niryuu/sni-each-converted huggingface dataset:
CoinCollector BIBREF8 is a class of text-based games where the objective is to find and collect a coin from a specific location in a given set of connected rooms . The agent wins the game after it collects the coin, at which point (for the first and only time) a reward of +1 is received by the agent. The environment parses only five admissible commands (go north, go east, go south, go west, and take coin) made by two worlds; CookingWorld BIBREF14 in this challenge, there are 4,440 games with 222 different levels of difficulty, with 20 games per level of difficulty, each with different entities and maps. The goal of each game is to cook and eat food from a given recipe, which includes the task of collecting ingredients (e.g. tomato, potato, etc.), objects (e.g. knife), and processing them according to the recipe (e.g. cook potato, slice tomato, etc.). The parser of each game accepts 18 verbs and 51 entities with a predefined grammar, but the overall size of the vocabulary of the observations is 20,000. In Appendix SECREF36 we provide more details about the levels and the games' grammar. 
 Question: On what Text-Based Games are experiments performed?
Output: CoinCollector  CookingWorld
Sentence classification combines natural language processing (NLP) with machine learning to identify trends in sentence structure, BIBREF14 , BIBREF15 . Each tweet is converted to a numeric word vector in order to identify distinguishing features by training an NLP classifier on a validated set of relevant tweets. The classifier acts as a tool to sift through ads, news, and comments not related to patients. Our scheme combines a logistic regression classifier, BIBREF16 , with a Convolutional Neural Network (CNN), BIBREF17 , BIBREF18 , to identify self-reported diagnostic tweets.

It is important to be wary of automated accounts (e.g. bots, spam) whose large output of tweets pollute relevant organic content, BIBREF19 , and can distort sentiment analyses, BIBREF20 . Prior to applying sentence classification, we removed tweets containing hyperlinks to remove automated content (some organic content is necessarily lost with this strict constraint). Twitter allows users to spread content from other users via `retweets'. We also removed these posts prior to classification to isolate tweets authored by patients. We also accounted for non-relevant astrological content by removing all tweets containing any of the following horoscope indicators: `astrology',`zodiac',`astronomy',`horoscope',`aquarius',`pisces',`aries',`taurus',`leo',`virgo',`libra', and `scorpio'. We preprocessed tweets by lowercasing and removing punctuation. We also only analyzed tweets for which Twitter had identified `en' for the language English. 
 Question: What machine learning and NLP methods were used to sift tweets relevant to breast cancer experiences?
Output: ML  logistic regression classifier combined with a Convolutional Neural Network (CNN) to identify self-reported diagnostic tweets.
NLP methods:  tweet conversion to numeric word vector,  removing tweets containing hyperlinks, removing "retweets", removing all tweets containing horoscope indicators,  lowercasing and  removing punctuation.
The input of our model are the words in the input text $x[1], ... , x[n]$ and query $q[1], ... , q[n]$ . We concatenate pre-trained word embeddings from GloVe BIBREF40 and character embeddings trained by CharCNN BIBREF41 to represent input words. The $2d$ -dimension embedding vectors of input text $x_1, ... , x_n$ and query $q_1, ... , q_n$ are then fed into a Highway Layer BIBREF42 to improve the capability of word embeddings and character embeddings as

$$\begin{split} g_t &= {\rm sigmoid}(W_gx_t+b_g) \\ s_t &= {\rm relu } (W_xx_t+b_x) \\ u_t &= g_t \odot s_t + (1 - g_t) \odot x_t~. \end{split}$$ (Eq. 18) The same Highway Layer is applied to $q_t$ and produces $v_t$ . Next, $u_t$ and $v_t$ are fed into a Bi-Directional Long Short-Term Memory Network (BiLSTM) BIBREF44 respectively in order to model the temporal interactions between sequence words: Then we feed $\mathbf {U}$ and $\mathbf {V}$ into the attention flow layer BIBREF27 to model the interactions between the input text and query. Therefore, we introduce Self-Matching Layer BIBREF29 in our model as

$$\begin{split} o_t &= {\rm BiLSTM}(o_{t-1}, [h_t, c_t]) \\ s_j^t &= w^T {\rm tanh}(W_hh_j+\tilde{W_h}h_t)\\ \alpha _i^t &= {\rm exp}(s_i^t)/\Sigma _{j=1}^n{\rm exp}(s_j^t)\\ c_t &= \Sigma _{i=1}^n\alpha _i^th_i ~. \end{split}$$ (Eq. 20) Finally we feed the embeddings $\mathbf {O} = [o_1, ... , o_n]$ into a Pointer Network BIBREF39 to decode the answer sequence as

$$\begin{split} p_t &= {\rm LSTM}(p_{t-1}, c_t) \\ s_j^t &= w^T {\rm tanh}(W_oo_j+W_pp_{t-1})\\ \beta _i^t &= {\rm exp}(s_i^t)/\Sigma _{j=1}^n{\rm exp}(s_j^t)\\ c_t &= \Sigma _{i=1}^n\beta _i^to_i~. \end{split}$$ (Eq. 21) Therefore, the probability of generating the answer sequence $\textbf {a}$ is as follows

