.
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| 273
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Nowadays, a large amount of information is produced and shared in unstructured form, mostly unstructured text BIBREF0 , BIBREF1 . This information can be exploited in decision making processes but, to be useful, it should be transformed and presented in ways that make its intrinsic knowledge more readily intelligible. For that, we need efficient methods and tools that quickly extract useful information from unstructured text collections. Such demand can be observed, for instance, in Biology, where researchers, in order to be abreast of all developments, need to analyse new biomedical literature on a daily basis BIBREF2 . Another application is on fraud and corruption studies where the network information — the set of actors and their relationships — is implicitly stored in unstructured natural-language documents BIBREF3 . Hence, text mining and information extraction are required to pre-process the texts in order to extract the entities and the relations between them.
Information extraction is a challenging task mainly due to the ambiguous features of natural-language. Moreover, most tools need to be adapted to different human languages and to different domains BIBREF4 . In fact, the language of the processed texts is still the decisive factor when choosing among existing information extraction technologies. This is also true for the task of entity extraction (Named Entity Recognition - NER).
For several reasons, text mining tools are typically first developed for English and only afterwards extended to other languages. Thus, there are still relatively few text mining tools for Portuguese and even less that are freely accessible. In particular, for the named entities recognition task in Portuguese texts, we find three extractors available: Alchemy, Zemanta and Rembrandt BIBREF5 . We also find some studies where the measures ( INLINEFORM0 , INLINEFORM1 and INLINEFORM2 ) for those extractors are computed and compared BIBREF6 , but their comparative effectiveness remains domain and final purpose dependent.
In this work, we present PAMPO (PAttern Matching and POs tagging based algorithm for NER), a new method to automatically extract named entities from unstructured texts, applicable to the Portuguese language but potentially adaptable to other languages as well. The method relies on flexible pattern matching, part-of-speech tagging and lexical-based rules. All steps are implemented using free software and taking advantage of various existing packages.
The process has been developed using as case-study a specific book written in Portuguese, but it has since been used in other applications and successfully tested in different text collections. In this paper, we describe the evaluation procedures on independent textual collections, and produce a comparative study of PAMPO with other existing tools for NER.
In this work, we present PAMPO (PAttern Matching and POs tagging based algorithm for NER), a new method to automatically extract named entities from unstructured texts, applicable to the Portuguese language but potentially adaptable to other languages as well.
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What is PAMPO?
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PAttern Matching and POs tagging based algorithm for Named Entity Recognition - NER, a new method to automatically extract named entities from unstructured texts.
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1912.00819
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In this work, we apply an automated neural ensemble annotation process for dialogue act labeling. Several neural models are trained with the Switchboard Dialogue Act (SwDA) Corpus BIBREF9, BIBREF10 and used for inferring dialogue acts on the emotion datasets. We ensemble five model output labels by checking majority occurrences (most of the model labels are the same) and ranking confidence values of the models. We have annotated two potential multi-modal conversation datasets for emotion recognition: IEMOCAP (Interactive Emotional dyadic MOtion CAPture database) BIBREF6 and MELD (Multimodal EmotionLines Dataset) BIBREF8. Figure FIGREF2, shows an example of dialogue acts with emotion and sentiment labels from the MELD dataset. We confirmed the reliability of annotations with inter-annotator metrics. We analysed the co-occurrences of the dialogue act and emotion labels and discovered a key relationship between them; certain dialogue acts of the utterances show significant and useful association with respective emotional states. For example, Accept/Agree dialogue act often occurs with the Joy emotion while Reject with Anger, Acknowledgements with Surprise, Thanking with Joy, and Apology with Sadness, etc. The detailed analysis of the emotional dialogue acts (EDAs) and annotated datasets are being made available at the SECURE EU Project website.
