niryuu/sni-each-converted huggingface dataset:
Our lexical features include 1-, 2-, and 3-grams in both word and character levels. We also use the number of characters and the number of words as features. We then use all the POS tags with their corresponding tf-idf values as our syntactic features and feature values, respectively. Firstly, we employ 300-dimensional pre-trained word embeddings from GloVe BIBREF29 to compute a tweet embedding as the average of the embeddings of words in the tweet. Secondly, we apply the latent semantic indexing BIBREF30 to capture the underlying semantics of the dataset. Here, each tweet is represented as a vector of 100 dimensions. Thirdly, we also extract tweet representation by applying the Brown clustering algorithm BIBREF31 , BIBREF32 —a hierarchical clustering algorithm which groups the words with similar meaning and syntactical function together. Motivated by the verbal irony by means of polarity contrast, such as “I really love this year's summer; weeks and weeks of awful weather”, we use the number of polarity signals appearing in a tweet as the polarity features. The signals include positive words (e.g., love), negative words (e.g., awful), positive emoji icon and negative emoji icon. We use the sentiment dictionaries provided by BIBREF33 to identify positive and negative words in a tweet. We further use boolean features that check whether or not a negation word is in a tweet (e.g., not, n't). 
 Question: What type of lexical, syntactic, semantic and polarity features are used?
Output: Our lexical features include 1-, 2-, and 3-grams in both word and character levels. number of characters and the number of words POS tags 300-dimensional pre-trained word embeddings from GloVe latent semantic indexing tweet representation by applying the Brown clustering algorithm positive words (e.g., love), negative words (e.g., awful), positive emoji icon and negative emoji icon boolean features that check whether or not a negation word is in a tweet
The annotation scheme was developed to capture three aspects of classroom talk that are theorized in the literature as important to discussion quality and learning opportunities: argumentation (the process of systematically reasoning in support of an idea), specificity (the quality of belonging or relating uniquely to a particular subject), and knowledge domain (area of expertise represented in the content of the talk). 
 Question: how do they measure discussion quality?
Output: Measuring three aspects: argumentation, specificity and knowledge domain.
To enable this, we use a bifocal attention mechanism which computes an attention over fields at a macro level and over values at a micro level. We then fuse these attention weights such that the attention weight for a field also influences the attention over the values within it. Fused Bifocal Attention Mechanism
Intuitively, when a human writes a description from a table she keeps track of information at two levels. At the macro level, it is important to decide which is the appropriate field to attend to next and at a micro level (i.e., within a field) it is important to know which values to attend to next. To capture this behavior, we use a bifocal attention mechanism as described below. Fused Attention: Intuitively, the attention weights assigned to a field should have an influence on all the values belonging to the particular field. To ensure this, we reweigh the micro level attention weights based on the corresponding macro level attention weights. In other words, we fuse the attention weights at the two levels as: DISPLAYFORM0

where INLINEFORM0 is the field corresponding to the INLINEFORM1 -th value, INLINEFORM2 is the macro level context vector. 
 Question: What is a bifocal attention mechanism?
Output: At the macro level, it is important to decide which is the appropriate field to attend to next micro level (i.e., within a field) it is important to know which values to attend to next fuse the attention weights at the two levels
The task presented here was easy and simple to analyze, however, future work may be done on natural language tasks. If these properties hold it might indicate that a new evaluation paradigm for NLP should be pushed; one that emphasizes performance on uncharacteristic (but structurally sound) inputs in addition to the data typically seen in training. 
 Question: Can the findings of this paper be generalized to a general-purpose task?
Output: Yes
Notably, automatic translations of TED talks contain more words than the corresponding reference translation, which means that machine-translated texts of this type have also more potential tokens to enter in a coreference relation, and potentially indicating a shining through effect. The same does not happen with the news test set. We see how NMT translations increase the number of mentions about $30\%$ with respect to human references showing even a more marked explicitation effect than human translations do. 
 Question: What translationese effects are seen in the analysis?
