niryuu/sni-each-converted huggingface dataset:
All corpora provide datasets/splits for answer selection, whereas only (WikiQA, SQuAD) and (WikiQA, SelQA) provide datasets for answer extraction and answer triggering, respectively. SQuAD is much larger in size although questions in this corpus are often paraphrased multiple times. On the contrary, SQuAD's average candidates per question ( INLINEFORM0 ) is the smallest because SQuAD extracts answer candidates from paragraphs whereas the others extract them from sections or infoboxes that consist of bigger contexts. Although InfoboxQA is larger than WikiQA or SelQA, the number of token types ( INLINEFORM1 ) in InfoboxQA is smaller than those two, due to the repetitive nature of infoboxes.

All corpora show similar average answer candidate lengths ( INLINEFORM0 ), except for InfoboxQA where each line in the infobox is considered a candidate. SelQA and SQuAD show similar average question lengths ( INLINEFORM1 ) because of the similarity between their annotation schemes. It is not surprising that WikiQA's average question length is the smallest, considering their questions are taken from search queries. InfoboxQA's average question length is relatively small, due to the restricted information that can be asked from the infoboxes. InfoboxQA and WikiQA show the least question-answer word overlaps over questions and answers ( INLINEFORM2 and INLINEFORM3 in Table TABREF2 ), respectively. In terms of the F1-score for overlapping words ( INLINEFORM4 ), SQuAD gives the least portion of overlaps between question-answer pairs although WikiQA comes very close. 
 Question: How do they analyze contextual similaries across datasets?
Output: They compare the tasks that the datasets are suitable for, average number of answer candidates per question, number of token types, average answer candidate lengths, average question lengths, question-answer word overlap.
27,534 messages were left after filtering. This is the data set that is used for experimentation. 
 Question: What is the size of the labelled dataset?
Output: 27,534 messages
In this work, we propose to augment the integer linear programming (ILP)-based summarization framework with a low-rank approximation of the co-occurrence matrix, and further evaluate the approach on a broad range of datasets exhibiting high lexical diversity. 
 Question: What do they constrain using integer linear programming?
Output: low-rank approximation of the co-occurrence matrix
Note that the BoW/SVM model being a linear predictor relying directly on word frequency statistics, it lacks expressive power in comparison to the CNN model which additionally learns intermediate hidden layer representations and convolutional filters. Moreover the CNN model can take advantage of the semantic similarity encoded in the distributed word2vec representations, while for the BoW/SVM model all words are “equidistant” in the bag-of-words semantic space 
 Question: According to the authors, why does the CNN model exhibit a higher level of explainability?
Output: CNN model which additionally learns intermediate hidden layer representations and convolutional filters. Moreover the CNN model can take advantage of the semantic similarity encoded in the distributed word2vec representations
BIBREF5 adopt a machine learning approach for NER. Their NER system extracts medical problems, tests and treatments from discharge summaries and progress notes. The dataset used is the i2b2 2010 challenge dataset.  
 Question: Does the paper explore extraction from electronic health records?
Output: Yes
More specifically, we observe the impact of: (i) pre-trained word embeddings BIBREF11, BIBREF12, recurrent BIBREF13 and transformer-based sentence encoders BIBREF14 as question representation strategies; (ii) distinct convolutional neural networks used for visual feature extraction BIBREF15, BIBREF16, BIBREF17; and (iii) standard fusion strategies, as well as the importance of two main attention mechanisms BIBREF18, BIBREF19. 
 Question: What type of experiments are performed?
Output: pre-trained word embeddings BIBREF11, BIBREF12 recurrent BIBREF13 transformer-based sentence encoders BIBREF14 distinct convolutional neural networks standard fusion strategies  two main attention mechanisms BIBREF18, BIBREF19
The GENIA Corpus BIBREF3 contains biomedical abstracts from the PubMed database. We use GENIA technical term annotations 3.02, which cover linguistic expressions to entities of interest in molecular biology, e.g. proteins, genes and cells. CoNLL2003 BIBREF14 is a standard NER dataset based on the Reuters RCV-1 news corpus. It covers named entities of type person, location, organization and misc.

For testing the overall annotation performance, we utilize CoNLL2003-testA and a 50 document split from GENIA.  
 Question: what standard dataset were used?
Output: The GENIA Corpus  CoNLL2003
We focus here on the Europarl domain, for which we have ample data in several languages, and use as in-domain training data the Europarl corpus BIBREF5 for two translation directions: English INLINEFORM0 German and English INLINEFORM1 French. 
 Question: what language pairs are explored?
Output: English-German, English-French.
Surprisingly, using the best QA model (bert-large-wwm) does not lead to the best correlations with human judgments. On CNN/DM, bert-large-wwm slightly underperforms bert-base and bert-large. 
 Question: What models are evaluated with QAGS?
Output: bert-large-wwm bert-base bert-large
We used dataset, HEOT obtained from one of the past studies done by Mathur et al. where they annotated a set of cleaned tweets obtained from twitter for the conversations happening in Indian subcontinent. A labelled dataset for a corresponding english tweets were also obtained from a study conducted by Davidson et al. 
