niryuu/sni-each-converted huggingface dataset:
Finally, when investigating the relatedness between European vs. non European languages (cf. (En/Fr)$\rightarrow $Ar), we obtain similar results than those obtained in the monolingual experiment (macro F-score 62.4 vs. 68.0) and best results are achieved by Ar $\rightarrow $(En/Fr). This shows that there are pragmatic devices in common between both sides and, in a similar way, similar text-based patterns in the narrative way of the ironic tweets. 
 Question: Do the authors identify any cultural differences in irony use?
Output: No
Proposed Fusion Techniques ::: Step-Wise Decoder Fusion
Our first proposed technique is the step-wise decoder fusion of visual features during every prediction step i.e. we concatenate the visual encoding as context at each step of the decoding process. Proposed Fusion Techniques ::: Multimodal Attention Modulation
Similar to general attention BIBREF8, wherein a variable-length alignment vector $a_{th}(s)$, whose size equals the number of time steps on the source side, is derived by comparing the current target hidden state $h_{t}$ with each source hidden state $\overline{h_{s}}$; we consider a variant wherein the visual encoding $v_{t}$ is used to calculate an attention distribution $a_{tv}(s)$ over the source encodings as well. Proposed Fusion Techniques ::: Visual-Semantic (VS) Regularizer
In terms of leveraging the visual modality for supervision, BIBREF1 use multi-task learning to learn grounded representations through image representation prediction. 
 Question: What are 3 novel fusion techniques that are proposed?
Output: Step-Wise Decoder Fusion Multimodal Attention Modulation Visual-Semantic (VS) Regularizer
Our annotation scheme introduces opportunities for the educational community to conduct further research on the relationship between features of student talk, student learning, and discussion quality. Once automated classifiers are developed, such relations between talk and learning can be examined at scale. Also, automatic labeling via a standard coding scheme can support the generalization of findings across studies, and potentially lead to automated tools for teachers and students.  The development of an annotation scheme explicitly designed for this problem is the first step towards collecting and annotating corpora that can be used by the NLP community to advance the field in this particular area. 
 Question: what opportunities are highlighted?
Output: Our annotation scheme introduces opportunities for the educational community to conduct further research  Once automated classifiers are developed, such relations between talk and learning can be examined at scale  automatic labeling via a standard coding scheme can support the generalization of findings across studies, and potentially lead to automated tools for teachers and students collecting and annotating corpora that can be used by the NLP community to advance the field in this particular area
Above all, we introduce two common baselines. The first one just selects the leading sentences to form a summary. It is often used as an official baseline of DUC, and we name it “LEAD”. The other system is called “QUERY_SIM”, which directly ranks sentences according to its TF-IDF cosine similarity to the query. Above all, we introduce two common baselines. The first one just selects the leading sentences to form a summary. It is often used as an official baseline of DUC, and we name it “LEAD”. The other system is called “QUERY_SIM”, which directly ranks sentences according to its TF-IDF cosine similarity to the query. In addition, we implement two popular extractive query-focused summarization methods, called MultiMR BIBREF2 and SVR BIBREF20 . Since our model is totally data-driven, we introduce a recent summarization system DocEmb BIBREF9 that also just use deep neural network features to rank sentences. To verify the effectiveness of the joint model, we design a baseline called ISOLATION, which performs saliency ranking and relevance ranking in isolation. 
 Question: What models do they compare to?
Output: LEAD QUERY_SIM MultiMR SVR DocEmb ISOLATION
Work on VAE in BIBREF17 to learn acoustic embeddings conducted experiments using the TIMIT data set. The baseline performance for VAE based phone classification experiments in BIBREF17 report an accuracy of 72.2%. The re-implementation forming the basis for our work gave an accuracy of 72.0%, a result that was considered to provide a credible basis for further work. 
 Question: What classification baselines are used for comparison?
Output: VAE
We compare our model with several baselines including:

Attn seq2seq BIBREF22: A model with simple attention over the input context at each time step during decoding.

Ptr-UNK BIBREF23: Ptr-UNK is the model which augments a sequence-to-sequence architecture with attention-based copy mechanism over the encoder context.

KV Net BIBREF6: The model adopted and argumented decoder which decodes over the concatenation of vocabulary and KB entities, which allows the model to generate entities.

Mem2Seq BIBREF7: Mem2Seq is the model that takes dialogue history and KB entities as input and uses a pointer gate to control either generating a vocabulary word or selecting an input as the output.