We adopted the neural architectures based on Bothe et al. bothe2018discourse where two variants are: non-context model (classifying at utterance level) and context model (recognizing the dialogue act of the current utterance given a few preceding utterances). From conversational analysis using dialogue acts in Bothe et al. bothe2018interspeech, we learned that the preceding two utterances contribute significantly to recognizing the dialogue act of the current utterance. Hence, we adapt this setting for the context model and create a pool of annotators using recurrent neural networks (RNNs). RNNs can model the contextual information in the sequence of words of an utterance and in the sequence of utterances of a dialogue. Each word in an utterance is represented with a word embedding vector of dimension 1024. We use the word embedding vectors from pre-trained ELMo (Embeddings from Language Models) embeddings BIBREF22. We have a pool of five neural annotators as shown in Figure FIGREF6. Our online tool called Discourse-Wizard is available to practice automated dialogue act labeling. In this tool we use the same neural architectures but model-trained embeddings (while, in this work we use pre-trained ELMo embeddings, as they are better performant but computationally and size-wise expensive to be hosted in the online tool). The annotators are:
n this work, we apply an automated neural ensemble annotation process for dialogue act labeling. Several neural models are trained with the Switchboard Dialogue Act (SwDA) Corpus BIBREF9, BIBREF10 and used for inferring dialogue acts on the emotion datasets. We ensemble five model output labels by checking majority occurrences (most of the model labels are the same) and ranking confidence values of the models.
We adopted the neural architectures based on Bothe et al. bothe2018discourse where two variants are: non-context model (classifying at utterance level) and context model (recognizing the dialogue act of the current utterance given a few preceding utterances).
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How many models were used?
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The answers are shown as follows:
* five
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How do I see how large a dataset is in Spark?
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You can find the number of records in a DataFrame or RDD using the `count()` method. If you want to see how large it is in terms of bytes, you can find this information on the Spark UI.
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How many players are on a hockey team?
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An NHL hockey team can max a maximum of 20 players in 1 game. Typically, teams will have 12 forwards, 6 defensemen and 2 goalies, however, some teams will choose to play 11 forwards and 7 defensemen. Teams will typically have 3 forwards, 2 defensemen and a goalie on the ice at one time. Teams that commit a foul or penalty are penalized by temporarily sending one of their players to a penalty box, thus giving their opponents a man advantage, called a power play.
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Is a person with dark brown hair considered a brunette?
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Yes, a brunette is a person with brown or black hair color. It typically refers to people with darker hair color which includes black and dark brown hair. There is no separate term for people with black hair. Brunette refers to a female with darker hair and and brunet refers to a male with darker hair.
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1912.09713
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Note that the experiment based on output-length exhibits a worse accuracy than what we would expect based on its compositional divergence. One explanation for this is that the test distribution varies from the training distribution in other ways than compound divergence (namely in output length and a slightly higher atom divergence), which seems to make this split particularly difficult for the baseline architectures. To analyze the influence of the length ratio further, we compute the correlation between length ratios and accuracy of the baseline systems and compare it to the correlation between compound divergence and accuracy. We observe $R^2$ correlation coefficients between 0.11 and 0.22 for the input and output length ratios and between 0.81 and 0.88 for the compound divergence. This shows that despite the known phenomenon that the baseline systems struggle to generalize to longer lengths, the compound divergence seems to be a stronger explanation for the accuracy on different splits than the lengths ratios.
We observe $R^2$ correlation coefficients between 0.11 and 0.22 for the input and output length ratios and between 0.81 and 0.88 for the compound divergence.
|
How strong is negative correlation between compound divergence and accuracy in performed experiment?
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The answers are shown as follows:
* between 0.81 and 0.88
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null | false
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Identify which animal species is alive or extinct: Tyrannosaurus Rex, Amazon River Dolphin
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Tyrannosaurus Rex is extinct, Amazon River Dolphin is alive.