 Question: How is the dataset collected?
Output: A labelled dataset for a corresponding english tweets were also obtained from a study conducted by Davidson et al HEOT obtained from one of the past studies done by Mathur et al
Routines were developed to simulate input data based on the authors experience with real healthcare data. The reasons for this choice were twofold: One, healthcare data can be high in incidental complexity, requiring one-off code to handle unusual inputs, but not necessarily in such a way as to significantly alter the fundamental engineering choices in a semantic enrichment engine such as this one. Two, healthcare data is strictly regulated, and the process for obtaining access to healthcare data for research can be cumbersome and time-consuming.

A simplified set of input data, in a variety of different formats that occur frequently in a healthcare setting, was used for simulation. 
 Question: What type of simulations of real-time data feeds are used for validaton?
Output: simplified set of input data, in a variety of different formats that occur frequently in a healthcare setting
Our neural network model mainly consists of three parallel LSTM BIBREF21 layers. 
 Question: What architecture has the neural network?
Output: three parallel LSTM BIBREF21 layers
For estimating the sense of the word INLINEFORM0 in a sentence, we search for such a synset INLINEFORM1 that maximizes the cosine similarity to the sentence vector: DISPLAYFORM0 
 Question: What measure of semantic similarity is used?
Output: cosine similarity
Since BERT has already achieved the state-of-the-art performance of question-answering, in this section we compare our proposed model with state-of-the-art question answering models (i.e. QANet BIBREF39) and BERT-Base BIBREF26. As BERT has two versions: BERT-Base and BERT-Large, due to the lack of computational resource, we can only compare with BERT-Base model instead of BERT-Large. 
 Question: What are strong baseline models in specific tasks?
Output: state-of-the-art question answering models (i.e. QANet BIBREF39) and BERT-Base BIBREF26
As we mention in the Section 2, CAEVO is the current state-of-the-art system for feature-based temporal event relation extraction BIBREF10 . It's widely used as the baseline for evaluating TB-Dense data. We adopt it as our baseline for evaluating CaTeRS and RED datasets. 
 Question: What were the traditional linguistic feature-based models?
Output: CAEVO
This corpus has multiple versions, and we choose the following two versions as their test dataset has significantly larger number of instances of multiple relation tuples with overlapping entities. (i) The first version is used by BIBREF6 (BIBREF6) (mentioned as NYT in their paper) and has 24 relations. We name this version as NYT24. (ii) The second version is used by BIBREF11 (BIBREF11) (mentioned as NYT10 in their paper) and has 29 relations. 
 Question: Are there datasets with relation tuples annotated, how big are datasets available?
Output: Yes
Furthermore, we do not restrict ourselves to a test set of sequences of fixed lengths during testing. Rather, we exhaustively enumerate all the sequences in a language by their lengths and then go through the sequences in the test set one by one until our network errs $k$ times, thereby providing a more fine-grained evaluation criterion of its generalization capabilities. To study the effect of various length distributions on the learning capability and speed of LSTM models, we experimented with four discrete probability distributions supported on bounded intervals (Figure 2 ) to sample the lengths of sequences for the languages.  
 Question: Are the unobserved samples from the same distribution as the training data?
Output: No
We evaluate the performance of our proposed method using two criteria: i) rank-correlation BIBREF25 to evaluate visual grounding and ii) accuracy to evaluate question answering. Intuitively, rank-correlation measures the similarity between human and model attention maps under a rank-based metric. A high rank-correlation means that the model is `looking at' image areas that agree to the visual information used by a human to answer the same question. In terms of accuracy of a predicted answer INLINEFORM0 is evaluated by: DISPLAYFORM0

 
 Question: How do they measure the correlation between manual groundings and model generated ones?
Output: rank-correlation BIBREF25
We evaluated the phrase compositionality models on the adjective–noun and noun–noun phrase similarity tasks compiled by Mitchell2010, using the same evaluation scheme as in the original work. Spearman's INLINEFORM0 between phrasal similarities derived from our compositional functions and the human annotators (computed individually per annotator and then averaged across all annotators) was the evaluation measure. 
 Question: How do they score phrasal compositionality?
Output: Spearman's INLINEFORM0 between phrasal similarities derived from our compositional functions and the human annotators
With BERT, two fully unsupervised tasks are performed. The Masked Language Model and the Next Sentence Prediction (NSP).

For this study, the NSP is used as a proxy for the relevance of response. 
 Question: was bert used?
Output: Yes
In the instances where a model thinks all historical samples should be considered equally important in a sequential analysis task, we must look elsewhere for a computationally inexpensive means to understand what happened at the stopping point. 
 Question: Can their method of creating more informative visuals be applied to tasks other than turn taking in conversations?
Output: computationally inexpensive means to understand what happened at the stopping point
In addition, in order to estimate annotation reliability and provide for better evaluation, every question in the test set is answered by at least two additional experts. 
 Question: Are the answers double (and not triple) annotated?