DSR BIBREF9: DSR leveraged dialogue state representation to retrieve the KB implicitly and applied copying mechanism to retrieve entities from knowledge base while decoding. 
 Question: What were the baseline systems?
Output: Attn seq2seq Ptr-UNK KV Net Mem2Seq DSR
We first test our model on the single domain dataset, WoZ2.0 BIBREF19 . It consists of 1,200 dialogues from the restaurant reservation domain with three pre-defined slots: food, price range, and area. Since the name slot rarely occurs in the dataset, it is not included in our experiments, following previous literature BIBREF3 , BIBREF20 . Our model is also tested on the multi-domain dataset, MultiWoZ BIBREF9 . It has a more complex ontology with 7 domains and 25 predefined slots. Since the combined slot-value pairs representation of the belief states has to be applied for the model with $O(n)$ ITC, the total number of slots is 35.  
 Question: Which datasets are used to evaluate performance?
Output: the single domain dataset, WoZ2.0  the multi-domain dataset, MultiWoZ
WMT14 En-Fr and En-De datasets The WMT 2014 English-French translation dataset, consisting of $36M$ sentence pairs, is adopted as a big dataset to test our model. For medium dataset, we borrow the setup of BIBREF0 and adopt the WMT 2014 English-German translation dataset which consists of $4.5M$ sentence pairs, the BPE vocabulary size is set to $32K$. IWSLT De-En and En-Vi datasets Besides, we perform experiments on two small IWSLT datasets to test the small version of MUSE with other comparable models. The IWSLT 2014 German-English translation dataset consists of $160k$ sentence pairs. We also adopt a joint source and target BPE factorization with the vocabulary size of $32K$. The IWSLT 2015 English-Vietnamese translation dataset consists of $133K$ training sentence pairs. 
 Question: What datasets are used?
Output: WMT14 En-Fr and En-De datasets IWSLT De-En and En-Vi datasets
We manually inspect 148 samples from the seen part of the test set, containing 440 relations, counting expressed, omitted, wrong and over-generated (hallucinated) facts. 
 Question: How is faithfulness of the resulting text evaluated?
Output: manually inspect
Table TABREF18 shows the Spearman correlation values of GM$\_$KL model evaluated on the benchmark word similarity datasets: SL BIBREF20, WS, WS-R, WS-S BIBREF21, MEN BIBREF22, MC BIBREF23, RG BIBREF24, YP BIBREF25, MTurk-287 and MTurk-771 BIBREF26, BIBREF27, and RW BIBREF28.  Table TABREF19 shows the evaluation results of GM$\_$KL model on the entailment datasets such as entailment pairs dataset BIBREF29 created from WordNet with both positive and negative labels, a crowdsourced dataset BIBREF30 of 79 semantic relations labelled as entailed or not and annotated distributionally similar nouns dataset BIBREF31 
 Question: What are the qualitative experiments performed on benchmark datasets?
Output: Spearman correlation values of GM_KL model evaluated on the benchmark word similarity datasets.
Evaluation results of GM_KL model on the entailment datasets such as entailment pairs dataset created from WordNet, crowdsourced dataset of 79 semantic relations labelled as entailed or not and annotated distributionally similar nouns dataset.
In this work, we use pretraining on the unlabeled data of each task and show that it can increase the performance of classification systems. 
 Question: Where is MVCNN pertained?
Output: on the unlabeled data of each task
We first describe our models individually and then the ensembling technique that we employ. In the following, MN denotes Memory Networks to encode conversational history, RCNN signify R-CNN for object level representations of an image, Wt represents additional linear layer in the decoder, and LF a late fusion mechanism as defined in BIBREF0.

Models ::: LF-RCNN
Late fusion encoder BIBREF0 with concatenated history. We use two-layered LSTMs with 512 hidden units for embedding questions and history. The object-level features are weighed using only question embeddings. The word embeddings from Glove vectors are frozen and are not fine-tuned. Figure FIGREF6 gives an overview of the architecture.

Models ::: MN-RCNN
Memory network encoder BIBREF0 with bi-directional GRUs and word embeddings fine-tuned. Object-level features are weighed by question and caption embedding. The rest of the scheme is same as above. (Figure FIGREF6)