Output: potentially indicating a shining through effect explicitation effect
We find that dynamic communities, such as Seahawks or starcraft, have substantially higher rates of monthly user retention than more stable communities (Spearman's INLINEFORM0 = 0.70, INLINEFORM1 0.001, computed with community points averaged over months; Figure FIGREF11 .A, left). Similarly, more distinctive communities, like Cooking and Naruto, exhibit moderately higher monthly retention rates than more generic communities (Spearman's INLINEFORM2 = 0.33, INLINEFORM3 0.001; Figure FIGREF11 .A, right). As with monthly retention, we find a strong positive relationship between a community's dynamicity and the average number of months that a user will stay in that community (Spearman's INLINEFORM0 = 0.41, INLINEFORM1 0.001, computed over all community points; Figure FIGREF11 .B, left). This verifies that the short-term trend observed for monthly retention translates into longer-term engagement and suggests that long-term user retention might be strongly driven by the extent to which a community continually provides novel content. Interestingly, there is no significant relationship between distinctiveness and long-term engagement (Spearman's INLINEFORM2 = 0.03, INLINEFORM3 0.77; Figure FIGREF11 .B, right). Thus, while highly distinctive communities like RandomActsOfMakeup may generate focused commitment from users over a short period of time, such communities are unlikely to retain long-term users unless they also have sufficiently dynamic content. 
 Question: How do the various social phenomena examined manifest in different types of communities?
Output: Dynamic communities have substantially higher rates of monthly user retention than more stable communities. More distinctive communities exhibit moderately higher monthly retention rates than more generic communities. There is also a strong positive relationship between a community's dynamicity and the average number of months that a user will stay in that community - a short-term trend observed for monthly retention translates into longer-term engagement and suggests that long-term user retention might be strongly driven by the extent to which a community continually provides novel content.
We compare our best F1 score with other state-of-the-art approaches in table TABREF39 , which shows our model has competitive advantage in dealing with drug-drug interaction extraction. 
 Question: By how much does their model outperform existing methods?
Output: Answer with content missing: (Table II) Proposed model has F1 score of  0.7220 compared to 0.7148 best state-state-of-the-art result.
We adopt the DSTC2 BIBREF20 dataset and Maluuba BIBREF21 dataset to evaluate our proposed model. 
 Question: What two benchmark datasets are used?
Output: DSTC2 Maluuba
In this context, we propose a robust multilingual sentiment analysis method, tested in eight different languages: Spanish, English, Italian, Arabic, German, Portuguese, Russian and Swedish. 
 Question: What eight language are reported on?
Output: Spanish, English, Italian, Arabic, German, Portuguese, Russian and Swedish
We found interesting patterns which are learned by our model and help understand these monolingual gains. For example, a recurring pattern is that words in English which are translated to the same word, or to semantically close words, in the target language end up closer together after our transformation. For example, in the case of English-Spanish the following pairs were among the pairs whose similarity increased the most by applying our transformation: cellphone-telephone, movie-film, book-manuscript or rhythm-cadence, which are either translated to the same word in Spanish (i.e., teléfono and película in the first two cases) or are already very close in the Spanish space. More generally, we found that word pairs which move together the most tend to be semantically very similar and belong to the same domain, e.g., car-bicycle, opera-cinema, or snow-ice.

 
 Question: Why does the model improve in monolingual spaces as well? 
Output: because word pair similarity increases if the two words translate to similar parts of the cross-lingual embedding space
Using this annotation model, we create a new large publicly available dataset of English tweets. 
 Question: In what language are the tweets?
Output: English
First, we analyze the types of structures in code-mixed puns and classify them into two categories namely intra-sequential and intra-word.  
 Question: What are the categories of code-mixed puns?
Output: intra-sequential and intra-word
We evaluated our detection models on three benchmarks: the FCE test data (41K tokens) and the two alternative annotations of the CoNLL 2014 Shared Task dataset (30K tokens) BIBREF3 .  