$${\rm P}(\textbf {a}|\mathbf {O}) = \prod _t {\rm P}(a^t | a^1, ... , a^{t-1}, \mathbf {O})~.$$ (Eq. 23) 
 Question: What QA models were used?
Output: A pointer network decodes the answer from a bidirectional LSTM with attention flow layer and self-matching layer, whose inputs come from word and character embeddings of the query and input text fed through a highway layer.
In this paper, we introduce automatic `drunk-texting prediction' as a computational task. Given a tweet, the goal is to automatically identify if it was written by a drunk user.  
 Question: Do the authors equate drunk tweeting with drunk texting? 
Output: Yes
Concretely, we selected gold standards that fit our problem definition and were published in the years 2016 to 2019, have at least $(2019 - publication\ year) \times 20$ citations, and bucket them according to the answer selection styles as described in Section SECREF4 
 Question: What modern MRC gold standards are analyzed?
Output: fit our problem definition and were published in the years 2016 to 2019, have at least $(2019 - publication\ year) \times 20$ citations
We used two parallel corpora: Asian Scientific Paper Excerpt Corpus (ASPEC) BIBREF0 and NTCIR PatentMT Parallel Corpus BIBREF1 . 
 Question: What parallel corpus did they use?
Output: Asian Scientific Paper Excerpt Corpus (ASPEC) BIBREF0 NTCIR PatentMT Parallel Corpus BIBREF1
In order to understand the latent topics of those #MeToo tweets for college followers, we first utilize Latent Dirichlet Allocation (LDA) to label universal topics demonstrated by the users. Since certain words frequently appear in those #MeToo tweets (e.g., sexual harassment, men, women, story, etc.), we transform our corpus using TF-IDF, a term-weighting scheme that discounts the influence of common terms. 
 Question: How are the topics embedded in the #MeToo tweets extracted?
Output: Using Latent Dirichlet Allocation on TF-IDF transformed from the corpus
We evaluate our newly proposed models and related baselines in several low-resource settings across different languages with real, distantly supervised data with non-synthetic noise.  
 Question: How they evaluate their approach?
Output: They  evaluate newly proposed models in several low-resource settings across different languages with real, distantly supervised data with non-synthetic noise
the attention heads in the proposed adaptively sparse Transformer can specialize more and with higher confidence In particular, we analyzed how the attention heads in the proposed adaptively sparse Transformer can specialize more and with higher confidence. 
 Question: How does their model improve interpretability compared to softmax transformers?
Output: the attention heads in the proposed adaptively sparse Transformer can specialize more and with higher confidence
For the training data, we use Daily Mail news articles released by BIBREF9 .  
 Question: what dataset was used?
Output: Daily Mail news articles released by BIBREF9
GPT-2 achieves the highest score and the $n$-gram the lowest. 
 Question: Which of the model yields the best performance?
Output: GPT-2
The task of humor identification in social media texts is analyzed as a classification problem and several machine learning classification models are used. 
 Question: What experiments were carried out on the corpus?
Output: task of humor identification in social media texts is analyzed as a classification problem
Table TABREF14 show the results obtained for proposed s2sL approach in comparison to that of MLP for the tasks of Speech/Music and Neutral/Sad classification, by considering different proportions of training data. 
 Question: Which models/frameworks do they compare to?
Output: MLP
The involved algorithms are detailed as follows.

CNN-C denotes the CNN with (Chinese) character embedding.

CNN-W denotes the CNN with (Chinese) word embedding.

CNN-Lex-C denotes the algorithm which also integrates polar words in CNN which is proposed by Shin et al. BIBREF24 . The (Chinese) character embedding is used.

CNN-Lex-W denotes the algorithm which also integrates polar words in CNN which is proposed by Shin et al. BIBREF24 . The (Chinese) word embedding is used.

Bi-LSTM-C denotes the BI-LSTM with (Chinese) character embedding.

Bi-LSTM-W denotes the Bi-LSTM with (Chinese) word embedding.

Lex-rule denotes the rule-based approach shows in Fig. 1. This approach is unsupervised.

BOW denotes the conventional algorithm which is based of bag-of-words features. 
 Question: What are the other models they compare to?
Output: CNN-C CNN-W CNN-Lex-C CNN-Lex-W Bi-LSTM-C  Bi-LSTM-W Lex-rule BOW
Semantic Retrieval: We treated the neural semantic retrieval at both the paragraph and sentence level as binary classification problems with models' parameters updated by minimizing binary cross entropy loss. To be specific, we fed the query and context into BERT as:

We applied an affine layer and sigmoid activation on the last layer output of the [$\mathit {CLS}$] token which is a scalar value. The parameters were updated with the objective function:

where $\hat{p}_i$ is the output of the model, $\mathbf {T}^{p/s}_{pos}$ is the positive set and $\mathbf {T}^{p/s}_{neg}$ is the negative set. As shown in Fig. FIGREF2, at sentence level, ground-truth sentences were served as positive examples while other sentences from upstream retrieved set were served as negative examples. Similarly at the paragraph-level, paragraphs having any ground-truth sentence were used as positive examples and other paragraphs from the upstream term-based retrieval processes were used as negative examples. 
 Question: How do they train the retrieval modules?
Output: We treated the neural semantic retrieval at both the paragraph and sentence level as binary classification problems with models' parameters updated by minimizing binary cross entropy loss.
This layer consists of two submodules: the Multi-Head Attention (MHA) and the Point-wise Convolution Transformation (PCT). 
 Question: Do they use multi-attention heads?
Output: Yes
Word embedding models, and BERT in particular, contain vast amounts of information collected through the course of their training. BERT Base for instance, has 110 Million parameters and was trained on both Wikipedea Corpus and BooksCorpus BIBREF0, a combined collection of over 3 Billion words. 
 Question: What is the language model pre-trained on?
Output: Wikipedea Corpus and BooksCorpus
We carried out the experimentation with a range of classifiers of different types: Support Vector Machines (SVM), Gaussian Naive Bayes, Multinomial Naive Bayes, Decision Trees, Random Forests and a Maximum Entropy classifier. 
 Question: What model do they train?
Output: Support Vector Machines (SVM), Gaussian Naive Bayes, Multinomial Naive Bayes, Decision Trees, Random Forests and a Maximum Entropy classifier
As we show below, STransE performs better than the SE and TransE models and other state-of-the-art link prediction models on two standard link prediction datasets WN18 and FB15k, so it can serve as a new baseline for KB completion. 
 Question: What datasets are used to evaluate the model?
Output: WN18, FB15k
The experiment dataset comes from Microsoft Research (MSR) . It contains three domains: movie, taxi, and restaurant. The total count of dialogues per domain and train/valid/test split is reported in Table TABREF11. At every turn both user and agent acts are annotated, we use only the agent side as targets in our experiment. The acts are ordered in the dataset (each output sentence aligns with one act). 
 Question: What datasets are used for training/testing models? 
Output: Microsoft Research dataset containing movie, taxi and restaurant domains.
We compare our approach with two baseline vision-based methods proposed in BIBREF6 , BIBREF7 , which measure the similarity of two sets of global visual features for bilingual lexicon induction:

CNN-mean: taking the similarity score of the averaged feature of the two image sets.

CNN-avgmax: taking the average of the maximum similarity scores of two image sets. 
 Question: Which vision-based approaches does this approach outperform?
Output: CNN-mean CNN-avgmax
Multiple turns: The average number of utterances per dialog is about 23 which ensures context-rich language behaviors. 
 Question: What is the average number of turns per dialog?
Output: The average number of utterances per dialog is about 23
For generating a poem from images we use an existing actor-critic architecture BIBREF1. For Shakespearizing modern English texts, we experimented with various types of sequence to sequence models. We use a sequence-to-sequence model which consists of a single layer unidrectional LSTM encoder and a single layer LSTM decoder and pre-trained retrofitted word embeddings shared between source and target sentences. Since a pair of corresponding Shakespeare and modern English sentences have significant vocabulary overlap we extend the sequence-to-sequence model mentioned above using pointer networks BIBREF11 that provide location based attention and have been used to enable copying of tokens directly from the input. 
 Question: What models are used for painting embedding and what for language style transfer?
Output: generating a poem from images we use an existing actor-critic architecture various types of sequence to sequence models
Conversation Based Features
These features are devoted to exploit the peculiar characteristics of the dataset, which have a tree structure reflecting the conversation thread.

Text Similarity to Source Tweet: Jaccard Similarity of each tweet with its source tweet.

Text Similarity to Replied Tweet: the degree of similarity between the tweet with the previous tweet in the thread (the tweet is a reply to that tweet).