Models ::: MN-RCNN-Wt
Same as above but with an additional linear layer applied to the dot product of candidate answer and encoder output, and gated using tanh function. Compare Figure FIGREF6 with Figure FIGREF6 
 Question: Which three discriminative models did they use?
Output: LF-RCNN MN-RCNN MN-RCNN-Wt
The size of the training datasets varies considerably from 469 posts to 17 million; a difference of four orders of magnitude. 
 Question: How big are this dataset and catalogue?
Output:  from 469 posts to 17 million
The 16 classes are inspired by the OntoNotes5 corpus BIBREF7 as well as the ACE (Automatic Content Extraction) English Annotation Guidelines for Entities Version 6.6 2008.06.13 BIBREF8. 
 Question: How did they determine the distinct classes?
Output: inspired by the OntoNotes5 corpus BIBREF7 as well as the ACE (Automatic Content Extraction) English Annotation Guidelines for Entities Version 6.6 2008.06.13 BIBREF8
We carried out a reliability study for the proposed scheme using two pairs of expert annotators, P1 and P2.  
 Question: do they use a crowdsourcing platform?
Output: No
We carried out a reliability study for the proposed scheme using two pairs of expert annotators, P1 and P2.  Inter-rater reliability was assessed using Cohen's kappa: unweighted for argumentation and knowledge domain, but quadratic-weighted for specificity given its ordered labels. 
 Question: what experiments are conducted?
Output: a reliability study for the proposed scheme
We then train the sensationalism scorer by classifying sensational and non-sensational headlines using a one-layer CNN with a binary cross entropy loss $L_{\text{sen}}$. Firstly, 1-D convolution is used to extract word features from the input embeddings of a headline. This is followed by a ReLU activation layer and a max-pooling layer along the time dimension. All features from different channels are concatenated together and projected to the sensationalism score by adding another fully connected layer with sigmoid activation. Binary cross entropy is used to compute the loss $L_{\text{sen}}$. 
 Question: How is sensationalism scorer trained?
Output: by classifying sensational and non-sensational headlines using a one-layer CNN with a binary cross entropy loss $L_{\text{sen}}$
For the cross-domain polarity classification experiments, we use the second version of Multi-Domain Sentiment Dataset BIBREF0 . The data set contains Amazon product reviews of four different domains: Books (B), DVDs (D), Electronics (E) and Kitchen appliances (K). Reviews contain star ratings (from 1 to 5) which are converted into binary labels as follows: reviews rated with more than 3 stars are labeled as positive, and those with less than 3 stars as negative. In each domain, there are 1000 positive and 1000 negative reviews. 
 Question: How long is the dataset?
Output: 8000
An attention-based sequence-to-sequence model with the emoji vector as additional input as discribed in MojiTalk BIBREF16. CVAE.
An RNN-based conditional variational autoencoder for dialogue response generation BIBREF16, which uses a multivariate Gaussian latent variable to model the response and concatenate it with the last hidden state of the encoder as the initial state of the decoder. KL annealing, early stopping strategy and bag-of-word auxiliary loss are applied during the training. We use the implementation released by BIBREF16 
 Question: What baselines other than standard transformers are used in experiments?
Output: attention-based sequence-to-sequence model  CVAE
We collected a total of 657 ratings by 14 volunteers, 5 Italian and 9 non-Italian listeners, spread over the 24 clips and three testing conditions. 
 Question: How many people are employed for the subjective evaluation?
Output: 14 volunteers
time: decade (classes between 1900s and 2010s) and year representative of the time when the genre became meainstream 
 Question: Which decades did they look at?
Output: between 1900s and 2010s
We use the method of BIBREF5 to train neural sequence-to-sequence Spanish-English ST models. Obtaining gold topic labels for our data would require substantial manual annotation, so we instead use the human translations from the 1K (train20h) training set utterances to train the NMF topic model with scikit-learn BIBREF14 
 Question: What is the architecture of the model?
Output: BIBREF5 to train neural sequence-to-sequence NMF topic model with scikit-learn BIBREF14
In fact, realizing the necessity of large text corpus for Sindhi, we started this research by collecting raw corpus from multiple web resource using web-scrappy framwork for extraction of news columns of daily Kawish and Awami Awaz Sindhi newspapers, Wikipedia dumps, short stories and sports news from Wichaar social blog, news from Focus Word press blog, historical writings, novels, stories, books from Sindh Salamat literary websites, novels, history and religious books from Sindhi Adabi Board and tweets regarding news and sports are collected from twitter. 
 Question: How is the data collected, which web resources were used?
Output: daily Kawish and Awami Awaz Sindhi newspapers Wikipedia dumps short stories and sports news from Wichaar social blog news from Focus Word press blog historical writings, novels, stories, books from Sindh Salamat literary website novels, history and religious books from Sindhi Adabi Board  tweets regarding news and sports are collected from twitter
We evaluate with all commonly-used metrics in question generation BIBREF13: BLEU-1 (B1), BLEU-2 (B2), BLEU-3 (B3), BLEU-4 (B4) BIBREF17, METEOR (MET) BIBREF18 and ROUGE-L (R-L) BIBREF19. We use the evaluation script released by Chen2015MicrosoftCC. 
 Question: What metrics do they use?
Output: BLEU-1 (B1), BLEU-2 (B2), BLEU-3 (B3), BLEU-4 (B4) BIBREF17, METEOR (MET) BIBREF18 and ROUGE-L (R-L) BIBREF19
We therefore propose attention models to infer the latent context, i.e., the series of posts that trigger an intervention. 
 Question: What type of latent context is used to predict instructor intervention?
Output: the series of posts that trigger an intervention
After having sentence representation, we use some classification models to classify input sentences. Those models will be described in detail in the section SECREF13. The first model is TextCNN (figure FIGREF2) proposed in BIBREF11. It only contains CNN blocks following by some Dense layers. The output of multiple CNN blocks with different kernel sizes is connected to each other.

The second model is VDCNN (figure FIGREF5) inspired by the research in BIBREF12. Like the TextCNN model, it contains multiple CNN blocks. The addition in this model is its residual connection.

The third model is a simple LSTM bidirectional model (figure FIGREF15). It contains multiple LSTM bidirectional blocks stacked to each other.

The fourth model is LSTMCNN (figure FIGREF24). Before going through CNN blocks, series of word embedding will be transformed by LSTM bidirectional block. The final model is the system named SARNN (figure FIGREF25). It adds an attention block between LTSM blocks. In this system, we use the Stacking method. In this method, the output of each model is not only class id but also the probability of each class in the set of three classes. This probability will become a feature for the ensemble model. The stacking ensemble model here is a simple full-connection model with input is all of probability that output from sub-model. The output is the probability of each class. 
 Question: What classifier do they use?
Output: Stacking method LSTMCNN SARNN simple LSTM bidirectional model TextCNN
We improve over the previous state-of-the-art BIBREF35 for VQA dataset by around 6% in BLEU score and 10% in METEOR score. In the VQG-COCO dataset, we improve over BIBREF5 by 3.7% and BIBREF36 by 3.5% in terms of METEOR scores. 
 Question: What were the previous state of the art benchmarks?
Output: BIBREF35 for VQA dataset BIBREF5 BIBREF36
We use the following three evaluation metrics:

Phoneme Error Rate (PER) is the Levenshtein distance between the predicted phoneme sequences and the gold standard phoneme sequences, divided by the length of the gold standard phoneme sequences.