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2001.06354
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As shown in Table TABREF46, consensus dropout fusion improves the score of NDCG by around 1.0 from the score of the joint model while still yielding comparable scores for other metrics. Unlike ensemble way, consensus dropout fusion does not require much increase in the number of model parameters.
As also shown in Table TABREF46, the ensemble model seems to take the best results from each model. Specifically, the NDCG score of the ensemble model is comparable to that of the image-only model and the scores of other metrics are comparable to those of the image-history joint model. From this experiment, we can confirm that the two models are in complementary relation.
As shown in Table TABREF46, consensus dropout fusion improves the score of NDCG by around 1.0 from the score of the joint model while still yielding comparable scores for other metrics.
As also shown in Table TABREF46, the ensemble model seems to take the best results from each model.
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Which method for integration peforms better ensemble or consensus dropout fusion with shared parameters?
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The answers are shown as follows:
* ensemble model
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| null |
Why do humans fall in love?
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There are various reasons why humans fall in love: for example, because they see themselves in each other and thus find it easier to manage the relationship in terms of understanding and compatibility; some people feel attracted to those who have similar values to their own and thus the same way of feeling and acting. Still others are attracted to the physical part of the other person and think they are in love.
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| 204
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As explained in the introduction, the overarching aim of this project is to investigate in how far information conveyed in social media posts can complement more traditional research methods in clinical psychology to get insights into the recovery experience of individuals with a BD diagnosis. Therefore, we will first conduct a systematic literature review of qualitative evidence to establish a solid base of what is already known about personal recovery experiences in BD for the subsequent social media studies.
Our research questions, which regard the experiences of different populations, lend themselves to several subprojects. First, we will collect and analyse English-language data from westerners. Then, we will address ethnically diverse English-speaking populations and finally multilingual accounts. This has the advantage that we can build data processing and methodological workflows along an increase in complexity of the data collection and analysis throughout the project.
In each project phase, we will employ a mixed-methods approach to combine the advantages of quantitative and qualitative methods BIBREF52 , BIBREF53 , which is established in mental health research BIBREF54 , BIBREF55 , BIBREF56 , BIBREF57 and specifically recommended to investigate personal recovery BIBREF58 . Quantitative methods are suitable to study observable behaviour such as language and yield more generalisable results by taking into account large samples. However, they fall short of capturing the subjective, idiosyncratic meaning of socially constructed reality, which is important when studying individuals' recovery experience BIBREF59 , BIBREF22 , BIBREF23 , BIBREF60 . Therefore, we will apply an explanatory sequential research design BIBREF53 , starting with statistical analysis of the full dataset followed by a manual investigation of fewer examples, similar to `distant reading' BIBREF61 in digital humanities.
Since previous research mainly employed (semi-)structured interviews and we do not expect to necessarily find the same aspects emphasised in unstructured settings, even less so when looking at a more diverse and non-English speaking population, we will not derive hypotheses from existing recovery models for testing on the online data. Instead, we will start off with exploratory quantitative research using comparative analysis tools such as Wmatrix BIBREF62 to uncover important linguistic features, e.g., on keywords and key concepts that occur with unexpected frequency in our collected datasets relative to reference corpora. The underlying assumption is that keywords and key concepts are indicative of certain aspects of personal recovery, such as those specified in the CHIME model BIBREF24 , other previous research BIBREF22 , BIBREF23 , BIBREF60 , or novel ones. Comparing online sources with transcripts of structured interviews or subcorpora originating from different cultural backgrounds might uncover aspects that were not prominently represented in the accounts studied in prior research.