 Question: Which annotated corpus did they use?
Output:  FCE test data (41K tokens) and the two alternative annotations of the CoNLL 2014 Shared Task dataset (30K tokens)
We test the existing debiasing approaches, CDA and REG, as well but since BIBREF5 reported that results fluctuate substantially with different REG regularization coefficients, we perform hyperparameter tuning and report the best results in Table TABREF12 . 
 Question: which existing strategies are compared?
Output: CDA REG
In this way, an attention vector sequence is segmented into several subsequences and each subsequence represents the attention of one word. Then, motivated by the psycholinguistic finding that readers are likely to pay approximate attention to each character in one Chinese word, we devise an appropriate aggregation module to fuse the inner-word character attention. 
 Question: How does the fusion method work?
Output: ttention vector sequence is segmented into several subsequences and each subsequence represents the attention of one word we devise an appropriate aggregation module to fuse the inner-word character attention
We have observed that the number of epochs is an important parameter and its increase leads to results that rank our two worst models almost equal, or even better than others. 
 Question: How does different parameter settings impact the performance and semantic capacity of resulting model?
Output: number of epochs is an important parameter and its increase leads to results that rank our two worst models almost equal, or even better than others
In case of polysemous words, only the first word sense (usually the most common) is taken into account. 
 Question: How do they handle polysemous words in their entity library?
Output: only the first word sense (usually the most common) is taken into account
In contrast to other work, we do not show the documents to the workers at all, but provide only a description of the document cluster's topic along with the propositions. This ensures that tasks are small, simple and can be done quickly (see Figure FIGREF4 ). 
 Question: How were crowd workers instructed to identify important elements in large document collections?
Output: provide only a description of the document cluster's topic along with the propositions
We combine 13k questions gathered from this pipeline with an additional 3k questions with yes/no answers from the NQ training set to reach a total of 16k questions.  
 Question: what is the size of BoolQ dataset?
Output:  16k questions
A total of 27 different genres were scraped. 
 Question: how many movie genres do they explore?
Output: 27
We carried out two human evaluations using Mechanical Turk to compare the performance of our model and the baseline. 
 Question: Is there any human evaluation involved in evaluating this famework?
Output: Yes
The gated mechanism learn the domain agnostic representations. They together control the information that has to flow through further fully connected output layer after max pooling. The effectiveness of gated architectures rely on the idea of training a gate with sole purpose of identifying a weightage. In the task of sentiment analysis this weightage corresponds to what weights will lead to a decrement in final loss or in other words, most accurate prediction of sentiment. In doing so, the gate architecture learns which words or n-grams contribute to the sentiment the most, these words or n-grams often co-relate with domain independent words. On the other hand the gate gives less weightage to n-grams which are largely either specific to domain or function word chunks which contribute negligible to the overall sentiment. This is what makes gated architectures effective at Domain Adaptation. We see that gated architectures almost always outperform recurrent, attention and linear models BoW, TFIDF, PV. This is largely because while training and testing on same domains, these models especially recurrent and attention based may perform better. However, for Domain Adaptation, as they lack gated structure which is trained in parallel to learn importance, their performance on target domain is poor as compared to gated architectures. As gated architectures are based on convolutions, they exploit parallelization to give significant boost in time complexity as compared to other models.  
 Question: Are there conceptual benefits to using GCNs over more complex architectures like attention?
Output: Yes
We now consider the following problem: given two BabelNet categories $A$ and $B$, predict whether they are likely to be conceptual neighbors based on the sentences from a text corpus in which they are both mentioned. To train such a classifier, we use the distant supervision labels from Section SECREF8 as training data. Once this classifier has been trained, we can then use it to predict conceptual neighborhood for categories for which only few instances are known. To find sentences in which both $A$ and $B$ are mentioned, we rely on a disambiguated text corpus in which mentions of BabelNet categories are explicitly tagged. Such a disambiguated corpus can be automatically constructed, using methods such as the one proposed by BIBREF30 mancini-etal-2017-embedding, for instance. For each pair of candidate categories, we thus retrieve all sentences where they co-occur. Next, we represent each extracted sentence as a vector.  
