niryuu/sni-each-converted huggingface dataset:
For the emotion recognition from text, we manually transcribe all utterances of our AMMER study. To exploit existing and available data sets which are larger than the AMMER data set, we develop a transfer learning approach. We use a neural network with an embedding layer (frozen weights, pre-trained on Common Crawl and Wikipedia BIBREF36), a bidirectional LSTM BIBREF37, and two dense layers followed by a soft max output layer. This setup is inspired by BIBREF38. We use a dropout rate of 0.3 in all layers and optimize with Adam BIBREF39 with a learning rate of $10^{-5}$ (These parameters are the same for all further experiments). We build on top of the Keras library with the TensorFlow backend. We consider this setup our baseline model. 
 Question: What is the baseline method for the task?
Output: For the emotion recognition from text they use described neural network as baseline.
For audio and face there is no baseline.
We collected Japanese fictional stories from the Web to construct the dataset. 
 Question: How is the dataset created?
Output: We collected Japanese fictional stories from the Web
The results are shown in Fig. FIGREF87 for different numbers of topics, where we can see that the proposed model outperforms all the baselines, being the svi version the one that performs best. In order to assess the computational advantages of the stochastic variational inference (svi) over the batch algorithm, the log marginal likelihood (or log evidence) was plotted against the number of iterations. Fig. FIGREF88 shows this comparison. Not surprisingly, the svi version converges much faster to higher values of the log marginal likelihood when compared to the batch version, which reflects the efficiency of the svi algorithm. 
 Question: what are the advantages of the proposed model?
Output: he proposed model outperforms all the baselines, being the svi version the one that performs best. the svi version converges much faster to higher values of the log marginal likelihood when compared to the batch version, which reflects the efficiency of the svi algorithm.
Given a statement and articles, workers are asked to judge whether the statement can be derived from the articles at three grades: True, Likely (i.e. Answerable), or Unsure (i.e. Unanswerable). If a worker selects Unsure, we ask workers to tell us why they are unsure from two choices (“Not stated in the article” or “Other”). If a worker selects True or Likely in the judgement task, we first ask which sentences in the given articles are justification explanations for a given statement, similarly to HotpotQA BIBREF2. The “summary” text boxes (i.e. NLDs) are then initialized with these selected sentences. 
 Question: How was the dataset annotated?
Output: True, Likely (i.e. Answerable), or Unsure (i.e. Unanswerable) why they are unsure from two choices (“Not stated in the article” or “Other”) The “summary” text boxes
Let us take a closer look at what we mean by agreement between the two parse trees. Essentially, if we have two words in the English sentence denoted by i and i', aligned to words j and j' in the parallel Hindi sentence respectively, we can expect a dependency edge between i and i' in the English parse tree to correspond to an edge between j and j' in the Hindi parse tree, and vice versa. For an edge in the English parse tree, we term the corresponding edge or path in the Hindi parse tree as the projection or projected path of the English edge on the Hindi parse tree, and similarly there are projected paths from Hindi to English. The dual decomposition inference algorithm tries to bring the parse trees in the two languages through its constraints. 
 Question: How does enforcing agreement between parse trees work across different languages?
Output: we can expect a dependency edge between i and i' in the English parse tree to correspond to an edge between j and j' in the Hindi parse tree dual decomposition inference algorithm tries to bring the parse trees in the two languages through its constraints
Our model introduces a multi-turns inference mechanism to process multi-perspective matching features.  
 Question: Which matching features do they employ?
Output: Matching features from matching sentences from various perspectives.
We use the publicly available dataset KVRET BIBREF5 in our experiments.  This dataset includes dialogues in 3 domains: calendar, weather, navigation (POI) which is suitable for our mix-domain dialogue experiments.  
 Question: Which domains did they explored?
Output: calendar weather navigation
We use English translations of the Chinese source texts in the WMT 2017 English–Chinese test set BIBREF18 for all experiments presented in this article: 
 Question: What languages do they investigate for machine translation?
Output: English  Chinese
For this reason as a baseline algorithm for English dataset we refer to results from BIBREF0, and as for Russian dataset, we used the probabilistic language model, described in BIBREF8. 
 Question: Which languages are used in the paper?
Output: English Russian
We carry out experiments on multiple language pairs including German-English, French-English, and Japanese-English.  
 Question: Which languages are used in the multi-lingual caption model?
Output: German-English, French-English, and Japanese-English
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 . 
 Question: Which datasets did they use?
Output: Stanford - Twitter Sentiment Corpus (STS Corpus) Sanders - Twitter Sentiment Corpus Health Care Reform (HCR)
In addition, we note that our approach can still lead to erroneous facts or even hallucinations. An interesting perspective might be to further constrain the model on the data structure in order to prevent inaccurate of even contradictory descriptions. 
 Question: What future possible improvements are listed?
Output: rther constrain the model on the data structure in order to prevent inaccurate of even contradictory descriptions
Most of the results seem to match with the views of some experts such as the political pundits of the Washington Post. 
 Question: Who are the experts?
Output: political pundits of the Washington Post
In this study, we experiment four different classification models including logistic regression (LR), recurrent neural network (RNN) BIBREF35, convolutional neural network (CNN) BIBREF36 and Google BERT BIBREF37.  
 Question: Which document classifiers do they experiment with?
Output: logistic regression (LR), recurrent neural network (RNN) BIBREF35, convolutional neural network (CNN) BIBREF36 and Google BERT BIBREF37
Traditional text-to-speech (TTS) systems are composed of complex pipelines BIBREF0 , these often include accoustic frontends, duration model, acoustic prediction model and vocoder models. Neural text-to-speech systems have garnered large research interest in the past 2 years. The first to fully explore this avenue of research was Google's tacotron BIBREF1 system The architecture of our model utilizes RNN-based Seq2Seq model for generating mel spectrogram from text. The architecture is similar to that of Tacotron 2 BIBREF4 .  Direct comparison of model parameters between ours and the open-source tacotron 2, our model contains 4.5 million parameters, whereas the Tacotron 2 contains around 13 million parameters with default setting 
 Question: How do they measure the size of models?
Output: Direct comparison of model parameters
convertible.pl: implementing DCG rules for 1st and 3rd steps in the three-steps conversion, as well as other rules including lexicon. 
 Question: What DCGs are used?
Output: Author's own DCG rules are defined from scratch.
We utilize the VATEX dataset for video captioning, which contains over 41,250 videos and 825,000 captions in both English and Chinese. Among the captions, there are over 206,000 English-Chinese parallel translation pairs. It covers 600 human activities and a variety of video content. Each video is paired with 10 English and 10 Chinese diverse captions. We follow the official split with 25,991 videos for training, 3,000 videos for validation and 6,000 public test videos for final testing. 
 Question: How big is the dataset used?
Output: over 41,250 videos and 825,000 captions in both English and Chinese. over 206,000 English-Chinese parallel translation pairs
For a given query $q = \langle s, r, ? \rangle $ , we identify mentions in $S_q$ of the entities in $C_q \cup \lbrace s\rbrace $ and create one node per mention. This process is based on the following heuristic:

we consider mentions spans in $S_q$ exactly matching an element of $C_q \cup \lbrace s\rbrace $ . Admittedly, this is a rather simple strategy which may suffer from low recall.

we use predictions from a coreference resolution system to add mentions of elements in $C_q \cup \lbrace s\rbrace $ beyond exact matching (including both noun phrases and anaphoric pronouns). In particular, we use the end-to-end coreference resolution by BIBREF16 .

we discard mentions which are ambiguously resolved to multiple coreference chains; this may sacrifice recall, but avoids propagating ambiguity. 
 Question: How did they detect entity mentions?
Output: Exact matches to the entity string and predictions from a coreference resolution system
The joke corpus in this dataset contains thousands of unique jokes of different categories (sci-fi, sports, etc) and types (puns, limerick, etc). 
 Question: Where did the real production data come from?
Output:  jokes of different categories (sci-fi, sports, etc) and types (puns, limerick, etc)
An important feature when suggesting an article INLINEFORM0 to an entity INLINEFORM1 is the novelty of INLINEFORM2 w.r.t the already existing entity profile INLINEFORM3 Given an entity INLINEFORM0 and the already added news references INLINEFORM1 up to year INLINEFORM2 , the novelty of INLINEFORM3 at year INLINEFORM4 is measured by the KL divergence between the language model of INLINEFORM5 and articles in INLINEFORM6 . We combine this measure with the entity overlap of INLINEFORM7 and INLINEFORM8 . The novelty value of INLINEFORM9 is given by the minimal divergence value. Low scores indicate low novelty for the entity profile INLINEFORM10 .