A specific challenge will be to narrow down the data to parts relevant for personal recovery, since there is no control over the discussed topics compared to structured interviews. To investigate how individuals discuss personal recovery online and what (potentially unrecorded) aspects they associate with it, without a priori narrowing down the search-space to specific known keywords seems like a chicken-and-egg problem. We propose to address this challenge by an iterative approach similar to the one taken in a corpus linguistic study of cancer metaphors BIBREF63 . Drawing on results from previous qualitative research BIBREF24 , BIBREF23 , we will compile an initial dictionary of recovery-related terms. Next, we will examine a small portion of the dataset manually, which will be partly randomly sampled and partly selected to contain recovery-related terms. Based on this, we will be able to expand the dictionary and additionally automatically annotate semantic concepts of the identified relevant text passages using a semantic tagging approach such as the UCREL Semantic Analysis System (USAS) BIBREF64 . Crucially for the multilingual aspect of the project, USAS can tag semantic categories in eight languages BIBREF8 . Then, semantic tagging will be applied to the full corpus to retrieve all text passages mentioning relevant concepts. Furthermore, distributional semantics methods BIBREF65 , BIBREF66 can be used to find terms that frequently co-occur with words from our keyword dictionary. Occurrences of the identified keywords or concepts can be quantified in the full corpus to identify the importance of the related personal recovery aspects.
Linguistic Inquiry and Word Count (LIWC) BIBREF67 is a frequently used tool in social-science text analysis to analyse emotional and cognitive components of texts and derive features for classification models BIBREF47 , BIBREF46 , BIBREF68 , BIBREF69 . LIWC counts target words organised in a manually constructed hierarchical dictionary without contextual disambiguation in the texts under analysis and has been psychometrically validated and developed for English exclusively. While translations for several languages exist, e.g., Dutch BIBREF9 , and it is questionable to what extent LIWC concepts can be transferred to other languages and cultures by mere translation. We therefore aim to apply and develop methods that require less manual labour and are applicable to many languages and cultures. One option constitute unsupervised methods, such as topic modelling, which has been applied to explore cultural differences in mental-health related online data already BIBREF37 , BIBREF36 . The Differential Language Analysis ToolKit (DLATK) BIBREF70 facilitates social-scientific language analyses, including tools for preprocessing, such as emoticon-aware tokenisers, filtering according to meta data, and analysis, e.g. via robust topic modelling methods.
Furthermore, emotion and sentiment analysis constitute useful tools to investigate the emotions involved in talking about recovery and identify factors that facilitate or hinder it. There are many annotated datasets to train supervised classifiers BIBREF71 , BIBREF3 for these actively researched NLP tasks. Machine learning methods were found to usually outperform rule-based approaches based on look-ups in dictionaries such as LIWC. Again, most annotated resources are English, but state of the art approaches based on multilingual embeddings allow transferring models between languages BIBREF4 .
Linguistic Inquiry and Word Count (LIWC) (Pennebaker et al., 2015) is a frequently used tool in social-science text analysis to analyse emotional and cognitive components of texts and derive features for classification models (Cohan et al., 2018; Sekulic et al. ´ , 2018; Tackman et al., 2018; Wang and Jurgens, 2018).
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What is the Linguistic Inquiry and Word Count?
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It is a frequently used tool in social-science text analysis to analyse emotional and cognitive components of texts and derive features for classification models.
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| 104
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Following the setting of titovcrosslingual, we evaluate only on the arguments that were correctly identified, as the incorrectly identified arguments do not have any gold semantic labels. Evaluation is done using the metric proposed by lang2011unsupervised, which has 3 components: (i) Purity (PU) measures how well an induced cluster corresponds to a single gold role, (ii) Collocation (CO) measures how well a gold role corresponds to a single induced cluster, and (iii) F1 is the harmonic mean of PU and CO. For each predicate, let INLINEFORM0 denote the total number of argument instances, INLINEFORM1 the instances in the induced cluster INLINEFORM2 , and INLINEFORM3 the instances having label INLINEFORM4 in gold annotations. INLINEFORM5 , INLINEFORM6 , and INLINEFORM7 . The score for each predicate is weighted by the number of its argument instances, and a weighted average is computed over all the predicates.
Following the setting of Titov and Klementiev (2012b), we evaluate only on the arguments that were correctly identified, as the incorrectly identified arguments do not have any gold semantic labels.