 Question: How they indentify conceptual neighbours?
Output: Once this classifier has been trained, we can then use it to predict conceptual neighborhood for categories for which only few instances are known.
We compared our models with the following state-of-the-art baselines:

Sequence to Sequence (Seq2Seq): A simple encoder-decoder model which concatenates four sentences to a long sentence with an attention mechanism BIBREF31 .

Hierarchical LSTM (HLSTM): The story context is represented by a hierarchical LSTM: a word-level LSTM for each sentence and a sentence-level LSTM connecting the four sentences BIBREF29 . A hierarchical attention mechanism is applied, which attends to the states of the two LSTMs respectively.

HLSTM+Copy: The copy mechanism BIBREF32 is applied to hierarchical states to copy the words in the story context for generation.

HLSTM+Graph Attention(GA): We applied multi-source attention HLSTM where commonsense knowledge is encoded by graph attention.

HLSTM+Contextual Attention(CA): Contextual attention is applied to represent commonsense knowledge. 
 Question: Did they compare to Transformer based large language models?
Output: No
The improvement over all benchmark datasets also shows that conditional BERT is a general augmentation method for multi-labels sentence classification tasks. 
 Question: Does the new objective perform better than the original objective bert is trained on?
Output: Yes
Using the distribution of the individual words in a category, we can compile distributions for the entire category, and therefore generate maps for these word categories.  
 Question: Do they build a model to automatically detect demographic, lingustic or psycological dimensons of people?
Output: No
We use the existing large-scale antonym and synonym pairs previously used by Nguyen:16. Originally, the data pairs were collected from WordNet BIBREF9 and Wordnik. 
 Question: What dataset do they use to evaluate their method?
Output: antonym and synonym pairs collected from WordNet BIBREF9 and Wordnik
An extrinsic evaluation was carried out on the task of Open IE BIBREF7. 
 Question: Is the semantic hierarchy representation used for any task?
Output: Yes, Open IE
Since those sets are expected to reflect social norms, they are referred as Dos and Don'ts hereafter. Analogously, some of the negative words just describe inappropriate behaviour, like slur or misdeal, whereas others are real crimes as murder. 
 Question: Do they report results only on English data?
Output: Yes
For sentiment classification, we systematically study the effect of character-level adversarial attacks on two architectures and four different input formats.  We also consider the task of paraphrase detection. 
 Question: What end tasks do they evaluate on?
Output: Sentiment analysis and paraphrase detection under adversarial attacks
We show that by determining and integrating heterogeneous set of features from different modalities – aesthetic features from posted images (colorfulness, hue variance, sharpness, brightness, blurriness, naturalness), choice of profile picture (for gender, age, and facial expression), the screen name, the language features from both textual content and profile's description (n-gram, emotion, sentiment), and finally sociability from ego-network, and user engagement – we can reliably detect likely depressed individuals in a data set of 8,770 human-annotated Twitter users. 
 Question: What is the source of the visual data? 
Output: Profile pictures from the Twitter users' profiles.
Both systems were optimized on the tst2014 using Minimum error rate training BIBREF20 . A detailed description of the systems can be found in BIBREF21 . 
 Question: How is the PBMT system trained?
Output: systems were optimized on the tst2014 using Minimum error rate training BIBREF20
We asked for two distinct paraphrases of each sentence because we believe that a good sentence embedding should put paraphrases close together in vector space.

Several modification types were specifically selected to constitute a thorough test of embeddings. In different meaning, the annotators should create a sentence with some other meaning using the same words as the original sentence. Other transformations which should be difficult for embeddings include minimal change, in which the sentence meaning should be significantly changed by using only very small modification, or nonsense, in which words of the source sentence should be shuffled so that it is grammatically correct, but without any sense. 
 Question: What annotations are available in the dataset?