Tweet Depth: the depth value is obtained by counting the node from sources (roots) to each tweet in their hierarchy. 
 Question: What conversation-based features are used?
Output: Text Similarity to Source Tweet Text Similarity to Replied Tweet Tweet Depth
Building Extractive CNN/Daily Mail 
 Question: What is the problem with existing metrics that they are trying to address?
Output: Answer with content missing: (whole introduction) However, recent
studies observe the limits of ROUGE and find in
some cases, it fails to reach consensus with human.
judgment (Paulus et al., 2017; Schluter, 2017).
We use the guidelines of the state-of-the-art approach of BIBREF12 that achieved the highest inflection accuracy in the latest SIGMORPHON 2019 morphological inflection shared task. Our models are implemented in DyNet BIBREF13. We trained an encoder-decoder model that receives the form as character-level input, encodes it with a BiLSTM encoder, and then an attention enhanced decoder BIBREF14 outputs the corresponding sequence of morphological tags, implemented in DyNet. We trained a character level encoder-decoder model, similar to the above-mentioned inflection system, implemented in DyNet. 
 Question: What system is used as baseline?
Output: DyNet
Clustering was performed separately for each specialty of doctors. We also examined the distribution of doctors' IDs in the obtained clusters. It turned out that some clusters covered almost exactly descriptions written by one doctor. This situation took place in the specialties where clusters are separated with large margins (e.g. psychiatry, pediatrics, cardiology). 
 Question: Do they explore similarity of texts across different doctors?
Output: Yes
To be able to employ the trained model, test sets are first translated to English via machine translation and then inference takes place.   For machine translation, Google translation API is used. 
 Question: how did the authors translate the reviews to other languages?
Output: Using Google translation API.
after a shared CLWE space has been induced, the task is to retrieve target language translations for a test set of source language words retrieve target language translations for a test set of source language words BLI is cast as a ranking task in our BLI setup with only one correct translation for each “query” word, MAP is equal to mean reciprocal rank (MRR) 
 Question: How does BLI measure alignment quality?
Output: we use mean average precision (MAP) as the main evaluation metric
We evaluate our approach for six target languages: French (fr), Russian (ru), Arabic (ar), Chinese (zh), Hindi (hi), and Vietnamese (vi).  
 Question: What languages are the model transferred to?
Output: French (fr), Russian (ru), Arabic (ar), Chinese (zh), Hindi (hi), and Vietnamese (vi)
We assessed the proposed models on four different NLG domains: finding a restaurant, finding a hotel, buying a laptop, and buying a television. The Restaurant and Hotel were collected in BIBREF4 , while the Laptop and TV datasets have been released by BIBREF22 with a much larger input space but only one training example for each DA so that the system must learn partial realization of concepts and be able to recombine and apply them to unseen DAs.  
 Question: Does the model evaluated on NLG datasets or dialog datasets?
Output: NLG datasets
Python package twitterscraper is used to scrap tweets from twitter. 10,478 tweets from the past two years from domains like `sports', `politics', `entertainment' were extracted. 
 Question: Where did the texts in the corpus come from?
Output: tweets from the past two years from domains like `sports', `politics', `entertainment'
Table TABREF31 shows the F-score, the Area Under the ROC Curve (AUC) and the mean accuracy (ACC) of the proposed models when different inputs are available. Fig. FIGREF32 shows the Precision vs Recall plot and the ROC curve (which plots the True Positive Rate vs the False Positive Rate) of the different models. 
 Question: What metrics are used to benchmark the results?
Output: F-score Area Under the ROC Curve (AUC) mean accuracy (ACC) Precision vs Recall plot ROC curve (which plots the True Positive Rate vs the False Positive Rate)
The D2V model has been rated 80 times as "bad relevance" while the pmra returned only 24 times badly relevant documents.  
 Question: How better are results for pmra algorithm  than Doc2Vec in human evaluation? 
Output: The D2V model has been rated 80 times as "bad relevance" while the pmra returned only 24 times badly relevant documents.
One common point in all the approaches yet has been the use of only textual features available in the dataset. Our model not only incorporates textual features, modeled using BiLSTM and augmented with an attention mechanism, but also considers related images for the task. 
 Question: What are the differences with previous applications of neural networks for this task?
Output: This approach considers related images
Fenerbahçe We have decided to consider tweets about popular sports clubs as our domain for stance detection.  Hence we have determined our targets as Galatasaray (namely Target-1) and Fenerbahçe (namely, Target-2) which are two of the most popular football clubs in Turkey.  Then, we have annotated the stance information in the tweets for these targets as Favor or Against. For the purposes of the current study, we have not annotated any tweets with the Neither class. 
 Question: How were the tweets annotated?
Output: tweets are annotated with only Favor or Against for two targets - Galatasaray and Fenerbahçe
The Machine Learning techniques used varied from Maximum Entropy Classifiers (BIBREF4) to Support Vector Machines (BIBREF5,BIBREF6,BIBREF7,BIBREF8), while the deep learning approaches included Recursive Neural Networks (BIBREF9,BIBREF10), Convolutional Neural Networks (BIBREF11) and most recently transfer learning-based architectures like Bidirectional Encoder Representation from Transformers (BERT) (BIBREF12). Figures FIGREF1 and FIGREF1 contain a summary of the papers addressing speculation detection and scope resolution (BIBREF13, BIBREF5, BIBREF9, BIBREF3, BIBREF14, BIBREF15, BIBREF16, BIBREF17, BIBREF6, BIBREF11, BIBREF18, BIBREF10, BIBREF19, BIBREF7, BIBREF4, BIBREF8). 
 Question: What were the baselines?
Output: varied from Maximum Entropy Classifiers (BIBREF4) to Support Vector Machines (BIBREF5,BIBREF6,BIBREF7,BIBREF8) Recursive Neural Networks (BIBREF9,BIBREF10), Convolutional Neural Networks (BIBREF11) and most recently transfer learning-based architectures like Bidirectional Encoder Representation from Transformers (BERT) (BIBREF12)
We have used a dataset of 14 TDs to conduct our experiments. 
 Question: Which dataset(s) do they use?
Output: 14 TDs BIBREF15
After removing 135 questions during the validation, the final dataset comprises 13,939 questions, 3,827 of which require commonsense knowledge (i.e. 27.4%).  
 Question: what is the size of their dataset?
Output: 13,939
Concerning logical inferences, monotonicity reasoning BIBREF6 , BIBREF7 , which is a type of reasoning based on word replacement, requires the ability to capture the interaction between lexical and syntactic structures. 
 Question: What is monotonicity reasoning?
Output: a type of reasoning based on word replacement, requires the ability to capture the interaction between lexical and syntactic structures
The overall performance of BERT was significantly better than the other models, having the lowest average difference in accuracy of 22.5 points. 
 Question: Which model generalized the best?
Output: BERT
Based on the resulting 1.8 million lists of about 169,000 distinct user ids, we compute a topic model with INLINEFORM0 topics using Latent Dirichlet Allocation BIBREF3 . For each of the user ids, we extract the most probable topic from the inferred user id-topic distribution as cluster id. This results in a thematic cluster id for most of the user ids in our background corpus grouping together accounts such as American or German political actors, musicians, media websites or sports clubs (see Table TABREF17 ).  
 Question: What topic clusters are identified by LDA?
Output: Clusters of Twitter user ids from accounts of American or German political actors, musicians, media websites or sports club
BIBREF3 also use bilingual crowd workers, but the studies supporting the use of crowdsourcing for MT evaluation were performed with older MT systems, and their findings may not carry over to the evaluation of contemporary higher-quality neural machine translation (NMT) systems. In addition, the MT developers to which crowd workers were compared are usually not professional translators.  We hypothesise that an evaluation of sentences in isolation, as applied by BIBREF3, precludes raters from detecting translation errors that become apparent only when inter-sentential context is available, and that they will judge MT quality less favourably when evaluating full documents. BIBREF3 used all source texts of the WMT 2017 Chinese–English test set for their experiments, of which only half were originally written in Chinese;  
 Question: What was the weakness in Hassan et al's evaluation design?