Word Error Rate (WER) is the percentage of words in which the predicted phoneme sequence does not exactly match the gold standard phoneme sequence.

Word Error Rate 100 (WER 100) is the percentage of words in the test set for which the correct guess is not in the first 100 guesses of the system. 
 Question: what evaluation metrics were used?
Output: Phoneme Error Rate (PER) Word Error Rate (WER) Word Error Rate 100 (WER 100)
Logician is trained under the attention-based sequence-to-sequence paradigm, with three mechanisms: restricted copy mechanism to ensure literally honestness, coverage mechanism to alleviate the under extraction and over extraction problem, and gated dependency attention mechanism to incorporate dependency information.  
 Question: How is Logician different from traditional seq2seq models?
Output: restricted copy mechanism to ensure literally honestness, coverage mechanism to alleviate the under extraction and over extraction problem, and gated dependency attention mechanism to incorporate dependency information
In order to explore the problem space, we thus specifically target five binary classification problems addressed in BIBREF17 , BIBREF18 , i.e presence/absence of consonants, phonemic nasal, bilabial, high-front vowels and high-back vowels. 
 Question: What are the five different binary classification tasks?
Output:  presence/absence of consonants, phonemic nasal, bilabial, high-front vowels and high-back vowels.
For each qualifying diagnosis tweet we retrieve the timeline of the corresponding Twitter user using the Twitter user_timeline API endpoint . Subsequently, we remove all non-English tweets (Twitter API machine-detected“lang” field), all retweets, and tweets that contain “diagnos*” or “depress*”, but not a valid diagnosis statement. The resulting Depressed cohort contains 1,207 individuals and 1,759,644 tweets ranging from from May 2008 to September 2018. 
 Question: Do they report results only on English datasets?
Output: Yes
The study of the mathematical structure of grammar has indicated that the fundamental things making up sentences are not the words, but some atomic grammatical types, such as the noun-type and the sentence-type BIBREF23 , BIBREF24 , BIBREF25 . The transitive verb-type is not an atomic grammatical type, but a composite made up of two noun-types and one sentence-type. Hence, particularly interesting here is that atomic doesn't really mean smallest...

On the other hand, just like in particle physics where we have particles and anti-particles, the atomic types include types as well as anti-types. But unlike in particle physics, there are two kinds of anti-types, namely left ones and right ones. This makes language even more non-commutative than quantum theory! 
 Question: Do they break down word meanings into elementary particles as in the standard model of quantum theory?
Output: No
In this study, we investigate the efficacy of bias reduction during training by introducing a new loss function which encourages the language model to equalize the probabilities of predicting gendered word pairs like he and she.  
 Question: what kinds of male and female words are looked at?
Output: gendered word pairs like he and she
Table TABREF20 shows that, on WoZ and DSTC2 datasets, SIM model has the same number of parameters, which is only 23% and 19% of that in GLAD model. 
 Question: How do they measure model size?
Output: By the number of parameters.
We typically start by identifying the questions we wish to explore. Can text analysis provide a new perspective on a “big question” that has been attracting interest for years? Or can we raise new questions that have only recently emerged, for example about social media? For social scientists working in computational analysis, the questions are often grounded in theory, asking: How can we explain what we observe? Computational analysis of text motivated by these questions is insight driven: we aim to describe a phenomenon or explain how it came about. For example, what can we learn about how and why hate speech is used or how this changes over time? Is hate speech one thing, or does it comprise multiple forms of expression? Is there a clear boundary between hate speech and other types of speech, and what features make it more or less ambiguous? Sometimes we also hope to connect to multiple disciplines. For example, while focusing on the humanistic concerns of an archive, we could also ask social questions such as “is this archive more about collaborative processes, culture-building or norm creation?” or “how well does this archive reflect the society in which it is embedded?" BIBREF3 used quantitative methods to tell a story about Darwin's intellectual development—an essential biographical question for a key figure in the history of science. 
 Question: What kind of issues (that are not on the forefront of computational text analysis) do they tackle?
Output: identifying the questions we wish to explore Can text analysis provide a new perspective on a “big question” that has been attracting interest for years? How can we explain what we observe? hope to connect to multiple disciplines
The flexibility of the DNN model allowed us to include many more surface level features such as affixes, leading and trailing characters in words and stems, and the presence of words in large gazetteers of named entities. As we show later, these additional features significantly lowered CEER. 
 Question: what surface-level features are used?
Output: affixes, leading and trailing characters in words and stems, and the presence of words in large gazetteers of named entities
Moreover, the add and add-gated variants of Bertram perform surprisingly well for more frequent words, improving the score for WNLaMPro-medium by 50% compared to BERT$_\text{base}$ and 31% compared to Attentive Mimicking. 
 Question: How much is representaton improved for rare/medum frequency words compared to standalone BERT and previous work?
Output: improving the score for WNLaMPro-medium by 50% compared to BERT$_\text{base}$ and 31% compared to Attentive Mimicking
Datasets
We conduct experiments on two Chinese question answering datasets from NLPCC-2016 evaluation task BIBREF13 . DBQA is a document based question answering dataset.  KBRE is a knowledge based relation extraction dataset. 
 Question: Which dataset(s) do they evaluate on?
Output: DBQA KBRE
We use two basic features:

Parts of Speech (POS) tags: We use the POS tagger of NLTK to tag the tweet texts BIBREF0 . We use counts of noun, adjective, adverb, verb words in a tweet as POS features.