 
 Question: What features are used to represent the novelty of news articles to entity pages?
Output: KL-divergences of language models for the news article and the already added news references
Our final survey based classification results showed an accuracy of 96% in detecting PTSD and mean squared error of 1.2 in estimating its intensity given we have four intensity, No PTSD, Low Risk PTSD, Moderate Risk PTSD and High Risk PTSD with a score of 0, 1, 2 and 3 respectively.  
 Question: How is the intensity of the PTSD established?
Output: Given we have four intensity, No PTSD, Low Risk PTSD, Moderate Risk PTSD and High Risk PTSD with a score of 0, 1, 2 and 3 respectively, the estimated intensity  is established as mean  squared error.
The second, Continuous Bag-of-Words (CBOW), the model is given the sequence of words without the middle one and attempts to predict this omitted word.  
 Question: Which word embeddings are analysed?
Output: Continuous Bag-of-Words (CBOW)
The decoder is the second LSTM network that uses the information obtained from the encoder to generate the sequence's story. The first input $x_0$ to the decoder is the image for which the text is being generated. The last hidden state from the encoder $h_e^{(t)}$ is used to initialize the first hidden state of the decoder $h_d^{(0)}$ . With this strategy, we provide the decoder with the context of the whole sequence and the content of the current image (i.e. global and local information) to generate the corresponding text that will contribute to the overall story. 
 Question: How is the sequential nature of the story captured?
Output: we provide the decoder with the context of the whole sequence and the content of the current image (i.e. global and local information) to generate the corresponding text that will contribute to the overall story
We evaluate our model and baseline models on three versions of the dataset. The first one (Inc) only considers the original data, containing naturally incorrect tweets, and achieves accuracy of 80$\%$ against BERT's 72$\%$. The second version (Corr) considers the corrected tweets, and shows higher accuracy given that it is less noisy.  
 Question: Should their approach be applied only when dealing with incomplete data?
Output: No
Specifically, for each multiple-choice question $(q,A) \in Q_\mathit {tr}$ and each choice $a \in A$ , we use all non-stopword tokens in $q$ and $a$ as an ElasticSearch query against S. We take the top 200 hits, run Open IE v4, and aggregate the resulting tuples over all $a \in A$ and over all questions in $Q_\mathit {tr}$ to create the tuple KB (T). 
 Question: What OpenIE method was used to generate the extractions?
Output: for each multiple-choice question $(q,A) \in Q_\mathit {tr}$ and each choice $a \in A$ , we use all non-stopword tokens in $q$ and $a$ as an ElasticSearch query against S take the top 200 hits, run Open IE v4, and aggregate the resulting tuples over all $a \in A$ and over all questions in $Q_\mathit {tr}$
In light of this, we suggest that care is needed when applying graph embeddings in NLP pipelines, and work needed to develop robust methods to debias such embeddings. 
 Question: Do they propose any solution to debias the embeddings?
Output: No
29,794 The Wikipedia dataset consists of articles from English Wikipedia, with quality class labels assigned by the Wikipedia community. Wikipedia articles are labelled with one of six quality classes, in descending order of quality: Featured Article (“FA”), Good Article (“GA”), B-class Article (“B”), C-class Article (“C”), Start Article (“Start”), and Stub Article (“Stub”). We randomly sampled 5,000 articles from each quality class and removed all redirect pages, resulting in a dataset of 29,794 articles. 
 Question: How large is their data set?
Output: a sample of  29,794 wikipedia articles and 2,794 arXiv papers
In order to evaluate the models trained on generated data, we manually annotated a named entities dataset comprising 53453 tokens and 2566 sentences selected from over 250 news texts from ilur.am. 
 Question: what is the source of the news sentences?
Output: ilur.am
The DBpedia SPARQL endpoint is used for query answering and reasoning. 
 Question: What is the reasoning method that is used?
Output: SPARQL
We tested the system on two datasets, different in size and complexity of the addressed language.

Experimental Evaluation ::: Datasets ::: NLU-Benchmark dataset
The first (publicly available) dataset, NLU-Benchmark (NLU-BM), contains $25,716$ utterances annotated with targeted Scenario, Action, and involved Entities. Experimental Evaluation ::: Datasets ::: ROMULUS dataset
The second dataset, ROMULUS, is composed of $1,431$ sentences, for each of which dialogue acts, semantic frames, and corresponding frame elements are provided. This dataset is being developed for modelling user utterances to open-domain conversational systems for robotic platforms that are expected to handle different interaction situations/patterns – e.g., chit-chat, command interpretation. 
 Question: Which publicly available NLU dataset is used?
Output: ROMULUS dataset NLU-Benchmark dataset
We compile three Chinese text corpora from online data for three domains, namely, “hotel", “mobile phone (mobile)", and “travel". All texts are about user reviews.  
 Question: What are the sources of the data?
Output: User reviews written in Chinese collected online for hotel, mobile phone, and travel domains
To do so, we constructed a new dataset with 23,700 queries that are short and unstructured, in the same style made by real users of task-oriented systems.  
 Question: What is the size of this dataset?
Output: 23,700
In BIBREF8 a refined collection of tweets gathered from twitter is presented. Their dataset which is labeled for named entity recognition task contains 8,257 tweets. There are 12,784 entities in total in this dataset. Table TABREF19 shows statistics related to each named entity in training, development and test sets.

 
 Question: Which social media platform is explored?
Output: twitter
we apply our domain adaptation method to a neural captioning model and show performance improvement over other standard methods on several datasets and metrics.  
 Question: Did they only experiment with captioning task?
Output: Yes
Just as introduced in sec:introduction, it is intractable to compute similarity exactly, as involving INLINEFORM0 computation. Hence, we consider the monte-carlo approximation: DISPLAYFORM0