Output: Yes
We create the graph using all data, and training set tweets have an initial language label distribution. A naïve approach to building the tweet-tweet subgraph requires O( INLINEFORM0 ) comparisons, measuring the similarity of each tweet with all others. Instead, we performed INLINEFORM1 -nearest-neighbour classification on all tweets, represented as a bag of unigrams, and compared each tweet and the top- INLINEFORM2 neighbours. We use Junto (mad) BIBREF5 to propagate labels from labelled to unlabelled nodes. Upon convergence, we renormalise label scores for initially unlabelled nodes to find the value of INLINEFORM4 . 
 Question: How are labels propagated using this approach?
Output: We use Junto (mad) BIBREF5 to propagate labels from labelled to unlabelled nodes.
At training time, we use the plan-to-DFS mapping to perform the correct sequence of traversals, and train a neural classifier to act as a controller, choosing which action to perform at each step. 
 Question: How is neural planning component trained?
Output: plan-to-DFS mapping to perform the correct sequence of traversals, and train a neural classifier to act as a controller
We observe interesting hidden correlation in data. Fig. FIGREF24 has Topic 2 as selected topic. Topic 2 contains top-4 co-occurring keywords "vegan", "yoga", "job", "every_woman" having the highest term frequency. We can infer different things from the topic that "women usually practice yoga more than men", "women teach yoga and take it as a job", "Yogi follow vegan diet". We would say there are noticeable correlation in data i.e. `Yoga-Veganism', `Women-Yoga'. Women-Yoga 
 Question: What other interesting correlations are observed?
Output: Women-Yoga
We have collected more than 3,934,610 million tweets so far. 
 Question: How big is the dataset?
Output: more than 3,934,610 million tweets
The question generation model provides each candidate answer with a score by measuring semantic relevance between the question and the generated question based on the semantics of the candidate answer.  
 Question: Where is a question generation model used?
Output: The question generation model provides each candidate answer with a score by measuring semantic relevance between the question and the generated question based on the semantics of the candidate answer.
An octave convolutional layer BIBREF0 factorizes the output feature maps of a convolutional layer into two groups. The resolution of the low-frequency feature maps is reduced by an octave – height and width dimensions are divided by 2. In this work, we explore spatial reduction by up to 3 octaves – dividing by $2^t$, where $t=1,2,3$ – and for up to 4 groups. We refer to such a layer as a multi-octave convolutional (MultiOctConv) layer, and an example with three groups and reductions of one and two octaves is depicted in Fig. FIGREF1. 
 Question: How is octave convolution concept extended to multiple resolutions and octaves?
Output: The resolution of the low-frequency feature maps is reduced by an octave – height and width dimensions are divided by 2. In this work, we explore spatial reduction by up to 3 octaves – dividing by $2^t$, where $t=1,2,3$ – and for up to 4 groups. We refer to such a layer as a multi-octave convolutional (MultiOctConv) layer,
In this paper, we present an extraction-then-synthesis framework for machine reading comprehension shown in Figure 1 , in which the answer is synthesized from the extraction results. We build an evidence extraction model to predict the most important sub-spans from the passages as evidence, and then develop an answer synthesis model which takes the evidence as additional features along with the question and passage to further elaborate the final answers. 
 Question: What two components are included in their proposed framework?
Output: evidence extraction and answer synthesis
Our model contains five independent decoders, one for each image in the sequence. This allows each decoder to learn a specific language model for each position of the sequence. 
 Question: Do the decoder LSTMs all have the same weights?
Output: No
We trained a logistic regression baseline model (line 1 in Table TABREF10 ) using character ngrams and word unigrams using TF*IDF weighting BIBREF13 , to provide a baseline since HAR has no reported results. For the SR and HATE datasets, the authors reported their trained best logistic regression model's results on their respective datasets. 
 Question: what was the baseline?
Output: logistic regression
Our study focused primarily on English tweets, since this was the language of our diagnostic training sample. 
 Question: Do the authors report results only on English datasets?
Output: Yes
The PolyResponse restaurant search is currently available in 8 languages and for 8 cities around the world: English (Edinburgh), German (Berlin), Spanish (Madrid), Mandarin (Taipei), Polish (Warsaw), Russian (Moscow), Korean (Seoul), and Serbian (Belgrade). 
 Question: In what 8 languages is PolyResponse engine used for restourant search and booking system?