Prior polarity of the words: We use a polarity dictionary BIBREF3 to get the prior polarity of words. The dictionary contains positive, negative and neutral words along with their polarity strength (weak or strong). The polarity of a word is dependent on its POS tag. For example, the word `excuse' is negative when used as `noun' or `adjective', but it carries a positive sense when used as a `verb'. We use the tags produced by NLTK postagger while selecting the prior polarity of a word from the dictionary. We also employ stemming (Porter Stemmer implementation from NLTK) while performing the dictionary lookup to increase number of matches. We use the counts of weak positive words, weak negative words, strong positive words and strong negative words in a tweet as features.

We have also explored some advanced features that helps improve detecting sentiment of tweets.

Emoticons: We use the emoticon dictionary from BIBREF2 , and count the positive and negtive emocicons for each tweet.

The sentiment of url: Since almost all the articles are written in well-formatted english, we analyze the sentiment of the first paragraph of the article using Standford Sentiment Analysis tool BIBREF4 . It predicts sentiment for each sentence within the article. We calculate the fraction of sentences that are negative, positive, and neutral and use these three values as features.

Hashtag: We count the number of hashtags in each tweet.

Capitalization: We assume that capitalization in the tweets has some relationship with the degree of sentiment. We count the number of words with capitalization in the tweets.

Retweet: This is a boolean feature indicating whether the tweet is a retweet or not.

User Mention: A boolean feature indicating whether the tweet contains a user mention.

Negation: Words like `no', `not', `won't' are called negation words since they negate the meaning of the word that is following it. As for example `good' becomes `not good'. We detect all the negation words in the tweets. If a negation word is followed by a polarity word, then we negate the polarity of that word. For example, if `good' is preceeded by a `not', we change the polarity from `weak positive' to `weak negative'.

Text Feature: We use tf-idf based text features to predict the sentiment of a tweet. We perform tf-idf based scoring of words in a tweet and the hashtags present in the tweets. We use the tf-idf vectors to train a classifier and predict the sentiment. This is then used as a stacked prediction feature in the final classifier. 
 Question: What linguistic features are used?
Output: Parts of Speech (POS) tags Prior polarity of the words Capitalization Negation Text Feature
We apply our adaptively sparse Transformers on four machine translation tasks.  IWSLT 2017 German $\rightarrow $ English BIBREF27: 200K sentence pairs.

KFTT Japanese $\rightarrow $ English BIBREF28: 300K sentence pairs.

WMT 2016 Romanian $\rightarrow $ English BIBREF29: 600K sentence pairs.

WMT 2014 English $\rightarrow $ German BIBREF30: 4.5M sentence pairs. 
 Question: What tasks are used for evaluation?
Output: four machine translation tasks: German -> English, Japanese -> English, Romanian -> English, English -> German
Secondly, we examine the effect of Washington Post on the views of the users. This is done by looking at the sentiments of the candidates (to predict winners) of a debate before and after the winners are announced by the experts in Washington Post. This way, we can see if Washington Post has had any effect on the sentiments of the users. One can see the winners suggested by the Washington Post in Table TABREF35.  
 Question: How do you establish the ground truth of who won a debate?
Output: experts in Washington Post
The main reason is that the datasets only contain a small portion of multi-aspect sentences with different polarities. The distraction of attention will not impact the sentiment prediction much in single-aspect sentences or multi-aspect sentences with the same polarities. 
 Question: Is the model evaluated against the baseline also on single-aspect sentences?
Output: No
Using sequence-to-sequence networks, the approach here is jointly training annotated utterance-level intents and slots/intent keywords by adding / tokens to the beginning/end of each utterance, with utterance-level intent-type as labels of such tokens. Our approach is an extension of BIBREF2 , in which only an term is added with intent-type tags associated to this sentence final token, both for LSTM and Bi-LSTM cases. However, we experimented with adding both and terms as Bi-LSTMs will be used for seq2seq learning, and we observed that slightly better results can be achieved by doing so. The idea behind is that, since this is a seq2seq learning problem, at the last time step (i.e., prediction at ) the reverse pass in Bi-LSTM would be incomplete (refer to Fig. FIGREF24 (a) to observe the last Bi-LSTM cell). Therefore, adding token and leveraging the backward LSTM output at first time step (i.e., prediction at ) would potentially help for joint seq2seq learning. An overall network architecture can be found in Fig. FIGREF30 for our joint models. We will report the experimental results on two variations (with and without intent keywords) as follows:

Joint-1: Seq2seq Bi-LSTM for utterance-level intent detection (jointly trained with slots)

Joint-2: Seq2seq Bi-LSTM for utterance-level intent detection (jointly trained with slots & intent keywords) 
 Question: What is shared in the joint model?
Output: jointly trained with slots
The Wikipedia revision dumps that were previously introduced by Leskovec et al. leskovec2010governance contain eight GB (compressed size) revision edits with meta data. 
 Question: How large is the Wikipedia revision dump dataset?
Output: eight GB
Then, we compute embeddings of concepts (by GloVe) for interview descriptions and for examination descriptions separately. The simplest way to generate text embeddings based on term embeddings is to use some kind of aggregation of term embeddings such as an average. This approach was tested for example by BIBREF21 and BIBREF13 . BIBREF22 computed a weighted mean of term embeddings by the construction of a loss function and training weights by the gradient descent method.