where INLINEFORM0 is a list of entity pairs sampled from INLINEFORM1 . We use sequential sampling to gain INLINEFORM6 , which means we first sample INLINEFORM7 given INLINEFORM8 from INLINEFORM9 , and then sample INLINEFORM10 given INLINEFORM11 and INLINEFORM12 from INLINEFORM13 . 
 Question: Which sampling method do they use to approximate similarity between the conditional probability distributions over entity pairs?
Output: monte-carlo sequential sampling
mcdonald:11 established that, when no treebank annotations are available in the target language, training on multiple source languages outperforms training on one (i.e., multi-source model transfer outperforms single-source model transfer). Following guo:16, for each target language, we train the parser on six other languages in the Google universal dependency treebanks version 2.0 (de, en, es, fr, it, pt, sv, excluding whichever is the target language), and we use gold coarse POS tags. 
 Question: How does the model work if no treebank is available?
Output: train the parser on six other languages in the Google universal dependency treebanks version 2.0 (de, en, es, fr, it, pt, sv, excluding whichever is the target language), and we use gold coarse POS tags
Our official scores (column Ens Test in Table TABREF19 ) have placed us second (EI-Reg, EI-Oc), fourth (V-Reg) and fifth (V-Oc) on the SemEval AIT-2018 leaderboard. 
 Question: What were the scores of their system?
Output: column Ens Test in Table TABREF19
The corpus used for sentiment analysis is the IMDb dataset of movie reviews by BIBREF11 while that for NER is Groningen Meaning Bank (GMB) by BIBREF12, containing 47,959 sentence samples.  
 Question: What Named Entity Recognition dataset is used?
Output: Groningen Meaning Bank
We ensemble BERT and E-BERT by (a) mean-pooling their outputs (AVG) or (b) concatenating the entity and its name with a slash symbol (CONCAT), e.g.: Jean_Marais / Jean Mara ##is. 
 Question: What are the two ways of ensembling BERT and E-BERT?
Output: mean-pooling their outputs (AVG) concatenating the entity and its name with a slash symbol (CONCAT)
In the first task, we use the SST-2 (Stanford Sentiment Treebank, version 2) dataset BIBREF25 to conduct sentiment analysis experiments. SST-2 is a sentence binary classification dataset with train/dev/test splits provided and two types of sentence labels, i.e., positive and negative. Here we experiment with the Snips dataset BIBREF26, which is widely used in SLU research. This dataset contains test spoken utterances (text) classified into one of 7 intents. 
 Question: Which real-world datasets did they use?
Output: SST-2 (Stanford Sentiment Treebank, version 2) Snips
Each of the methods we explore improve in % gendered words, % male bias, and F1 over the baseline Transformer generation model, but we find combining all methods in one – the ALL model is the most advantageous. 
 Question: What baseline is used to compare the experimental results against?
Output: Transformer generation model
To evaluate our sampling strategy, we compare it with the pretrained model and fine-tuned model on 16 different corpora. The results show that our approach outperforms those baselines on all corpora: it achieves 1.6% lower phone error rate on average. 
 Question: By how much do they, on average, outperform the baseline multilingual model on 16 low-resource tasks?
Output: 1.6% lower phone error rate on average
To tackle this issue, we present a new evaluation dataset that covers a wide range of monotonicity reasoning that was created by crowdsourcing and collected from linguistics publications (Section "Dataset" ). 
 Question: Do they release MED?
Output: Yes
We created our own causal explanation dataset by collecting 3,268 random Facebook status update messages. 
 Question: What types of social media did they consider?
Output: Facebook status update messages
In this paper, we propose a simple and effective method for learning affective events that only requires a very small seed lexicon and a large raw corpus. As illustrated in Figure FIGREF1, our key idea is that we can exploit discourse relations BIBREF4 to efficiently propagate polarity from seed predicates that directly report one's emotions (e.g., “to be glad” is positive). 
 Question: How does their model learn using mostly raw data?
Output: by exploiting discourse relations to propagate polarity from seed predicates to final sentiment polarity
Then, we created test sets with varying levels of perturbation operations - $\lbrace 20\%,40\%,60\%\rbrace $. 
 Question: Are recurrent neural networks trained on perturbed data?
Output: No
The baseline applies linear logistic regression to a set of stock technical signals to predict the following day’s stock return sign (+/‐). No sentiment features are provided to the baseline model. 
 Question: What is the baseline machine learning prediction approach?
Output: linear logistic regression to a set of stock technical signals
Accordingly, marcheggiani2017 presented a neural model putting syntax aside for dependency-based SRL and obtain favorable results, which overturns the inherent belief that syntax is indispensable in SRL task BIBREF11 . 
 Question: Are there syntax-agnostic SRL models before?
Output: Yes
The experiment settings from this paper and evaluation protocol for the Mboshi corpus (Boundary F-scores using the ZRC speech reference) are the same from BIBREF8. Table presents the results for bilingual UWS and multilingual leveraging. For the former, we reach our best result by using as aligned information the French, the original aligned language for this dataset.  For the multilingual selection experiments, we experimented combining the languages from top to bottom as they appear Table (ranked by performance; e.g. 1-3 means the combination of FR(1), EN(2) and PT(3)). We observe that the performance improvement is smaller than the one observed in previous work BIBREF10, which we attribute to the fact that our dataset was artificially augmented. Lastly, following the methodology from BIBREF8, we extract the most confident alignments (in terms of ANE) discovered by the bilingual models. Table presents the top 10 most confident (discovered type, translation) pairs. Looking at the pairs the bilingual models are most confident about, we observe there are some types discovered by all the bilingual models (e.g. Mboshi word itua, and the concatenation oboá+ngá). 
 Question: How is the performance of the model evaluated?
Output: The experiment settings from this paper and evaluation protocol for the Mboshi corpus (Boundary F-scores using the ZRC speech reference) are the same from BIBREF8. For the multilingual selection experiments, we experimented combining the languages from top to bottom as they appear Table (ranked by performance; e.g. 1-3 means the combination of FR(1), EN(2) and PT(3)).  Lastly, following the methodology from BIBREF8, we extract the most confident alignments (in terms of ANE) discovered by the bilingual models.
We used the following affective resources relying on different emotion models.