Output: For each source sentence, transformation sentences that are transformed according to some criteria (paraphrase, minimal change etc.)
The results of multilingual training in which the modeling unit is syllables are presented in Table 5. All error rates are the weighted averages of all evaluated speakers. Here, `+ both' represents the result of training with both JNAS and WSJ corpora. The multilingual training is effective in the speaker-open setting, providing a relative WER improvement of 10%. The JNAS corpus was more helpful than the WSJ corpus because of the similarities between Ainu and Japanese language. 
 Question: How big are improvements with multilingual ASR training vs single language training?
Output: relative WER improvement of 10%.
We compare our approach with several methods BIBREF1 , BIBREF31 , BIBREF11 , BIBREF8 , BIBREF10 , BIBREF39 in two cross-domain settings. Using string kernels, Giménez-Pérez et al. BIBREF10 reported better performance than SST BIBREF31 and KE-Meta BIBREF11 in the multi-source domain setting. In addition, we compare our approach with SFA BIBREF1 , CORAL BIBREF8 and TR-TrAdaBoost BIBREF39 in the single-source setting. Transductive string kernels. We present a simple and straightforward approach to produce a transductive similarity measure suitable for strings. Our transductive kernel classifier (TKC) approach is composed of two learning iterations.  
 Question: What machine learning algorithms are used?
Output: string kernels SST KE-Meta SFA CORAL TR-TrAdaBoost Transductive string kernels transductive kernel classifier
Results in Table TABREF13 show ten probes. Again, we see the performance of baseline ELMo-transformer and mSynC are similar, with mSynC doing slightly worse on 7 out of 9 tasks. As we would expect, on the probe for predicting chunk tags, mSynC achieves 96.9 $F_1$ vs. 92.2 $F_1$ for ELMo-transformer, indicating that mSynC is indeed encoding shallow syntax. Overall, the results further confirm that explicit shallow syntax does not offer any benefits over ELMo-transformer. 
 Question: What are improvements for these two approaches relative to ELMo-only baselines?
Output: only modest gains on three of the four downstream tasks
For the purpose of our experiments, we use data released by the APDA shared task organizers. The dataset is divided into train and test by organizers. The training set is distributed with labels for the three tasks of age, dialect, and gender. 
 Question: What are the three datasets used in the paper?
Output: Data released for APDA shared task contains 3 datasets.
Therefore, there are 44 tasks (22 INLINEFORM10 2) in total. In addition, there are 2 human summaries for each task. We selected three competitive systems (SumBasic, ILP, and ILP+MC) and therefore we have 3 system-system pairs (ILP+MC vs. ILP, ILP+MC vs. SumBasic, and ILP vs. SumBasic) for each task and each human summary. 
 Question: Do they build one model per topic or on all topics?
Output: One model per topic.
Our goal is to compare the efectiviness of the proposed approach against the baseline system described in BIBREF13 . We evaluate the false-reject (FR) and false-accept (FA) tradeoff across several end-to-end models of distinct sizes and computational complexities. As can be seen in the Receiver Operating Characteristic (ROC) curves in Figure FIGREF14 , the 2 largest end-to-end models, with 2-stage training, significantly outperform the recognition quality of the much larger and complex Baseline_1850K system. More specifically, E2E_318K_2stage and E2E_700K_2stage show up to 60% relative FR rate reduction over Baseline_1850K in most test conditions.  
 Question: How do they measure the quality of detection?
Output: We evaluate the false-reject (FR) and false-accept (FA) tradeoff across several end-to-end models of distinct sizes and computational complexities.
Table TABREF23 shows the Pearson, Spearman and Kendall correlation of Rouge and Sera, with pyramid scores. Interestingly, we observe that many variants of Rouge scores do not have high correlations with human pyramid scores. The lowest F-score correlations are for Rouge-1 and Rouge-L (with INLINEFORM0 =0.454). Weak correlation of Rouge-1 shows that matching unigrams between the candidate summary and gold summaries is not accurate in quantifying the quality of the summary. 