Final embeddings for visits are obtained by concatenation of average embeddings calculated separately for the interview and for the medical examination, see Figure FIGREF4 . 
 Question: Which word embeddings do they use to represent medical visits?
Output: GloVe concatenation of average embeddings calculated separately for the interview and for the medical examination
Among all three pre-training tasks, SR works slightly better than the other two tasks (i.e., NSG and MDG). 
 Question: Which of the three pretraining tasks is the most helpful?
Output: SR
We employ two sources of e-book annotation data: (i) editor tags, and (ii) Amazon search terms. For editor tags, we collect data of 48,705 e-books from 13 publishers, namely Kunstmann, Delius-Klasnig, VUR, HJR, Diogenes, Campus, Kiwi, Beltz, Chbeck, Rowohlt, Droemer, Fischer and Neopubli.  For the Amazon search terms, we collect search query logs of 21,243 e-books for 12 months (i.e., November 2017 to October 2018).  
 Question: what dataset was used?
Output: 48,705 e-books from 13 publishers search query logs of 21,243 e-books for 12 months
To validate the effectiveness of AEM for extracting events from social media (e.g. Twitter) and news media sites (e.g. Google news), three datasets (FSD BIBREF12 , Twitter, and Google datasets) are employed. 
 Question: What datasets are used?
Output: FSD BIBREF12 , Twitter, and Google datasets
To collect a diverse training dataset, we have randomly sampled 1000 posts each from the subreddits politics, business, science, and AskReddit, and 1000 additional posts from the Reddit frontpage. 
 Question: what are the topics pulled from Reddit?
Output: politics, business, science, and AskReddit, and 1000 additional posts from the Reddit frontpage.
For our study, we used the number of retweets to single-out those that went viral within our sample. Tweets within that subset (viral tweets hereafter) are varied and relate to different topics. We consider that a tweet contains fake news if its text falls within any of the following categories described by Rubin et al. BIBREF7 (see next section for the details of such categories): serious fabrication, large-scale hoaxes, jokes taken at face value, slanted reporting of real facts and stories where the truth is contentious. The dataset BIBREF8 , manually labelled by an expert, has been publicly released and is available to researchers and interested parties. 
 Question: How did they determine fake news tweets?
Output: an expert annotator determined if the tweet fell under a specific category
The CoNLL-2012 shared task BIBREF21 corpus is used as the evaluation dataset, which is selected from the Ontonotes 5.0. 
 Question: What dataset do they evaluate their model on?
Output: CoNLL-2012 shared task BIBREF21 corpus
Dataset: We perform experiments on two widely-used tasks for the English-to-Japanese language pair: KFTT BIBREF12 and BTEC BIBREF13 . KFTT is a collection of Wikipedia article about city of Kyoto and BTEC is a travel conversation corpus. BTEC is an easier translation task than KFTT, because KFTT covers a broader domain, has a larger vocabulary of rare words, and has relatively long sentences.  
 Question: What datasets were used?
Output: KFTT BIBREF12 and BTEC BIBREF13
We compare our personalized models against two baselines. The first is a name-based Nearest-Neighbor model (NN). We initially adapted the Neural Checklist Model of BIBREF0 as a baseline; however, we ultimately use a simple Encoder-Decoder baseline with ingredient attention (Enc-Dec), which provides comparable performance and lower complexity. 
 Question: What are the baseline models?
Output: name-based Nearest-Neighbor model (NN) Encoder-Decoder baseline with ingredient attention (Enc-Dec)
The corpus consists of 53 documents, which contain an average number of 156.1 sentences per document, each with 19.55 tokens on average. The corpus comprises 8,275 sentences and 167,739 words in total. 
 Question: How large is the corpus?
Output: 8,275 sentences and 167,739 words in total
Arab-Tweet. For modeling age and gender, we use Arap-Tweet BIBREF19 , which we will refer to as Arab-Tweet. UBC Twitter Gender Dataset. We also develop an in-house Twitter dataset for gender. We manually labeled 1,989 users from each of the 21 Arab countries. The dialect identification model in AraNet is based on our winning system in the MADAR shared task 2 BIBREF20 as described in BIBREF12. The corpus is divided into train, dev and test, and the organizers masked test set labels. We make use of two datasets, the LAMA-DINA dataset from BIBREF22, a Twitter dataset with a combination of gold labels from BIBREF23 and distant supervision labels. The rest of the dataset is only labeled using distant supervision (LAMA-DIST) ($182,605$ tweets) . For more information about the dataset, readers are referred to BIBREF22.  We use the dataset for irony identification on Arabic tweets released by IDAT@FIRE2019 shared-task BIBREF24. We collect 15 datasets related to sentiment analysis of Arabic, including MSA and dialects BIBREF25, BIBREF26, BIBREF27, BIBREF1, BIBREF28, BIBREF29, BIBREF30, BIBREF31, BIBREF32, BIBREF33, BIBREF34. Table TABREF28 shows all the corpora we use.  
 Question: What datasets are used in training?
Output: Arap-Tweet BIBREF19  an in-house Twitter dataset for gender the MADAR shared task 2 BIBREF20 the LAMA-DINA dataset from BIBREF22 LAMA-DIST Arabic tweets released by IDAT@FIRE2019 shared-task BIBREF24 BIBREF25, BIBREF26, BIBREF27, BIBREF1, BIBREF28, BIBREF29, BIBREF30, BIBREF31, BIBREF32, BIBREF33, BIBREF34
A gated neural network is employed over character embeddings to generate distributed representations of candidate words, which are sent to a LSTM model. 
 Question: Which neural network architectures are employed?
Output: gated neural network
This working note presents RNN and LSTM based embedding system for social media health text classification. Though the data sets of task 1 and task 2 are limited, this paper proposes RNN and LSTM based embedding method. 
 Question: What type of RNN is used?
Output: RNN LSTM
45 clinically interpretable features per admission were extracted as inputs to the readmission risk classifier. These features can be grouped into three categories (See Table TABREF5 for complete list of features):