Emolex: it contains 14,182 words associated with eight primary emotion based on the Plutchik model BIBREF10 , BIBREF11 .

EmoSenticNet(EmoSN): it is an enriched version of SenticNet BIBREF12 including 13,189 words labeled by six Ekman's basic emotion BIBREF13 , BIBREF14 .

Dictionary of Affect in Language (DAL): includes 8,742 English words labeled by three scores representing three dimensions: Pleasantness, Activation and Imagery BIBREF15 .

Affective Norms for English Words (ANEW): consists of 1,034 English words BIBREF16 rated with ratings based on the Valence-Arousal-Dominance (VAD) model BIBREF17 .

Linguistic Inquiry and Word Count (LIWC): this psycholinguistic resource BIBREF18 includes 4,500 words distributed into 64 emotional categories including positive (PosEMO) and negative (NegEMO). 
 Question: What affective-based features are used?
Output: affective features provided by different emotion models such as Emolex, EmoSenticNet, Dictionary of Affect in Language, Affective Norms for English Words and Linguistics Inquiry and Word Count
We recognise features that add ambiguity to the supporting facts, for example when information is only expressed implicitly by using an Ellipsis. As opposed to redundant words, we annotate Restrictivity and Factivity modifiers, words and phrases whose presence does change the meaning of a sentence with regard to the expected answer, and occurrences of intra- or inter-sentence Coreference in supporting facts (that is relevant to the question). Lastly, we mark ambiguous syntactic features, when their resolution is required in order to obtain the answer. Concretely, we mark argument collection with con- and disjunctions (Listing) and ambiguous Prepositions, Coordination Scope and Relative clauses/Adverbial phrases/Appositions. We recognise features that add ambiguity to the supporting facts, for example when information is only expressed implicitly by using an Ellipsis. As opposed to redundant words, we annotate Restrictivity and Factivity modifiers, words and phrases whose presence does change the meaning of a sentence with regard to the expected answer, and occurrences of intra- or inter-sentence Coreference in supporting facts (that is relevant to the question). Lastly, we mark ambiguous syntactic features, when their resolution is required in order to obtain the answer. Concretely, we mark argument collection with con- and disjunctions (Listing) and ambiguous Prepositions, Coordination Scope and Relative clauses/Adverbial phrases/Appositions. 
 Question: What features are absent from MRC gold standards that can result in potential lexical ambiguity?
Output: Restrictivity  Factivity  Coreference
Our WordDecoding (WDec) model achieves F1 scores that are $3.9\%$ and $4.1\%$ higher than HRL on the NYT29 and NYT24 datasets respectively. Similarly, our PtrNetDecoding (PNDec) model achieves F1 scores that are $3.0\%$ and $1.3\%$ higher than HRL on the NYT29 and NYT24 datasets respectively. 
 Question: Which one of two proposed approaches performed better in experiments?
Output: WordDecoding (WDec) model
First, it provides an annotation interface that allows users to define content elements, upload documents, and annotate documents Our platform is able to ingest documents in a variety of formats, including PDFs and Microsoft Word, and converts these formats into plain text before presenting them to the annotators. 
 Question: What type of documents are supported by the annotation platform?
Output: Variety of formats supported (PDF, Word...), user can define content elements of document
Depechemood is a lexicon-based emotion detection method gathered from crowd-annotated news BIBREF24. Drawing on approximately 23.5K documents with average of 500 words per document from rappler.com, researchers asked subjects to report their emotions after reading each article. They then multiplied the document-emotion matrix and word-document matrix to derive emotion-word matrix for these words.  
 Question: How id Depechemood trained?
Output: By multiplying crowd-annotated document-emotion matrix with emotion-word matrix.
We evaluate our model in a simulated binning task in which the robot is tasked to place a cube into a bowl as outlined by the verbal command. Each environment contains between three and five objects differentiated by their size (small, large), shape (round, square) and color (red, green, blue, yellow, pink), totalling in 20 different objects. Depending on the generated scenario, combinations of these three features are necessary to distinguish the targets from each other, allowing for tasks of varying complexity. 
 Question: What simulations are performed by the authors to validate their approach?
Output: a simulated binning task in which the robot is tasked to place a cube into a bowl as outlined by the verbal command
Non-contextual properties of a word. These features are derived directly from the word and capture the general tendency of a word being echoed in explanations. 
 Question: What non-contextual properties do they refer to?
Output: These features are derived directly from the word and capture the general tendency of a word being echoed in explanations.
We define the STransE score function $\mathcal {R}$1 as follows:

$ f_r(h, t) & = & \Vert \textbf {W}_{r,1}\textbf {h} + \textbf {r} - \textbf {W}_{r,2}\textbf {t}\Vert _{\ell _{1/2}} $

using either the $\ell _1$ or the $\ell _2$ -norm (the choice is made using validation data; in our experiments we found that the $\ell _1$ norm gave slightly better results). 
 Question: What scoring function does the model use to score triples?
Output: $ f_r(h, t) & = & \Vert \textbf {W}_{r,1}\textbf {h} + \textbf {r} - \textbf {W}_{r,2}\textbf {t}\Vert _{\ell _{1/2}} $
To compensate for the exclusion of slot-specific parameters, we incorporate better feature representation of user utterance and dialogue states using syntactic information and convolutional neural networks (CNN). 
 Question: What network architecture do they use for SIM?
Output: convolutional neural networks (CNN)
Then we extract these features from two sources of context texts, specifically the title of the news article that the comment was posted for and the screen name of the user who posted the comment. 
 Question: What context do they use?
Output: title of the news article screen name of the user
In order to make the corpus collection easier and faster, we adopted a semi-automatic procedure based on sequential neural models BIBREF19, BIBREF20. Instead, since tokens are transcribed at morpheme level, we split Arabish tokens into characters, and Arabic tokens into morphemes, and we treated each token itself as a sequence. Our model learns thus to map Arabish characters into Arabic morphemes. With this model we automatically transcribed into Arabic morphemes, roughly, 5,000 additional tokens, corresponding to the second annotation block.  Manual transcription plus a 
 Question: How does the semi-automatic construction process work?
Output: Automatic transcription of 5000 tokens through sequential neural models trained on the annotated part of the corpus
The gain from disabling a single head is different for different tasks, ranging from the minimum absolute gain of 0.1% for STS-B, to the maximum of 1.2% for MRPC (see fig:disableheadsall). Furthermore, disabling a whole layer, that is, all 12 heads in a given layer, also improves the results. fig:disablelayers shows the resulting model performance on the target GLUE tasks when different layers are disabled. 
 Question: In which certain heads was attention disabled in experiments?
Output: single head disabling a whole layer, that is, all 12 heads in a given layer
The experimental results are shown in Table 1, where it can be seen that autoencoders outperformed MLP and CNN outperformed autoencoders with the highest achieved accuracy of 82.6%. 
 Question: What was their performance on the dataset?
Output: accuracy of 82.6%
From the chart, it is clear that there is a higher chance of fake news coming from unverified accounts. Notice that accounts spreading viral tweets with fake news have, on average, a larger ratio of friends/followers.  Figure FIGREF24 shows that, in contrast to other kinds of viral tweets, those containing fake news were created more recently. 
 Question: What are the characteristics of the accounts that spread fake news?
Output: Accounts that spread fake news are mostly unverified, recently created and have on average high friends/followers ratio
However, what we did was more than this, and we also achieved great results on the second-level and third-level senses for the sake of our self-demand for high-quality, finally achieving agreement of 0.85 and Kappa value of 0.83 for these two deeper levels of senses. 
 Question: How high is the inter-annotator agreement?
Output: agreement of 0.85 and Kappa value of 0.83
In terms of this, rather than starting from Twitter API Search, we look into the archived snapshots of two major news websites (CNN, NBC), and then extract the tweet blocks that are embedded in the news articles. 
 Question: How do they determine if tweets have been used by journalists?
Output:  we look into the archived snapshots of two major news websites (CNN, NBC), and then extract the tweet blocks that are embedded in the news articles
The current release of Katecheo uses a Bi-Directional Attention Flow, or BiDAF, model for reading comprehension BIBREF6 . Future releases of Katecheo will include the ability to swap out the reading comprehension model for newer architectures based on, e.g., BERT BIBREF8 or XLNet BIBREF9 or custom trained models.