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Why do the authors only evaluate the arguments that were correctly identified?
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As the incorrectly identified arguments do not have any gold semantic labels.
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Which of the following is a type of fruit: cucumber, broccoli, orange, or apple?
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Among the options listed, orange and apple are both fruits. Cucumber and broccoli are vegetables.
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Which months have 30 days?
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April, June, September and November have 30 days.
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Tell me whether these are hockey teams or soccer teams: Anaheim Ducks, Calgary Flames, Brighton & Hove Albion, Edmonton Oilers, Arsenal, Aston Villa, Brentford, Los Angeles Kings, San Jose Sharks, Seattle Kraken, Crystal Palace, Everton, Vancouver Canucks, Vegas Golden Knights, Chelsea.
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Anaheim Ducks is a hockey team.
Calgary Flames is a hockey team.
Brighton & Hove Albion is a soccer team.
Edmonton Oilers is a hockey team.
Arsenal is a soccer team.
Aston Villa is a soccer team.
Brentford is a soccer team.
Los Angeles Kings is a hockey team.
San Jose Sharks is a hockey team.
Seattle Kraken is a hockey team.
Crystal Palace is a soccer team.
Everton is a soccer team.
Vancouver Canucks is a hockey team.
Vegas Golden Knights is a hockey team.
Chelsea is a soccer team.
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Mount Everest is Earth's highest mountain above sea level, located in the Mahalangur Himal sub-range of the Himalayas. The China–Nepal border runs across its summit point. Its elevation of 8,848.86 m was most recently established in 2020 by the Chinese and Nepali authorities.
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Where is Mount Everest located ?
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Mount Everest is located in the Mahalangur Himal sub-range of the Himalayas. It is bordered by China and Nepal which runs across its summit points.
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Northeast India (officially the North Eastern Region (NER)) is the easternmost region of India representing both a geographic and political administrative division of the country. It comprises eight states—Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland and Tripura (commonly known as the "Seven Sisters"), and the "brother" state Sikkim.
The region shares an international border of 5,182 kilometres (3,220 mi) (about 99 percent of its total geographical boundary) with several neighbouring countries – 1,395 kilometres (867 mi) with Tibet in the north, 1,640 kilometres (1,020 mi) with Myanmar in the east, 1,596 kilometres (992 mi) with Bangladesh in the south-west, 97 kilometres (60 mi) with Nepal in the west, and 455 kilometres (283 mi) with Bhutan in the north-west. It comprises an area of 262,230 square kilometres (101,250 sq mi), almost 8 percent of that of India. The Siliguri Corridor connects the region to the rest of mainland India.
The states of North Eastern Region are officially recognised under the North Eastern Council (NEC), constituted in 1971 as the acting agency for the development of the north eastern states. Long after induction of NEC, Sikkim formed part of the North Eastern Region as the eighth state in 2002. India's Look-East connectivity projects connect Northeast India to East Asia and ASEAN. Guwahati city in Assam is called the Gateway to the North East and is the largest metropolis in North East India.
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Name the states of Northeast India.
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Northeast India comprises eight states—Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland, Tripura, and Sikkim.
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One major festival is that of the Feast of the Holy Cross, which dates back far into the colonial period. It has a community cross made from basalt called the “Cruz de Huaquechula” which is incrusted with various relics related to the original cross of Christ. It is said to be so heavy that it cannot be lifted unless the bearers pray and the church plays a special melody on its bells. The festival lasts nine days beginning in April ending on May 3, with mass, traditional dance, food and music played by bands playing wind instruments, and fireworks set off from large frames in the shape bulls. The event has been named part of the Cultural Heritage of Puebla. During this event, traditional festive wear such as that of the charro and the China Poblana can be seen.
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Here is some reference information about Huaquechula, When does the feast of the Holy Cross in Huaquechula start?