 Question: What different correlations result when using different variants of ROUGE scores?
Output: we observe that many variants of Rouge scores do not have high correlations with human pyramid scores
We consider three benchmark datasets: ( INLINEFORM0 ) Cora, a paper citation network with text information, built by BIBREF44 .  Hepth, a paper citation network from Arxiv on high energy physics theory, with paper abstracts as text information. ( INLINEFORM2 ) Zhihu, a Q&A network dataset constructed by BIBREF9 , which has 10,000 active users with text descriptions and their collaboration links.  
 Question: Which dataset of texts do they use?
Output: Cora Hepth Zhihu
We choose the state-of-the-art Transformer BIBREF1 model and the conventional RNN-Search model BIBREF0 as our test bed. 
 Question: Which model architectures do they test their word importance approach on?
Output:  Transformer BIBREF1 model and the conventional RNN-Search model BIBREF0
In particular, we perform an extensive set of experiments on standard benchmarks for bilingual dictionary induction and monolingual and cross-lingual word similarity, as well as on an extrinsic task: cross-lingual hypernym discovery. For both the monolingual and cross-lingual settings, we can notice that our models generally outperform the corresponding baselines.  As can be seen in Table 1 , our refinement method consistently improves over the baselines (i.e., VecMap and MUSE) on all language pairs and metrics. First and foremost, in terms of model-wise comparisons, we observe that our proposed alterations of both VecMap and MUSE improve their quality in a consistent manner, across most metrics and data configurations. 
 Question: What are the tasks that this method has shown improvements?
Output: bilingual dictionary induction, monolingual and cross-lingual word similarity, and cross-lingual hypernym discovery
Bengali, Gujarati, Marathi, Malayalam and Tamil are the primary source languages, and translation from these to Hindi constitute the child tasks.  
 Question: On how many language pairs do they show that preordering assisting language sentences helps translation quality?
Output: 5
We focus primarily on the smaller data set of 645 hand-labeled articles provided to task participants, both for training and for validation.  We pre-trained BERT-base on the 600,000 articles without labels by using the same Cloze task BIBREF5 that BERT had originally used for pre-training.  
 Question: How long is the dataset?
Output: 645, 600000
Inspired by the psycholinguistic studies BIBREF5 , BIBREF4 , BIBREF7 proposed a semi-character based RNN (ScRNN) that processes a sentence of words with misspelled characters, predicting the correct words at each step. Let $s = \lbrace w_1, w_2, \dots , w_n\rbrace $ denote the input sentence, a sequence of constituent words $w_i$ . Each input word ( $w_i$ ) is represented by concatenating (i) a one hot vector of the first character ( $\mathbf {w_{i1}}$ ); (ii) a one hot representation of the last character ( $\mathbf {w_{il}}$ , where $l$ is the length of word $w_i$ ); and (iii) a bag of characters representation of the internal characters ( $\sum _{j=2}^{l-1}\mathbf {w_{ij}})$ . ScRNN treats the first and the last characters individually, and is agnostic to the ordering of the internal characters. Each word, represented accordingly, is then fed into a BiLSTM cell. At each sequence step, the training target is the correct corresponding word (output dimension equal to vocabulary size), and the model is optimized with cross-entropy loss. 
 Question: What is a semicharacter architecture?
Output: A semi-character based RNN (ScRNN) treats the first and last characters individually, and is agnostic to the ordering of the internal characters
Besides the Baseline and Oracle, where only ASR 1-best hypothesis is considered, we also perform experiments to utilize ASR $n$-best hypotheses during evaluation. 
 Question: What are the series of simple models?
Output: perform experiments to utilize ASR $n$-best hypotheses during evaluation
Experimental Setup 
 Question: what english datasets were used?
Output: Answer with content missing: (Data section) Penn Treebank (PTB)
To identify new keywords in the selected microposts, we again leverage crowdsourcing, as humans are typically better than machines at providing specific explanations BIBREF18, BIBREF19. In the crowdsourcing task, workers are first asked to find those microposts where the model predictions are deemed correct. Then, from those microposts, workers are asked to find the keyword that best indicates the class of the microposts as predicted by the model. 