Architecture and Configuration 
 Question: what pretrained models were used?
Output: BiDAF BERT
Similar to BIBREF9, we experiment with SVM with RBF kernel, with features that represent (1) the simple characteristics of the argument tree and (2) the linguistic characteristics of the claim. 
 Question: What models that rely only on claim-specific linguistic features are used as baselines?
Output: SVM with RBF kernel
Dialogue act (DA) characterizes the type of a speaker's intention in the course of producing an utterance and is approximately equivalent to the illocutionary act of BIBREF0 or the speech act of BIBREF1. The recognition of DA is essential for modeling and automatically detecting discourse structure, especially in developing a human-machine dialogue system. It is natural to predict the Answer acts following an utterance of type Question, and then match the Question utterance to each QA-pair in the knowledge base. The predicted DA can also guide the response generation process BIBREF2. For instance, system generates a Greeting type response to former Greeting type utterance. DA recognition is aimed to assign a label to each utterance in a conversation. It can be formulated as a supervised classification problem. There are two trends to solve this problem: 1) as a sequence labeling problem, it will predict the labels for all utterances in the whole dialogue history BIBREF13, BIBREF14, BIBREF9; 2) as a sentence classification problem, it will treat utterance independently without any context history BIBREF5, BIBREF15.  
 Question: What is dialogue act recognition?
Output: DA recognition is aimed to assign a label to each utterance in a conversation. It can be formulated as a supervised classification problem.
We evaluate the proposed architecture on two publicly available datasets: the Adverse Drug Events (ADE) dataset BIBREF6 and the CoNLL04 dataset BIBREF7. We show that our architecture is able to outperform the current state-of-the-art (SOTA) results on both the NER and RE tasks in the case of ADE. In the case of CoNLL04, our proposed architecture achieves SOTA performance on the NER task and achieves near SOTA performance on the RE task. On both datasets, our results are SOTA when averaging performance across both tasks. 
 Question: Do they repot results only on English data?
Output: Yes
Interesting prior work on quantifying social norm violation has taken a heavily data-driven focus BIBREF8 , BIBREF9 .  
 Question: Does this paper propose a new task that others can try to improve performance on?
Output: No, there has been previous work on recognizing social norm violation.
Grammatical error correction (GEC) is a challenging task due to the variability of the type of errors and the syntactic and semantic dependencies of the errors on the surrounding context. Most of the grammatical error correction systems use classification and rule-based approaches for correcting specific error types. However, these systems use several linguistic cues as features. The standard linguistic analysis tools like part-of-speech (POS) taggers and parsers are often trained on well-formed text and perform poorly on ungrammatical text. This introduces further errors and limits the performance of rule-based and classification approaches to GEC. As a consequence, the phrase-based statistical machine translation (SMT) approach to GEC has gained popularity because of its ability to learn text transformations from erroneous text to correct text from error-corrected parallel corpora without any additional linguistic information.  We model our GEC system based on the phrase-based SMT approach. However, traditional phrase-based SMT systems treat words and phrases as discrete entities. We take advantage of continuous space representation by adding two neural network components that have been shown to improve SMT systems BIBREF3 , BIBREF4 . To train NNJM, we use the publicly available implementation, Neural Probabilistic Language Model (NPLM) BIBREF14 . The latest version of Moses can incorporate NNJM trained using NPLM as a feature while decoding. Similar to NNGLM, we use the parallel text used for training the translation model in order to train NNJM. We use a source context window size of 5 and a target context window size of 4. We select a source context vocabulary of 16,000 most frequent words from the source side. The target context vocabulary and output vocabulary is set to the 32,000 most frequent words. We use a single hidden layer to speed up training and decoding with an input embedding dimension of 192 and 512 hidden layer nodes.  
 Question: Do they use pretrained word representations in their neural network models?
Output: No
GANN is novel neural network model for APC task aimed to solve the shortcomings of traditional RNNs and CNNs. The GANN applied the Gate Truncation RNN (GTR) to learn informative aspect-dependent sentiment clue representations. GANN obtained the state-of-the-art APC performance on the Chinese review datasets. 
 Question: What was previous state-of-the-art on four Chinese reviews datasets?
Output: GANN obtained the state-of-the-art APC performance on the Chinese review datasets
Even if LangID-High does not present a more accurate result, it does present a more compact one: LangID-High is 15.4 MB, while the combined wFST high resource models are 197.5 MB. 
 Question: how is model compactness measured?
Output: Using file size on disk
we take the CORD-19 dataset BIBREF2, which contains over 45,000 scholarly articles, including over 33,000 with full text, about COVID-19, SARS-CoV-2, and related coronaviruses. We develop sentence classification methods to identify all sentences narrating radiological findings from COVID-19.  We conduct experiments to verify the effectiveness of our method. From the CORD-19 dataset, our method successfully discovers a set of clinical findings that are closely related with COVID-19. 
 Question: How large is the collection of COVID-19 literature?
Output: 45,000 scholarly articles, including over 33,000 with full text
We also explored clustering 12742 STRENGTH sentences directly using CLUTO BIBREF19 and Carrot2 Lingo BIBREF20 clustering algorithms.  
 Question: What clustering algorithms were used?
Output: CLUTO Carrot2 Lingo
our method still can improve the state-of-the-art accuracy BIBREF7 from 60.32% to 60.34% Although we only have 57% of testing questions can benefit from the basic questions, our method still can improve the state-of-the-art accuracy BIBREF7 from 60.32% to 60.34%,  
 Question: What accuracy do they approach with their proposed method?
Output: our method still can improve the state-of-the-art accuracy BIBREF7 from 60.32% to 60.34%
Our attempt at language pre-training fell short of our expectations in all but one tested dataset. Our pre-training was unsuccessful in improving accuracy, even when applied to networks larger than those reported. 