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April 25th. It ends on May 3rd, and lasts 9 days.
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Best vacation in Mexico
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Cancun, best beach vacation in Mexico.You can visit Playa Del Carman, Tulum and one of the new Wondars in the world.
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Combat readiness is a condition of the armed forces and their constituent units and formations, warships, aircraft, weapon systems or other military technology and equipment to perform during combat military operations, or functions consistent with the purpose for which they are organized or designed, or the managing of resources and personnel training in preparation for combat.
Most armed forces maintain varying levels of readiness by the troops to engage in combat due to economic considerations which vary from minutes to months. In modern armed forces troops designated special forces are usually those kept at the highest state of readiness for combat, and are often alerted only a few hours before being committed to combat. Where time is of the essence in military action being initiated, the troops, such as pilots of interceptor aircraft, may be kept in constant state of combat readiness.
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Given a reference text about combat readiness, tell me which troops are kept most ready for combat.
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Designated special forces are normally kept at the highest state of readiness for combat.
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1908.06379
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FLOAT SELECTED: Table 3: Dependency parsing on PTB and CTB.
FLOAT SELECTED: Table 4: Comparison of constituent parsing on PTB.
FLOAT SELECTED: Table 5: Comparison of constituent parsing on CTB.
Tables TABREF17, TABREF18 and TABREF19 compare our model to existing state-of-the-art, in which indicator Separate with our model shows the results of our model learning constituent or dependency parsing separately, (Sum) and (Concat) respectively represent the results with the indicated input token representation setting. On PTB, our model achieves 93.90 F1 score of constituent parsing and 95.91 UAS and 93.86 LAS of dependency parsing. On CTB, our model achieves a new state-of-the-art result on both constituent and dependency parsing. The comparison again suggests that learning jointly in our model is superior to learning separately. In addition, we also augment our model with ELMo BIBREF48 or a larger version of BERT BIBREF49 as the sole token representation to compare with other pre-training models. Since BERT is based on sub-word, we only take the last sub-word vector of the word in the last layer of BERT as our sole token representation $x_i$. Moreover, our single model of BERT achieves competitive performance with other ensemble models.
FLOAT SELECTED: Table 3: Dependency parsing on PTB and CTB.
FLOAT SELECTED: Table 4: Comparison of constituent parsing on PTB.
FLOAT SELECTED: Table 5: Comparison of constituent parsing on CTB.
On PTB, our model achieves 93.90 F1 score of constituent parsing and 95.91 UAS and 93.86 LAS of dependency parsing.
On CTB, our model achieves a new state-of-the-art result on both constituent and dependency parsing. The comparison again suggests that learning jointly in our model is superior to learning separately.
|
What are the performances obtained for PTB and CTB?
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The answers are shown as follows:
* . On PTB, our model achieves 93.90 F1 score of constituent parsing and 95.91 UAS and 93.86 LAS of dependency parsing.
* On CTB, our model achieves a new state-of-the-art result on both constituent and dependency parsing.
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1910.08502
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In this paper, we experimentally showed that end-to-end approaches and different orthographic units were rather suitable to model the French language. RNN-transducer was found specially competitive with character units compared to other end-to-end approaches. Among the two orthographic units, subword was found beneficial for most methods to address the problems described in section SECREF14 and retain information on ambiguous patterns in French. Extending with language models, we could obtain promising results compared to traditional phone-based systems. The best performing systems being for character unit the RNN-transducer with additional attention module, achieving 7.8% in terms of CER and 17.6% on WER. For subword units, classic RNN-transducer, RNN-transducer with attention and joint CTC-attention show comparable performance on subword error rate and WER, with the first one being slightly better on WER ($17.4\%$) and the last one having a lower error rate on subword ($14.5\%$).