Output: English, German, Spanish, Mandarin, Polish, Russian, Korean and Serbian
According to the above analysis According to the above analysis, we proposed a weighted version of DIRL to address the problem caused by the shift of $\rm {P}(\rm {Y})$ to DIRL. The key idea of this framework is to first align $\rm {P}(\rm {Y})$ across domains before performing domain-invariant learning, and then take account the shift of $\rm {P}(\rm {Y})$ in the label prediction procedure. Specifically, it introduces a class weight $\mathbf {w}$ to weigh source domain examples by class. Based on the weighted source domain, the domain shift problem is resolved in two steps.  The motivation behind this practice is to adjust data distribution of the source domain or the target domain to alleviate the shift of $\rm {P}(\rm {Y})$ across domains before applying DIRL. Consider that we only have labels of source domain data, we choose to adjust data distribution of the source domain. To achieve this purpose, we introduce a trainable class weight $\mathbf {w}$ to reweigh source domain examples by class when performing DIRL, with $\mathbf {w}_i > 0$. Specifically, we hope that:

and we denote $\mathbf {w}^*$ the value of $\mathbf {w}$ that makes this equation hold.  We shall see that when $\mathbf {w}=\mathbf {w}^*$, DIRL is to align $\rm {P}_S(G(\rm {X})|\rm {Y})$ with $\rm {P}_T(G(\rm {X})|\rm {Y})$ without the shift of $\rm {P}(\rm {Y})$. According to our analysis, we know that due to the shift of $\rm {P}(\rm {Y})$, there is a conflict between the training objects of the supervised learning $\mathcal {L}_{sup}$ and the domain-invariant learning $\mathcal {L}_{inv}$. And the conflict degree will decrease as $\rm {P}_S(\rm {Y})$ getting close to $\rm {P}_T(\rm {Y})$. Therefore, during model training, $\mathbf {w}$ is expected to be optimized toward $\mathbf {w}^*$ since it will make $\rm {P}(\rm {Y})$ of the weighted source domain close to $\rm {P}_T(\rm {Y})$, so as to solve the conflict. 
 Question: How are different domains weighted in WDIRL?
Output: To achieve this purpose, we introduce a trainable class weight $\mathbf {w}$ to reweigh source domain examples by class when performing DIRL, with $\mathbf {w}_i > 0$
Stanford - Twitter Sentiment Corpus (STS Corpus): STS Corpus contains 1,600K training tweets collected by a crawler from BIBREF0 . BIBREF0 constructed a test set manually with 177 negative and 182 positive tweets. The Stanford test set is small. However, it has been widely used in different evaluation tasks BIBREF0 BIBREF5 BIBREF13 .

Sanders - Twitter Sentiment Corpus: This dataset consists of hand-classified tweets collected by using search terms: INLINEFORM0 , #google, #microsoft and #twitter. We construct the dataset as BIBREF14 for binary classification.

Health Care Reform (HCR): This dataset was constructed by crawling tweets containing the hashtag #hcr BIBREF15 . Task is to predict positive/negative tweets BIBREF14 . Table IV shows the result of our model for sentiment classification against other models. We compare our model performance with the approaches of BIBREF0 BIBREF5 on STS Corpus.  For Sanders and HCR datasets, we compare results with the model of BIBREF14 that used a ensemble of multiple base classifiers (ENS) such as NB, Random Forest (RF), SVM and Logistic Regression (LR).  
 Question: Are results reported only on English datasets?
Output: Yes
For this purpose, we construct a corpus of 172,815 articles from Tagalog Wikipedia which we call WikiText-TL-39 BIBREF18. 
 Question: What other datasets are used?
Output: WikiText-TL-39
It is interesting to observe that the baseline model amplifies the bias in the training data set as measured by INLINEFORM0 and INLINEFORM1 . From measurements using the described bias metrics, our method effectively mitigates bias in language modelling without a significant increase in perplexity. 
 Question: how is mitigation of gender bias evaluated?
Output: Using INLINEFORM0 and INLINEFORM1
Our user study compares the correctness of three scenarios:

Parser correctness - our baseline is the percentage of examples where the top query returned by the semantic parser was correct.

User correctness - the percentage of examples where the user selected a correct query from the top-7 generated by the parser.

Hybrid correctness - correctness of queries returned by a combination of the previous two scenarios. The system returns the query marked by the user as correct; if the user marks all queries as incorrect it will return the parser's top candidate. 
 Question: Do they conduct a user study where they show an NL interface with and without their explanation?
Output: No
While domain transfer is not new, compared to prior summarization studies BIBREF6, BIBREF7, our training (news) and tuning (student reflection) domains are quite dissimilar, and the in-domain data is small. 
 Question: Is the student reflection data very different from the newspaper data?  
Output: Yes
The baseline model BIBREF3 is implemented with a recurrent neural network based encoder-decoder framework. 
 Question: Do they compare against Noraset et al. 2017?
Output: Yes
Given the fact that the research on offensive language detection has to a large extent been focused on the English language, we set out to explore the design of models that can successfully be used for both English and Danish. 
 Question: What is the challenge for other language except English
Output: not researched as much as English
We find that all the factors we tested can qualitatively affect how a model generalizes on the question formation task. These factors are the type of recurrent unit, the type of attention, and the choice of sequential vs. tree-based model structure. 
 Question: What architectural factors were investigated?
Output: type of recurrent unit type of attention choice of sequential vs. tree-based model structure
For our study, we consider that a tweet went viral if it was retweeted more than 1000 times. 
 Question: What is the threshold for determining that a tweet has gone viral?
Output: 1000
Clean-labeled Datasets. We use three clean labeled datasets. The first one is the movie sentence polarity dataset from BIBREF19. The other two datasets are laptop and restaurant datasets collected from SemEval-2016 . Noisy-labeled Training Datasets. For the above three domains (movie, laptop, and restaurant), we collected 2,000 reviews for each domain from the same review source. 