Output: MT developers to which crowd workers were compared are usually not professional translators, evaluation of sentences in isolation prevents raters from detecting translation errors, used not originally written Chinese test set
In all our experiments, we used the out-of-the-box BERT models without any task-specific fine-tuning. 
 Question: How does their model differ from BERT?
Output: Their model does not differ from BERT.
We collected tweets associated to a dozen US mainstream news websites, i.e. most trusted sources described in BIBREF18, with the Streaming API, and we referred to Hoaxy API BIBREF16 for what concerns tweets containing links to 100+ US disinformation outlets. We filtered out articles associated to less than 50 tweets. The resulting dataset contains overall $\sim $1.7 million tweets for mainstream news, collected in a period of three weeks (February 25th, 2019-March 18th, 2019), which are associated to 6,978 news articles, and $\sim $1.6 million tweets for disinformation, collected in a period of three months (January 1st, 2019-March 18th, 2019) for sake of balance of the two classes, which hold 5,775 distinct articles. Diffusion censoring effects BIBREF14 were correctly taken into account in both collection procedures. We provide in Figure FIGREF4 the distribution of articles by source and political bias for both news domains. For what concerns the Italian scenario we first collected tweets with the Streaming API in a 3-week period (April 19th, 2019-May 5th, 2019), filtering those containing URLs pointing to Italian official newspapers websites as described in BIBREF22; these correspond to the list provided by the association for the verification of newspaper circulation in Italy (Accertamenti Diffusione Stampa). We instead referred to the dataset provided by BIBREF23 to obtain a set of tweets, collected continuously since January 2019 using the same Twitter endpoint, which contain URLs to 60+ Italian disinformation websites. In order to get balanced classes (April 5th, 2019-May 5th, 2019), we retained data collected in a longer period w.r.t to mainstream news. In both cases we filtered out articles with less than 50 tweets; overall this dataset contains $\sim $160k mainstream tweets, corresponding to 227 news articles, and $\sim $100k disinformation tweets, corresponding to 237 news articles. We provide in Figure FIGREF5 the distribution of articles according to distinct sources for both news domains.  
 Question: Which two news domains are country-independent?
Output: mainstream news and disinformation
For evaluation, the Visual Dialog task employs four metrics. NDCG is the primary metric of the Visual Dialog Challenge which considers multiple similar answers as correct ones. The other three are MRR, recall@k, and mean rank where they only consider the rank of a single answer. 
 Question: What metrics are used in challenge?
Output: NDCG MRR recall@k mean rank
For instance, we observed that winter words were together and far away from summer ones. Week days were grouped as well and far from week-end days. 
 Question: What geometric properties do embeddings display?
Output: Winter and summer words formed two separate clusters. Week day and week-end day words also formed separate clusters.
Three different datasets have been used to train our models: the Toronto book corpus, Wikipedia sentences and tweets.  Our Sent2Vec models also on average outperform or are at par with the C-PHRASE model, despite significantly lagging behind on the STS 2014 WordNet and News subtasks. This observation can be attributed to the fact that a big chunk of the data that the C-PHRASE model is trained on comes from English Wikipedia, helping it to perform well on datasets involving definition and news items.  
 Question: Do they report results only on English data?
Output: Yes
There have been many taxonomies for dialogue acts: speech acts BIBREF14 refer to the utterance, not only to present information but to the action at is performed. Speech acts were later modified into five classes (Assertive, Directive, Commissive, Expressive, Declarative) BIBREF15. There are many such standard taxonomies and schemes to annotate conversational data, and most of them follow the discourse compositionality. These schemes have proven their importance for discourse or conversational analysis BIBREF16. During the increased development of dialogue systems and discourse analysis, the standard taxonomy was introduced in recent decades, called Dialogue Act Markup in Several Layers (DAMSL) tag set. According to DAMSL, each DA has a forward-looking function (such as Statement, Info-request, Thanking) and a backwards-looking function (such as Accept, Reject, Answer) BIBREF17. 
 Question: How were dialogue act labels defined?
Output: Dialogue Act Markup in Several Layers (DAMSL) tag set
For a word belonging to any one of the word-groups representing these concepts, the modified cost term favors an increase for the value of this word's embedding vector dimension corresponding to the concept that the particular word belongs to.  
 Question: Along which dimension do the semantically related words take larger values?
Output: dimension corresponding to the concept that the particular word belongs to
We then train a paragraph vector model using the Document to Vector (Doc2Vec) framework BIBREF7 on the whole set (13 million) of preprocessed text records, although training on smaller sets (1 million) also produces good results. 
 Question: Which text embedding methodologies are used?
Output: Document to Vector (Doc2Vec)
To develop a generalizable model that avoids overfitting, we perform feature selection using statistical tests and all relevant ensemble learning models. It adds randomness to the data by creating shuffled copies of all features (shadow feature), and then trains Random Forest classifier on the extended data. 
 Question: What model is used to achieve 5% improvement on F1 for identifying depressed individuals on Twitter?
Output: Random Forest classifier
Two-talker overlapped speech is artificially generated by mixing these waveform segments. To maximize the speech overlap, we developed a procedure to mix similarly sized segments at around 0dB. First, we sort the speech segments by length. Then, we take segments in pairs, zero-padding the shorter segment so both have the same length. These pairs are then mixed together to create the overlapped speech data. 
 Question: How are the two datasets artificially overlapped?
Output: we sort the speech segments by length we take segments in pairs, zero-padding the shorter segment so both have the same length These pairs are then mixed together
The BD-4SK-ASR Dataset ::: The Language Model
We created the language from the transcriptions. The model was created using CMUSphinx in which (fixed) discount mass is 0.5, and backoffs are computed using the ratio method. The model includes 283 unigrams, 5337 bigrams, and 6935 trigrams. 
 Question: What are the results of the experiment?
Output: They were able to create a language model from the dataset, but did not test.
To learn better graph representation of multi-party dialogues, we adopt the dialogues with 8-15 utterances and 3-7 speakers. To simplify the task, we filter the dialogues with long sentences (more than 20 words). Finally, we obtain 52,053 dialogues and 460,358 utterances. 
 Question: How large is the proposed dataset?
Output: we obtain 52,053 dialogues and 460,358 utterances
The Swissmetro dataset consists of survey data collected on the trains between St. Gallen and Geneva, Switzerland, during March 1998.  
 Question: What datasets are used for evaluation?
Output: Swissmetro dataset
. A highly dynamic community constantly shifts interests from one time window to another, and these temporal variations are reflected in its use of volatile language. Formally, we define the dynamicity of a community INLINEFORM0 as the average volatility of all utterances in INLINEFORM1 .  
 Question: How do the authors measure how temporally dynamic a community is?
Output: the average volatility of all utterances
To investigate the extent to which the text obtained from the two platforms of Yahoo! Answers and Twitter reflect the true attributes of neighbourhoods, we first study whether there are significant, strong and meaningful correlations between the terms present in each corpus and the many neighbourhood attributes through the Pearson correlation coefficient $\rho $ . 
 Question: What do the correlation demonstrate? 
Output: the extent to which the text obtained from the two platforms of Yahoo! Answers and Twitter reflect the true attributes of neighbourhoods
In this work, we have restricted ourselves to the same datasets as BIBREF7 . These include nine (real-valued) numerical features, which are latitude, longitude, elevation, population, and five climate related features (avg. temperature, avg. precipitation, avg. solar radiation, avg. wind speed, and avg. water vapor pressure). In addition, 180 categorical features were used, which are CORINE land cover classes at level 1 (5 classes), level 2 (15 classes) and level 3 (44 classes) and 116 soil types (SoilGrids). Note that each location should belong to exactly 4 categories: one CORINE class at each of the three levels and a soil type. 