The best performing systems being for character unit the RNN-transducer with additional attention module, achieving 7.8% in terms of CER and 17.6% on WER. For subword units, classic RNN-transducer, RNN-transducer with attention and joint CTC-attention show comparable performance on subword error rate and WER, with the first one being slightly better on WER ($17.4\%$) and the last one having a lower error rate on subword ($14.5\%$).
|
Which model have the smallest Character Error Rate and which have the smallest Word Error Rate?
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The answers are shown as follows:
* character unit the RNN-transducer with additional attention module
* For subword units, classic RNN-transducer, RNN-transducer with attention and joint CTC-attention show comparable performance
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Beavers build dams and lodges using tree branches, vegetation, rocks and mud; they chew down trees for building material. Dams restrict water flow, and lodges serve as shelters. Their infrastructure creates wetlands used by many other species, and because of their effect on other organisms in the ecosystem, beavers are considered a keystone species.
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How do beaver damns benefit the environment?
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Although beavers build damns to protect themselves they also help generate wetlands which are natural habits for a lot of other species. They are a key contributor to the ecosystem they live in.
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Grove Park Sidings is a large stabling complex for suburban commuter trains in Lewisham, South East London. It is situated approximately halfway between Hither Green Station and Grove Park station.
Grove Park Sidings were previously named Hither Green Sidings, until sidings closer to Hither Green were built and took that name, causing the site to be renamed Grove Park Sidings.
It consists of two sets of sidings, one on either side of the Main line which are linked by a pedestrian footbridge. On the Down side are Bramdean sidings and the large carriage shed, whilst on the Up side are St Mildreds sidings. There is also a carriage washing plant on the inlet road of Down sidings.
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Given this paragraph about a train stabling complex, what was the original name of the Grove Park Sidings?
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Hither Green Sidings
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1706.07179
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There are three main components to the model: 1) input encoder 2) dynamic memory, and 3) output module. We will describe these three modules in details. The input encoder and output module implementations are similar to the Entity Network BIBREF17 and main novelty lies in the dynamic memory. We describe the operations executed by the network for a single example consisting of a document with $T$ sentences, where each sentence consists of a sequence of words represented with $K$ -dimensional word embeddings $\lbrace e_1, \ldots , e_N\rbrace $ , a question on the document represented as another sequence of words and an answer to the question.
There are three main components to the model: 1) input encoder 2) dynamic memory, and 3) output module. We will describe these three modules in details. The input encoder and output module implementations are similar to the Entity Network BIBREF17 and main novelty lies in the dynamic memory. We describe the operations executed by the network for a single example consisting of a document with $T$ sentences, where each sentence consists of a sequence of words represented with $K$ -dimensional word embeddings $\lbrace e_1, \ldots , e_N\rbrace $ , a question on the document represented as another sequence of words and an answer to the question.
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How is knowledge stored in the memory?
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entity memory and relational memory.
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What are the words of House Stark?
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"Winter is Coming"
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What are the names of five of the best Pokemon cards and why?
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1) Tera Arcanine EX (it's very powerful)
2) Tera Gyarados EX (it's also very powerful)
3) Snorlax VMAX (has a lot of health points)
4) Illustrators Pikachu (it's worth a lot of money)
5) First edition Shaddowless Holo Charizard (because it's a very rare card)
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The national flag of the United States of America (often referred to as the American flag, or the U.S. flag) consists of thirteen equal horizontal stripes of red (top and bottom) alternating with white, with a blue rectangle in the canton (referred to specifically as the "union") bearing fifty small, white, five-pointed stars arranged in nine offset horizontal rows, where rows of six stars (top and bottom) alternate with rows of five stars. The 50 stars on the flag represent the 50 U.S. states, and the 13 stripes represent the thirteen British colonies that declared independence from Great Britain, and became the first states in the U.S. Nicknames for the flag include the Stars and Stripes, Old Glory, and the Star-Spangled Banner.
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What is does the stars represent on the United States of America flag?
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The 50 stars on the United States of America flag represent the 50 U.S. states.
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