 Question: What is the dataset used to train the model?
Output:  movie sentence polarity dataset from BIBREF19 laptop and restaurant datasets collected from SemEval-201 we collected 2,000 reviews for each domain from the same review source
In spite of the windowing approach, the class distribution is still skewed, and an accuracy metric would reflect the particular class distribution in our data set. Therefore, we adopt the unweighted average recall (UAR) metric commonly used in emotion classification research. UAR is a reweighted accuracy where the samples of both classes are weighted equally in aggregate. UAR thus simulates a uniform class distribution. To match the objective, our classifiers are trained on appropriately weighted training data. Note that chance performance for UAR is by definition 50%, making results more comparable across different data sets. 
 Question: What they use as a metric of finding hot spots in meeting?
Output: unweighted average recall (UAR) metric
We also looked at where there was complete agreement by all annotators that a triple extraction was incorrect. In total there were 138 of these triples originating from 76 unique sentences.  
 Question: What is the most common error type?
Output: all annotators that a triple extraction was incorrect
As a starting point, we used the DIP corpus BIBREF37 , a collection of 49 clusters of 100 web pages on educational topics (e.g. bullying, homeschooling, drugs) with a short description of each topic. 
 Question: Which collections of web documents are included in the corpus?
Output: DIP corpus BIBREF37
However, we see that IR methods perform better than the best neural models. We do not find enough evidence to reject the null hypothesis regarding what context from the cited document should be used. 
 Question: Which baseline performs best?
Output: IR methods perform better than the best neural models
Our data has been developed by crawling and pre-processing an OSG web forum. The forum has a great variety of different groups such as depression, anxiety, stress, relationship, cancer, sexually transmitted diseases, etc. 
 Question: How did they obtain the OSG dataset?
Output: crawling and pre-processing an OSG web forum
Following BIBREF11 , we experiment on the non-anonymized version of . When generating summaries, we follow standard practice of tuning the maximum output length and disallow repeating the same trigram BIBREF27 , BIBREF14 . For this task we train language model representations on the combination of newscrawl and the training data. Table TABREF16 shows that pre-trained embeddings can significantly improve on top of a strong baseline transformer. We also compare to BIBREF26 who use a task-specific architecture compared to our generic sequence to sequence baseline. Pre-trained representations are complementary to their method. 
 Question: What other models do they compare to?
Output: BIBREF11  BIBREF26
Thus, we can distinguish two main regions or clusters in the maps. The purple background covers most of the map and represents rural regions with small, scattered population. Our analysis shows that this group of cells possesses more specific words in their lexicon. In contrast, the green and yellow cells form a second cluster that is largely concentrated on the center and along the coastline, which correspond to big cities and industrialized areas.  In these cells, the use of standard Spanish language is widespread due probably to school education, media, travelers, etc. 
 Question: Do the authors mention any possible confounds in their study?
Output: Yes
Each dataset consists of a collection of records with one QA problem per record. For each record, we include some question text, a context document relevant to the question, a set of candidate solutions, and the correct solution. The context document for each record consists of a list of ranked and scored pseudodocuments relevant to the question. Several baselines rely on the retrieved context to extract the answer to a question. For these, we refer to the fraction of instances for which the correct answer is present in the context as Search Accuracy. Naturally, the search accuracy increases as the context size increases, however at the same time reading performance decreases since the task of extracting the answer becomes harder for longer documents. 
 Question: Which retrieval system was used for baselines?
Output: The dataset comes with a ranked set of relevant documents. Hence the baselines do not use a retrieval system.
For French speech technologies, four corpora containing radio and TV broadcast are the most widely used: ESTER1 BIBREF13, ESTER2 BIBREF14, ETAPE BIBREF15 and REPERE BIBREF16. 
 Question: What are four major corpora of French broadcast?
Output: ESTER1 ESTER2 ETAPE REPERE
Given a news Twitter account, we read its tweets from the account's timeline. Then we sort the tweets by the posting date in ascending way and we split them into $N$ chunks. Each chunk consists of a sorted sequence of tweets labeled by the label of its corresponding account. 
 Question: Was the approach used in this work to detect fake news fully supervised?
Output: Yes
Future work can be improving the sensationalism scorer and investigating the applications of dynamic balancing methods between RL and MLE in textGANBIBREF23. Our work also raises the ethical questions about generating sensational headlines, which can be further explored. 
 Question: What is future work planed?
Output: ethical questions about generating sensational headlines, which can be further explored  improving the sensationalism scorer investigating the applications of dynamic balancing methods between RL and MLE
Methods ::: Combining the two methods
We further propose to use the two methods together to combine their strengths. In fact, while the length token acts as a soft constraint to bias NMT to produce short or long translation with respect to the source, actually no length information is given to the network. On the other side, length encoding leverages information about the target length, but it is agnostic of the source length. 
 Question: Do they experiment with combining both methods?