 Question: what dataset is used in this paper?
Output:  the same datasets as BIBREF7
As a warm-up procedure, the first 100 posts were annotated by two annotators (the author and the supervisor) and the results compared. The remainder of the annotation task was performed by the author, resulting in 3600 annotated samples. 
 Question: Who were the annotators?
Output: the author and the supervisor
For all the classifiers, our feature combination outperforms the baselines (considering only unigram features) as well as BIBREF3 , with the MILR classifier getting an F-score improvement of 3.7% and Kappa difference of 0.08. We also achieve an improvement of 2% over the baseline, using SVM classifier, when we employ our feature set. We also observe that the gaze features alone, also capture the differences between sarcasm and non-sarcasm classes with a high-precision but a low recall. 
 Question: What other evaluation metrics are looked at?
Output: F-score Kappa
CFQ contains 239,357 English question-answer pairs that are answerable using the public Freebase data. 
 Question: How big is new question answering dataset?
Output: 239,357 English question-answer pairs
We have collected total 3500 questions from the Internet and other sources such as books of general knowledge questions, history etc. The corpus contains the questions and the classes each question belongs to. 
 Question: what datasets did they use?
Output: Dataset of total 3500 questions from the Internet and other sources such as books of general knowledge questions, history, etc.
The clinical notes we used for the experiment are provided by domain experts, consisting of 1,160 physician logs of Medical ICU admission requests at a tertiary care center affiliated to Mount Sanai. The enriched corpus contains 42,506 Wikipedia articles, each of which corresponds to one candidate, 6 research papers and 2 critical care medicine textbooks, besides our raw ICU data. 
 Question: Which dataset do they use to build their model?
Output: 1,160 physician logs of Medical ICU admission requests 42,506 Wikipedia articles 6 research papers and 2 critical care medicine textbooks
For English dataset, we crawl 687 cybersecurity articles from a collection of advanced persistent threats (APT) reports which are published from 2008 to 2018.  
 Question: Where are the cybersecurity articles used in the model sourced from?
Output:  from a collection of advanced persistent threats (APT) reports which are published from 2008 to 2018
Table TABREF32 shows the perplexity of the models on each of the test sets. As expected, each model performs better on the test set they have been trained on. When applied to a different test set, both see an increase in perplexity. However, the Leipzig model seems to have more trouble generalizing: its perplexity nearly doubles on the SwissCrawl test set and raises by twenty on the combined test set. 
 Question: How is language modelling evaluated?
Output: perplexity of the models
The results indicate that most performance gains come from words embeddings, style, and morality features. Other features (emotion and sentiment) show lower importance: nevertheless, they still improve the overall system performance (on average 0.35% Macro-F$_1$ improvement) 
 Question: Based on this paper, what is the more predictive set of features to detect fake news?
Output: words embeddings, style, and morality features
In order to verify the effectiveness of our method (i.e., Multi-linear attention) replacing multi-head attention in Transformer, we carry out two NLP tasks named language modeling (LM) and neural machine translation (NMT). Then, we test different model configurations on the PTB BIBREF25 , WikiText-103 BIBREF26 and One-Billion Word benchmark BIBREF27 datasets and report the results in Table 1 and Table 2 . In this task, we have trained the Transformer model BIBREF2 on WMT 2016 English-German dataset BIBREF36 . 
 Question: What datasets or tasks do they conduct experiments on?
Output: Language Modeling (LM) PTB BIBREF25 , WikiText-103 BIBREF26 and One-Billion Word benchmark BIBREF27 datasets neural machine translation (NMT) WMT 2016 English-German dataset
To understand the reason behind the drop in performance, first we need to understand how BERT processes input text data. BERT uses WordPiece tokenizer to tokenize the text. WordPiece tokenizer utterances based on the longest prefix matching algorithm to generate tokens . The tokens thus obtained are fed as input of the BERT model. When it comes to tokenizing noisy data, we see a very interesting behaviour from WordPiece tokenizer. Owing to the spelling mistakes, these words are not directly found in BERT’s dictionary. Hence WordPiece tokenizer tokenizes noisy words into subwords. However, it ends up breaking them into subwords whose meaning can be very different from the meaning of the original word. Often, this changes the meaning of the sentence completely, therefore leading to substantial dip in the performance. 
 Question: What is the reason behind the drop in performance using BERT for some popular task?
Output: Hence WordPiece tokenizer tokenizes noisy words into subwords. However, it ends up breaking them into subwords whose meaning can be very different from the meaning of the original word. Often, this changes the meaning of the sentence completely, therefore leading to substantial dip in the performance.
We evaluate our proposed model on the SNAP (Stanford Network Analysis Project) Amazon review datasets BIBREF8, which contain not only reviews and ratings, but also golden summaries. We empirically compare different methods using Amazon SNAP Review Dataset BIBREF20, which is a part of Stanford Network Analysis Project. For fair comparison with previous work, we adopt the same partitions used by previous work BIBREF6, BIBREF7, which is, for each domain, the first 1000 samples are taken as the development set, the following 1000 samples as the test set, and the rest as the training set. 
 Question: Which review dataset do they use?
Output: SNAP (Stanford Network Analysis Project)
To create minimal pairs exemplifying a wide array of linguistic contrasts, it is necessary to artificially generate all datasets. This ensures both that we have sufficient unacceptable examples, and that the data is fully controlled, allowing for repeated isolation of a single linguistic phenomenon in each paradigm BIBREF30. The data generation scripts use a basic template to create each paradigm, pulling from a vocabulary of over 3000 words annotated for morphological, syntactic, and semantic features needed to create grammatical and semantically felicitous sentences. Examples SECREF6 and SECREF6 show one such template for the `acceptable' and `unacceptable' sentences within a pair: the sole difference between them is the underlined word, which differs only in whether the anaphor agrees in number with its antecedent. Our generation codebase and scripts are freely available. This generation procedure is not without limitations, and despite the very detailed vocabulary we use, implausible sentences are occasionally generated (e.g., `Sam ran around some glaciers'). In these cases, though, both the acceptable and unacceptable sentences will be equally implausible given world knowledge, so any difference in the probability assigned to them is still due to the intended grammatical contrast. 
 Question: How is the data automatically generated?
Output:  The data generation scripts use a basic template to create each paradigm, pulling from a vocabulary of over 3000 words annotated for morphological, syntactic, and semantic features needed to create grammatical and semantically felicitous sentences.
The term `propaganda' derives from propagare in post-classical Latin, as in “propagation of the faith" BIBREF1, and thus has from the beginning been associated with an intentional and potentially multicast communication; only later did it become a pejorative term. It was pragmatically defined in the World War II era as “the expression of an opinion or an action by individuals or groups deliberately designed to influence the opinions or the actions of other individuals or groups with reference to predetermined ends" BIBREF2. 
 Question: How is "propaganda" defined for the purposes of this study?
Output: an intentional and potentially multicast communication “the expression of an opinion or an action by individuals or groups deliberately designed to influence the opinions or the actions of other individuals or groups with reference to predetermined ends"
Also, note that the performance for this task is not expected to achieve a perfect accuracy, as there may be situations where more than one action is reasonable, and also because writers tell a story playing with elements such as surprise or uncertainty. 
 Question: Why do they think this task is hard?  What is the baseline performance?
Output: 1. there may be situations where more than one action is reasonable, and also because writers tell a story playing with elements such as surprise or uncertainty.
2. Macro F1 = 14.6 (MLR, length 96 snippet)
Weighted F1 = 31.1 (LSTM, length 128 snippet)
In future work we also intend to add these types of studies to the ERP predictions.