 Question: Does pre-training on general text corpus improve performance?
Output: No
To overcome the size issue of the student reflection dataset, we first explore the effect of incorporating domain transfer into a recent abstractive summarization model: pointer networks with coverage mechanism (PG-net)BIBREF0.  
 Question: What is the recent abstractive summarization method in this paper?
Output: pointer networks with coverage mechanism (PG-net)
The National Gang Threat Assessment Report confirms that at least tens of thousands of gang members are using social networking websites such as Twitter and video sharing websites such as YouTube in their daily life BIBREF0 . They are very active online; the 2007 National Assessment Center's survey of gang members found that 25% of individuals in gangs use the Internet for at least 4 hours a week BIBREF4 . 
 Question: Do the authors provide evidence that 'most' street gang members use Twitter to intimidate others?
Output: No
As a benchmark, we will use the results of the systems by Burckhardt et al. BIBREF22 , Liu et al. BIBREF18 , Dernoncourt et al. BIBREF9 and Yang et al. BIBREF10 on the i2b2 dataset and the performance of Burckhardt et al. on the nursing corpus. 
 Question: What is their baseline?
Output: Burckhardt et al. BIBREF22 Liu et al. BIBREF18 Dernoncourt et al. BIBREF9 Yang et al. BIBREF10
We test mgru on two well-known datasets, the Penn Treebank and Text8. 
 Question: Which dataset do they train their models on?
Output: Penn Treebank Text8
Our system learns Perceptron models BIBREF37 using the Machine Learning machinery provided by the Apache OpenNLP project with our own customized (local and clustering) features.  The local features constitute our baseline system on top of which the clustering features are added. 
 Question: what are the baselines?
Output: Perceptron model using the local features.
We illustrate the concept by discussing some instantiations that we have recently experimented with. The design also provides for a clear “relevance place” at which an opportunity arises for semantic negotiation, namely, before the final decision is made. An example of this is shown in the example below.   As SECREF12 shows, even in cases where a decision can be reached quickly, there can be an explicit mutual confirmation step, before the (silent) decision signal is sent. A third setting that we have explored BIBREF19 brings conceptual negotiation more clearly into the foreground. In that game, the players are presented with images of birds of particular species and are tasked with coming up with a description of common properties. Again, the final answer has to be approved by both participants. As SECREF13 shows, this can lead to an explicit negotiation of conceptual content. 
 Question: Do the authors perform experiments using their proposed method?
Output: Yes
The first part is described as Task A, the purpose of which is to identify the boundaries of timexes and assign them to one of the following classes: date, time, duration, set. We chose the best 3 results from each word embeddings group (EE, EP, EC) from Table TABREF19 presenting F1-scores for all models. Then we evaluated these results using more detailed measures for timexes, presented in BIBREF27 . 
 Question: What experiments are presented?
Output: identify the boundaries of timexes and assign them to one of the following classes: date, time, duration, set  Then we evaluated these results using more detailed measures for timexes
The retweeting behavior of MEPs is captured by their retweet network. Each MEP active on Twitter is a node in this network. An edge in the network between two MEPs exists when one MEP retweeted the other. The weight of the edge is the number of retweets between the two MEPs We measure the cohesion of a political group INLINEFORM0 as the average retweets, i.e., the ratio of the number of retweets between the MEPs in the group INLINEFORM1 to the number of MEPs in the group INLINEFORM2  
 Question: Do they authors account for differences in usage of Twitter amongst MPs into their model?
Output: No
We train $\mathcal {F}$ and $\mathcal {G}$ jointly and introduce two regularizers. Formally, we hope that $\mathcal {G}(\mathcal {F}(X))$ is similar to $X$ and $\mathcal {F}(\mathcal {G}(Y))$ is similar to $Y$. We implement this constraint as a cycle consistency loss. As a result, the proposed model has two learning objectives: i) an adversarial loss ($\ell _{adv}$) for each model as in the baseline. ii) a cycle consistency loss ($\ell _{cycle}$) on each side to avoid $\mathcal {F}$ and $\mathcal {G}$ from contradicting each other. 
 Question: What regularizers were used to encourage consistency in back translation cycles?
Output: an adversarial loss ($\ell _{adv}$) for each model as in the baseline a cycle consistency loss ($\ell _{cycle}$) on each side
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: Which framework they propose in this paper?
Output:  extraction-then-synthesis framework
Our model consists of two neural network modules, i.e. an extractor and abstractor. The extractor encodes a source document and chooses sentences from the document, and then the abstractor paraphrases the summary candidates.  The extractor is based on the encoder-decoder framework. We adapt BERT for the encoder to exploit contextualized representations from pre-trained transformers. We use LSTM Pointer Network BIBREF22 as the decoder to select the extracted sentences based on the above sentence representations.  Our abstractor is practically identical to the one proposed in BIBREF8. 
 Question: What's the method used here?
Output: Two neural networks: an extractor based on an encoder (BERT) and a decoder (LSTM Pointer Network BIBREF22) and an abstractor identical to the one proposed in BIBREF8.
69.10%/78.38% 
 Question: how much of improvement the adaptation model can get?
Output:  69.10%/78.38%
As shown in Table TABREF39 , the proposed framework consistently obtains the best scores on all of the four datasets. Compared with the winning systems of SemEval ABSA, our framework achieves 5.0%, 1.6%, 1.4%, 1.3% absolute gains on INLINEFORM0 , INLINEFORM1 , INLINEFORM2 and INLINEFORM3 respectively.

Our framework can outperform RNCRF, a state-of-the-art model based on dependency parsing, on all datasets. We also notice that RNCRF does not perform well on INLINEFORM0 and INLINEFORM1 (3.7% and 3.9% inferior than ours). We find that INLINEFORM2 and INLINEFORM3 contain many informal reviews, thus RNCRF's performance degradation is probably due to the errors from the dependency parser when processing such informal texts. 
 Question: By how much do they outperform state-of-the-art methods?
Output: Compared with the winning systems of SemEval ABSA, our framework achieves 5.0%, 1.6%, 1.4%, 1.3% absolute gains on INLINEFORM0 , INLINEFORM1 , INLINEFORM2 and INLINEFORM3 respectively.