Output: Yes
The corpus of supervisor assessment has 26972 sentences.  
 Question: What is the size of the real-life dataset?
Output: 26972
The improvement when the difficult subset with expert annotations is mixed with the remaining crowd annotation is 3.5 F1 score, much larger than when a random set of expert annotations are added. 
 Question: How much higher quality is the resulting annotated data?
Output: improvement when the difficult subset with expert annotations is mixed with the remaining crowd annotation is 3.5 F1 score, much larger than when a random set of expert annotations are added
We evaluate LinkNBed and baselines on two real world knowledge graphs: D-IMDB (derived from large scale IMDB data snapshot) and D-FB (derived from large scale Freebase data snapshot). 
 Question: On what data is the model evaluated?
Output: D-IMDB (derived from large scale IMDB data snapshot) D-FB (derived from large scale Freebase data snapshot)
We carried out experiments on four Chinese NLP tasks, including Emotion Classification (EC), Named Entity Recognition (NER), Sentence Pair Matching (SPM) and Natural Language Inference (NLI). 
 Question: What benchmarks did they experiment on?
Output: Emotion Classification (EC) Named Entity Recognition (NER) Sentence Pair Matching (SPM) Natural Language Inference (NLI)
In this work, we use the datasets released by BIBREF1 and HEOT dataset provided by BIBREF0 .  This was then followed by transliteration using the Xlit-Crowd conversion dictionary and translation of each word to English using Hindi to English dictionary.  
 Question: Do all the instances contain code-switching?
Output: No
In this paper, we have shown that both human and machine translation can alter superficial patterns in data, which requires reconsidering previous findings in cross-lingual transfer learning. 
 Question: Does the professional translation or the machine translation introduce the artifacts?
Output: Yes
We acquired the data in two rounds of annotation. In the first one, we were looking for original and uncommon sentence change suggestions. 
 Question: How do they introduce language variation?
Output:  we were looking for original and uncommon sentence change suggestions
The first dataset is an expanded version of the annotated Wikipedia conversations dataset from BIBREF9. This dataset uses carefully-controlled crowdsourced labels, strictly filtered to ensure the conversations are civil up to the moment of a personal attack. This is a useful property for the purposes of model analysis, and hence we focus on this as our primary dataset. However, we are conscious of the possibility that these strict labels may not fully capture the kind of behavior that moderators care about in practice. We therefore introduce a secondary dataset, constructed from the subreddit ChangeMyView (CMV) that does not use post-hoc annotations. Instead, the prediction task is to forecast whether the conversation will be subject to moderator action in the future. Wikipedia data. BIBREF9's `Conversations Gone Awry' dataset consists of 1,270 conversations that took place between Wikipedia editors on publicly accessible talk pages. The conversations are sourced from the WikiConv dataset BIBREF59 and labeled by crowdworkers as either containing a personal attack from within (i.e., hostile behavior by one user in the conversation directed towards another) or remaining civil throughout. Reddit CMV data. The CMV dataset is constructed from conversations collected via the Reddit API. In contrast to the Wikipedia-based dataset, we explicitly avoid the use of post-hoc annotation. Instead, we use as our label whether a conversation eventually had a comment removed by a moderator for violation of Rule 2: “Don't be rude or hostile to other users”. 
 Question: What labels for antisocial events are available in datasets?
Output: The Conversations Gone Awry dataset is labelled as either containing a personal attack from withint (i.e. hostile behavior by one user in the conversation directed towards another) or remaining civil throughout. The Reddit Change My View dataset is labelled with whether or not a coversation eventually had a comment removed by a moderator for violation of Rule 2: "Don't be rude or hostile to others users."
For those willing to have a more tightly-controlled installation of Seshat on their system, we also fully specify the manual installation steps in our online documentation). 
 Question: Is this software available to the public?
Output: Yes
We seek a function controlled by gates that can mix states across timesteps, but which acts independently on each channel of the state vector. The simplest option, which BIBREF12 term “dynamic average pooling”, uses only a forget gate: DISPLAYFORM0 
 Question: What pooling function is used?
Output: dynamic average pooling
Consequently, we investigate ways to detect suspicious accounts by considering their tweets in groups (chunks). Our hypothesis is that suspicious accounts have a unique pattern in posting tweet sequences. Since their intention is to mislead, the way they transition from one set of tweets to the next has a hidden signature, biased by their intentions. Given a news Twitter account, we read its tweets from the account's timeline. Then we sort the tweets by the posting date in ascending way and we split them into $N$ chunks. Each chunk consists of a sorted sequence of tweets labeled by the label of its corresponding account. 
 Question: How is a "chunk of posts" defined in this work?
Output: chunk consists of a sorted sequence of tweets labeled by the label of its corresponding account
We operationalized the question by submitting the chosen test data to the same vendor-based transcription pipeline that is used at Microsoft for production data (for model training and internal evaluation purposes), and then comparing the results to ASR system output under the NIST scoring protocol.  
 Question: what standard speech transcription pipeline was used?