Discussion 
 Question: Which two pairs of ERPs from the literature benefit from joint training?
Output: Answer with content missing: (Whole Method and Results sections) Self-paced reading times widely benefit ERP prediction, while eye-tracking data seems to have more limited benefit to just the ELAN, LAN, and PNP ERP components.
Select:
- ELAN, LAN
- PNP ERP
Nonetheless, the main caveat of this basic pre-training is that the source encoder is trained to be used by an English decoder, while the target decoder is trained to use the outputs of an English encoder — not of a source encoder. 
 Question: Is pivot language used in experiments English or some other language?
Output: Yes
We also conduct two human evaluations in order to assess (a) which type of summary participants prefer (we compare extractive and abstractive systems) and (b) how much key information from the document is preserved in the summary (we ask participants to answer questions pertaining to the content in the document by reading system summaries). 
 Question: Do they use other evaluation metrics besides ROUGE?
Output: Yes
We used the Twitter definition of hateful conduct in the first survey. This definition was presented at the beginning, and again above every tweet. 
 Question: What definition was one of the groups was shown?
Output: Twitter definition of hateful conduct
One example illustrates that the mistakes the model makes can be associated with changes in the party policy. 
 Question: Do changes in policies of the political actors account for all of the mistakes the model made?
Output: Yes
In our paper, we opt for a state-of-the-art character bigram CNN classifier BIBREF4 , and investigate various ways in which the discourse information can be featurized and integrated into the CNN.  
 Question: What was the previous state-of-the-art?
Output: character bigram CNN classifier
This kind of error affects the precision and recall for the “troll” and “engage” classes. A solution to this problem may be the inclusion of longer parts of the conversation.  
 Question: What potential solutions are suggested?
Output:  inclusion of longer parts of the conversation
We view different varieties of Arabic as different domains, and hence introduce a simple, yet effective, `in-domain' training measure where we further pre-train BERT on a dataset closer to task domain (in that it involves dialectal tweet data). 
 Question: What in-domain data is used to continue pre-training?
Output: dialectal tweet data
As far as we know, all existing systems treat this task as a pipeline of two separate subtasks, i.e., event extraction and temporal relation classification, and they also assume that gold events are given when training the relation classifier BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5. Specifically, they built end-to-end systems that extract events first and then predict temporal relations between them (Fig. FIGREF1). In these pipeline models, event extraction errors will propagate to the relation classification step and cannot be corrected afterwards. Our first contribution is the proposal of a joint model that extracts both events and temporal relations simultaneously (see Fig. FIGREF1). 
 Question: Is this the first paper to propose a joint model for event and temporal relation extraction?
Output: Yes
Each classifier is implemented with the following specifications:

Naïve Bayes (NB): Multinomial NB with additive smoothing constant 1

Logistic Regression (LR): Linear LR with L2 regularization constant 1 and limited-memory BFGS optimization

Support Vector Machine (SVM): Linear SVM with L2 regularization constant 1 and logistic loss function

Random Forests (RF): Averaging probabilistic predictions of 10 randomized decision trees

Gradient Boosted Trees (GBT): Tree boosting with learning rate 1 and logistic loss function Along with traditional machine learning approaches, we investigate neural network based models to evaluate their efficacy within a larger dataset. In particular, we explore Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and their variant models. 
 Question: What learning models are used on the dataset?
Output: Naïve Bayes (NB) Logistic Regression (LR) Support Vector Machine (SVM) Random Forests (RF) Gradient Boosted Trees (GBT)  Convolutional Neural Networks (CNN) Recurrent Neural Networks (RNN)
We use the public financial news dataset released by BIBREF4, which is crawled from Reuters and Bloomberg over the period from October 2006 to November 2013. We conduct our experiments on predicting the Standard & Poor’s 500 stock (S&P 500) index and its selected individual stocks, obtaining indices and prices from Yahoo Finance. Detailed statistics of the training, development and test sets are shown in Table TABREF8. We report the final results on test set after using development set to tune some hyper-parameters. 
 Question: What is dataset used for news-driven stock movement prediction?
Output: the public financial news dataset released by BIBREF4
Abstract meaning representation BIBREF0 , or AMR for short, allows us to do that with the inclusion of most of the shallow-semantic natural language processing (NLP) tasks that are usually addressed separately, such as named entity recognition, semantic role labeling and co-reference resolution. 
 Question: Which subtasks do they evaluate on?
Output:  entity recognition, semantic role labeling and co-reference resolution
Each passage was labelled by 3 unique annotators. 
 Question: How many annotators were there?
Output: 3
In addition to the majority baseline, we also compare our results with a lexicon-based approach. For experimental results, we report majority baseline for each language where the majority baseline corresponds to a model's accuracy if it always predicts the majority class in the dataset. 
 Question: what are the baselines?
Output: majority baseline lexicon-based approach