Sociodemographics: gender, age, marital status, etc.

Past medical history: number of previous admissions, history of suicidality, average length of stay (up until that admission), etc.

Information from the current admission: length of stay (LOS), suicidal risk, number and length of notes, time of discharge, evaluation scores, etc.

The Current Admission feature group has the most number of features, with 29 features included in this group alone. These features can be further stratified into two groups: `structured' clinical features and `unstructured' clinical features.

Feature Extraction ::: Structured Features
Structure features are features that were identified on the EHR using regular expression matching and include rating scores that have been reported in the psychiatric literature as correlated with increased readmission risk, such as Global Assessment of Functioning, Insight and Compliance:

Global Assessment of Functioning (GAF): The psychosocial functioning of the patient ranging from 100 (extremely high functioning) to 1 (severely impaired) BIBREF13.

Insight: The degree to which the patient recognizes and accepts his/her illness (either Good, Fair or Poor).

Compliance: The ability of the patient to comply with medication and to follow medical advice (either Yes, Partial, or None).

These features are widely-used in clinical practice and evaluate the general state and prognosis of the patient during the patient's evaluation.

Feature Extraction ::: Unstructured Features
Unstructured features aim to capture the state of the patient in relation to seven risk factor domains (Appearance, Thought Process, Thought Content, Interpersonal, Substance Use, Occupation, and Mood) from the free-text narratives on the EHR. These seven domains have been identified as associated with readmission risk in prior work BIBREF14.