Output: pipeline that is used at Microsoft for production data
In this work, we introduce a new logical inference engine called MonaLog, which is based on natural logic and work on monotonicity stemming from vanBenthemEssays86. Since our logic operates over surface forms, it is straightforward to hybridize our models. We investigate using MonaLog in combination with the language model BERT BIBREF20, including for compositional data augmentation, i.e, re-generating entailed versions of examples in our training sets.  We perform two experiments to test MonaLog. We first use MonaLog to solve the problems in a commonly used natural language inference dataset, SICK BIBREF1, comparing our results with previous systems. Second, we test the quality of the data generated by MonaLog. To do this, we generate more training data (sentence pairs) from the SICK training data using our system, and performe fine-tuning on BERT BIBREF20, a language model based on the transformer architecture BIBREF23, with the expanded dataset.  
 Question: How do they combine MonaLog with BERT?
Output: They use Monalog for data-augmentation to fine-tune BERT on this task
As this corpus of annotated patient notes comprises original healthcare data which contains protected health information (PHI) per The Health Information Portability and Accountability Act of 1996 (HIPAA) BIBREF16 and can be joined to the MIMIC-III database, individuals who wish to access to the data must satisfy all requirements to access the data contained within MIMIC-III. To satisfy these conditions, an individual who wishes to access the database must take a “Data or Specimens Management” course, as well as sign a user agreement, as outlined on the MIMIC-III database webpage, where the latest version of this database will be hosted as “Annotated Clinical Texts from MIMIC” BIBREF17. This corpus can also be accessed on GitHub after completing all of the above requirements. 
 Question: Is this dataset publicly available for commercial use?
Output: No
Argus Dataset AI2-8grade/CK12 Dataset We consider this dataset as preliminary since it was not reviewed by a human and many hypotheses are apparently unprovable by the evidence we have gathered (i.e. the theoretical top accuracy is much lower than 1.0).  MCTest Dataset We use an official extension of the dataset for RTE evaluation that again textually merges questions and answers. 
 Question: what datasets did they use?
Output: Argus Dataset AI2-8grade/CK12 Dataset MCTest Dataset
The encoder is a Convolutional Neural Network (CNN) and the decoder is a Long Short-Term Memory (LSTM) network, as presented in Figure 2 . The image is passed through the encoder generating the image representation that is used by the decoder to know the content of the image and generate the description word by word. 
 Question: What model is used to encode the images?
Output: a Convolutional Neural Network (CNN)
In order to obtain the citation data of the Czech apex courts, it was necessary to recognize and extract the references from the CzCDC 1.0. Given that training data for both the reference recognition model BIBREF13, BIBREF34 and the text segmentation model BIBREF33 are publicly available, we were able to conduct extensive error analysis and put together a pipeline to arguably achieve the maximum efficiency in the task. The pipeline described in this part is graphically represented in Figure FIGREF10. At this point, it is necessary to evaluate the performance of the above mentioned part of the pipeline before proceeding further. The evaluation of the performance is summarised in Table TABREF11. It shows that organising the two models into the pipeline boosted the performance of the reference recognition model, leading to a higher F1 measure in the initial recognition of the text spans and their classification. 
 Question: How is quality of the citation measured?
Output: it is necessary to evaluate the performance of the above mentioned part of the pipeline before proceeding further. The evaluation of the performance is summarised in Table TABREF11. It shows that organising the two models into the pipeline boosted the performance of the reference recognition model, leading to a higher F1 measure in the initial recognition of the text spans and their classification.
Encoders with induced latent structures have been shown to benefit several tasks including document classification, natural language inference BIBREF12, BIBREF13, and machine translation BIBREF11.  
 Question: Is there any evidence that encoders with latent structures work well on other tasks?
Output: Yes
More generally, we know little about the types of development issues that different countries prioritise, or whether country-specific factors such as wealth or democracy make countries more likely to push for specific development issues to be put on the global political agenda.  We find that discussion of Topic 2 is not significantly impacted by country-specific factors, such as wealth, population, democracy, levels of ODA, and conflict (although there are regional effects).  
 Question: What are the country-specific drivers of international development rhetoric?
Output: wealth  democracy  population levels of ODA conflict
The results show that NCEL consistently outperforms various baselines with a favorable generalization ability. 
 Question: How effective is their NCEL approach overall?
Output: NCEL consistently outperforms various baselines with a favorable generalization ability
In our method, we take an image as input and generate a natural question as output. The architecture for our model is shown in Figure FIGREF4 . Our model contains three main modules, (a) Representation Module that extracts multimodal features (b) Mixture Module that fuses the multimodal representation and (c) Decoder that generates question using an LSTM-based language model. 
 Question: How/where are the natural question generated?
Output: Decoder that generates question using an LSTM-based language model
In general, the baseline model CAEVO is outperformed by both NN models, and NN model with BERT embedding achieves the greatest performance. 
 Question: Do the BERT-based embeddings improve results?
Output: Yes
Shared Self-attention Layers As our model providing two outputs from one input, there is a bifurcation setting for how much shared part should be determined. Both constituent and dependency parsers share token representation and 8 self-attention layers at most. Assuming that either parser always takes input information flow through 8 self-attention layers as shown in Figure FIGREF4, then the number of shared self-attention layers varying from 0 to 8 may reflect the shared degree in the model. When the number is set to 0, it indicates only token representation is shared for both parsers trained for the joint loss through each own 8 self-attention layers. When the number is set to less than 8, for example, 6, then it means that both parsers first shared 6 layers from token representation then have individual 2 self-attention layers. For different numbers of shared layers, the results are in Table TABREF14. We respectively disable the constituent and the dependency parser to obtain a separate learning setting for both parsers in our model. The comparison in Table TABREF14 indicates that even though without any shared self-attention layers, joint training of our model may significantly outperform separate learning mode. At last, the best performance is still obtained from sharing full 8 self-attention layers. 
 Question: How are different network components evaluated?
Output: For different numbers of shared layers, the results are in Table TABREF14. We respectively disable the constituent and the dependency parser to obtain a separate learning setting for both parsers in our model.
Specifically, we design three tasks for seq2seq model pre-training, namely Sentence Reordering (SR), Next Sentence Generation (NSG), and Masked Document Generation (MDG).  
 Question: What is masked document generation?
Output: A task for seq2seq model pra-training that recovers a masked document to its original form.
Unlike the SQuAD dataset, which only has one passage given a question, there are several related passages for each question in the MS-MARCO dataset. In addition to annotating the answer, MS-MARCO also annotates which passage is correct.  
 Question: Why MS-MARCO is different from SQuAD?
Output: there are several related passages for each question in the MS-MARCO dataset. MS-MARCO also annotates which passage is correct
We apply attention probes to the task of identifying the existence and type of dependency relation between two words. Our first experiment is an exploratory visualization of how word sense affects context embeddings.  
 Question: What linguistic features were probed for?
Output: dependency relation between two words word sense
n the OLID dataset, we use a hierarchical annotation model split into three levels to distinguish between whether language is offensive or not (A), and type (B) and target (C) of the offensive language. Each level is described in more detail in the following subsections and examples are shown in Table TABREF10 . Level A: Offensive language Detection
Level A discriminates between offensive (OFF) and non-offensive (NOT) tweets. Level B: Categorization of Offensive Language
Level B categorizes the type of offense and two labels are used: targeted (TIN) and untargeted (INT) insults and threats. Level C: Offensive Language Target Identification
Level C categorizes the targets of insults and threats as individual (IND), group (GRP), and other (OTH). 
 Question: What are the three layers of the annotation scheme?
Output: Level A: Offensive language Detection
 Level B: Categorization of Offensive Language
 Level C: Offensive Language Target Identification
To develop the dataset, we extracted 200 sentences from Sorani Kurdish books of grades one to three of the primary school in the Kurdistan Region of Iraq. We randomly created 2000 sentences from the extracted sentences. 
 Question: How was the dataset collected?
Output: extracted text from Sorani Kurdish books of primary school and randomly created sentences
To train our best model we chose the best network from our experiments (6-layer bLSTM with 1024 hidden units), trained it with Adam optimizer and fine-tuned with SGD with momentum using exponential learning rate decay. 
 Question: Which architecture is their best model?
Output: 6-layer bLSTM with 1024 hidden units
Through experiments on two large complex cross-domain datasets, SParC BIBREF2 and CoSQL BIBREF6, we carefully compare and analyze the performance of different context modeling methods. 
 Question: What are two datasets models are tested on?
Output: SParC BIBREF2 and CoSQL BIBREF6
To ensure the universality and general applicability of the curriculum, we perform an in-depth investigation on three publicly available conversation corpora, PersonaChat BIBREF12, DailyDialog BIBREF13 and OpenSubtitles BIBREF7, consisting of 140 248, 66 594 and 358 668 real-life conversation samples, respectively. 
 Question: What three publicly available coropora are used?
Output: PersonaChat BIBREF12 DailyDialog BIBREF13 OpenSubtitles BIBREF7
In order to fully leverage gloss information, we propose GlossBERT to construct context-gloss pairs from all possible senses of the target word in WordNet, thus treating WSD task as a sentence-pair classification problem. 
 Question: Do they incoprorate WordNet into the model?
Output: construct context-gloss pairs from all possible senses of the target word in WordNet, thus treating WSD task as a sentence-pair classification problem
Spanish (SPA), in contrast, is morphologically rich, and disposes of much larger verbal paradigms than English. Like English, it is a suffixing language, and it additionally makes use of internal stem changes (e.g., o $\rightarrow $ ue). We choose Zulu (ZUL), a Bantoid language. In contrast to the first two, it is strongly prefixing. Second, the system pretrained on HUN performing well suggests again that a source language with an agglutinative, as opposed to a fusional, morphology seems to be beneficial as well. Again, TUR and HUN obtain high accuracy, which is an additional indicator for our hypothesis that a source language with an agglutinative morphology facilitates learning of inflection in another language. 
 Question: Are agglutinative languages used in the prediction of both prefixing and suffixing languages?
Output: Yes