These unstructured features include: 1) the relative number of sentences in the admission notes that involve each risk factor domain (out of total number of sentences within the admission) and 2) clinical sentiment scores for each of these risk factor domains, i.e. sentiment scores that evaluate the patient’s psychosocial functioning level (positive, negative, or neutral) with respect to each of these risk factor domain. 
 Question: What features are used?
Output: Sociodemographics: gender, age, marital status, etc. Past medical history: number of previous admissions, history of suicidality, average length of stay (up until that admission), etc. Information from the current admission: length of stay (LOS), suicidal risk, number and length of notes, time of discharge, evaluation scores, etc.
Metrics. We use tolerance accuracy BIBREF16, which measures how far away the predicted span is from the gold standard span, as a metric. The rationale behind the metric is that, in practice, it suffices to recommend a rough span which contains the answer – a difference of a few seconds would not matter much to the user. Metrics. We used accuracy and MRR (Mean Reciprocal Ranking) as metrics.  Metrics. To evaluate our pipeline approach we use overall accuracy after filtering and accuracy given that the segment is in the top 10 videos.  
 Question: What evaluation metrics were used in the experiment?
Output: For sentence-level prediction they used tolerance accuracy, for segment retrieval accuracy and MRR and for the pipeline approach they used overall accuracy
The News Category Dataset BIBREF11 is a collection of headlines published by HuffPost BIBREF12 between 2012 and 2018, and was obtained online from Kaggle BIBREF13. The full dataset contains 200k news headlines with category labels, publication dates, and short text descriptions. 
 Question: What news dataset was used?
Output: collection of headlines published by HuffPost BIBREF12 between 2012 and 2018
Analysis of challenges from UTD
Our system relies on the pseudotext produced by ZRTools (the only freely available UTD system we are aware of), which presents several challenges for MT.  Assigning wrong words to a cluster
Since UTD is unsupervised, the discovered clusters are noisy.  Splitting words across different clusters
Although most UTD matches are across speakers, recall of cross-speaker matches is lower than for same-speaker matches. As a result, the same word from different speakers often appears in multiple clusters, preventing the model from learning good translations. UTD is sparse, giving low coverage We found that the patterns discovered by ZRTools match only 28% of the audio. This low coverage reduces training data size, affects alignment quality, and adversely affects translation, which is only possible when pseudoterms are present. 
 Question: what challenges are identified?
Output: Assigning wrong words to a cluster Splitting words across different clusters sparse, giving low coverage
Since we were also interested in whether argumentation differs across registers, we included four different registers — namely (1) user comments to newswire articles or to blog posts, (2) posts in discussion forums (forum posts), (3) blog posts, and (4) newswire articles.  
 Question: How is the data in the new corpus come sourced?
Output: user comments to newswire articles or to blog posts forum posts blog posts newswire articles
Finally, we transform text to be classified into scalars representing their distance from the constructed hate vector and use these as input to a Random Forest classifier. 
 Question: What classifier did they use?
Output: Random Forest
Each dataset consisted of five Arabic dialects: Egyptian (EGY), Levantine (LEV), Gulf (GLF), North African (NOR), and Modern Standard Arabic (MSA). 
 Question: Which are the four Arabic dialects?
Output: Egyptian (EGY) Levantine (LEV) Gulf (GLF) North African (NOR)
For instance, the sentence “The girls sang a song and they danced” is translated into French as “Les filles ont chanté une chanson et ils ont dansé” by Google Translate (GT), Bing Translate, and Yandex. 
 Question: Do the authors conduct experiments on the tasks mentioned?
Output: Yes
We decided to evaluate our model using weighted F1-score, i.e., the per-class F1 score is calculated and averaged by weighting each label by its support.  
 Question: what evaluation metrics were used?
Output: weighted F1-score
In this paper we present our work, which is a part of the SociaL Media And Harassment Competition of the ECML PKDD 2019 Conference.   We are using the dataset of the competition, which includes text from tweets having the aforementioned categories. 
 Question: What were the datasets used in this paper?
Output: The dataset from the SociaL Media And Harassment Competition of the ECML PKDD 2019 Conference.
Past research took a reductionist approach, separately considering these two problems of “what” and “how” via content selection and question construction.  In contrast, neural models motivate an end-to-end architectures. Deep learned frameworks contrast with the reductionist approach, admitting approaches that jointly optimize for both the “what” and “how” in an unified framework.  
 Question: What learning paradigms do they cover in this survey?
Output: Considering "What" and "How" separately versus jointly optimizing for both.
We proposed joint learning NLP models that use convolutional neural network (CNN) BIBREF8 and bi-directional long short-term memory (BiLSTM) BIBREF9, BIBREF10 as basic units. Our models can automatically extract the key elements from the sexual harassment stories and at the same time categorize the stories in different dimensions. The proposed models outperformed the single task models, and achieved higher than previously reported accuracy in classifications of harassment forms BIBREF6. 
 Question: What model did they use?
Output: joint learning NLP models that use convolutional neural network (CNN) BIBREF8 and bi-directional long short-term memory (BiLSTM)
All data were downloaded from Twitter in two separate batches using the “twint" scraping tool BIBREF5 to sample historical tweets for several different search terms; queries always included either “climate change" or “global warming", and further included disaster-specific search terms (e.g., “bomb cyclone," “blizzard," “snowstorm," etc.).  
 Question: Do they report results only on English data?
Output: Yes
Evaluation metric. Since our problem is imbalanced, we use the F1 score as our evaluation metric. For the tagging approach, we average the labels of words with the same stemmed version to obtain a single prediction for the stemmed word. To establish a baseline, we consider a random method that predicts the positive label with 0.15 probability (the base rate of positive instances). 
 Question: What metrics are used in evaluation of this task?
Output: F1 score
Task processing: converting data exploration tasks to algebraic operations on the embedding space by following task-specific conversion templates. Some important tasks and their conversion templates are discussed in Section SECREF5.

Query processing: executing semantic query on the embedding space and return results. Note that the algebraic operations on embedding vectors are linear and can be performed in parallel. Therefore, the semantic query is efficient. 
 Question: What data exploration is supported by the analysis of these semantic structures?
Output: Task processing: converting data exploration tasks to algebraic operations on the embedding space Query processing: executing semantic query on the embedding space and return results
As of June 2019, AA had $\sim $50K entries, however, this includes some number of entries that are not truly research publications (for example, forewords, prefaces, table of contents, programs, schedules, indexes, calls for papers/participation, lists of reviewers, lists of tutorial abstracts, invited talks, appendices, session information, obituaries, book reviews, newsletters, lists of proceedings, lifetime achievement awards, erratum, and notes). We discard them for the analyses here. (Note: CL journal includes position papers like squibs, letter to editor, opinion, etc. We do not discard them.) We are then left with 44,896 articles. 
 Question: How many papers are used in experiment?
Output: 44,896 articles
Our dataset covers 218 videos from NALCS and 103 from LMS for a total of 321 videos from week 1 to week 9 in 2017 spring series from each tournament.  
 Question: How big was the dataset presented?
Output: 321 videos
The unlabeled set of development data was used in the training of both the UBM and the i-vector extractor. 
 Question: Do they single out a validation set from the fixed SRE training set?
Output: No
Pretraining. We collect three years of online news articles from June 2016 to June 2019. We filter out articles overlapping with the evaluation data on media domain and time range. We then conduct several data cleaning strategies. 
 Question: What did they pretrain the model on?
Output: hree years of online news articles from June 2016 to June 2019