 Question: How is the keyword specific expectation elicited from the crowd?
Output: workers are first asked to find those microposts where the model predictions are deemed correct  asked to find the keyword that best indicates the class of the microposts
We collect human judgments on Amazon Mechanical Turk via ParlAI BIBREF18. 
 Question: Do they use crowdsourcing to collect human judgements?
Output: Yes
Generic Sarcasm. We first examine the different patterns learned on the Gen dataset.  We observe that the not-sarcastic patterns appear to capture technical and scientific language, while the sarcastic patterns tend to capture subjective language that is not topic-specific. We observe an abundance of adjective and adverb patterns for the sarcastic class, although we do not use adjective and adverb patterns in our regex retrieval method. Rhetorical Questions. We notice that while the not-sarcastic patterns generated for RQs are similar to the topic-specific not-sarcastic patterns we find in the general dataset, there are some interesting features of the sarcastic patterns that are more unique to the RQs.

Many of our sarcastic questions focus specifically on attacks on the mental abilities of the addressee. This generalization is made clear when we extract and analyze the verb, subject, and object arguments using the Stanford dependency parser BIBREF32 for the questions in the RQ dataset. Hyperbole. One common pattern for hyperbole involves adverbs and adjectives, as noted above. We did not use this pattern to retrieve hyperbole, but because each hyperbolic sarcastic utterance contains multiple cues, we learn an expanded class of patterns for hyperbole.  We learn a number of verbal patterns that we had not previously associated with hyperbole, as shown in Table TABREF34 . Interestingly, many of these instantiate the observations of CanoMora2009 on hyperbole and its related semantic fields: creating contrast by exclusion, e.g. no limit and no way, or by expanding a predicated class, e.g. everyone knows. Many of them are also contrastive.  
 Question: What are the linguistic differences between each class?
Output: Each class has different patterns in adjectives, adverbs and verbs for sarcastic and non-sarcastic classes
We use the VisDial v1.0 BIBREF0 dataset to train our models, where one example has an image with its caption, 9 question-answer pairs, and follow-up questions and candidate answers for each round. At round $r$, the caption and the previous question-answer pairs become conversational context. The whole dataset is split into 123,287/2,000/8,000 images for train/validation/test, respectively. Unlike the images in the train and validation sets, the images in the test set have only one follow-up question and candidate answers and their corresponding conversational context. 
 Question: How big is dataset for this challenge?
Output: 133,287 images
We evaluated our model on three benchmark datasets, namely the CNN/DailyMail news highlights dataset BIBREF24, the New York Times Annotated Corpus (NYT; BIBREF25), and XSum BIBREF22. 
 Question: What are the datasets used for evaluation?
Output: CNN/DailyMail news highlights New York Times Annotated Corpus XSum
We download 210 users' all twitter posts who are war veterans and clinically diagnosed with PTSD sufferers as well which resulted a total 12,385 tweets.  
 Question: How many twitter users are surveyed using the clinically validated survey?
Output: 210
An acoustic word embedding is a function that takes as input a speech segment corresponding to a word, INLINEFORM0 , where each INLINEFORM1 is a vector of frame-level acoustic features, and outputs a fixed-dimensional vector representing the segment, INLINEFORM2 . 
 Question: How do they represent input features of their model to train embeddings?
Output: a vector of frame-level acoustic features
In this work, the metrics detailed below are proposed and we evaluate their quality through a human evaluation in subsection SECREF32. In addition to the automatic metrics, we proceeded to a human evaluation. We chose to use the data from our SQuAD-based experiments in order to also to measure the effectiveness of the proposed approach to derive Curiosity-driven QG data from a standard, non-conversational, QA dataset. We randomly sampled 50 samples from the test set. Three professional English speakers were asked to evaluate the questions generated by: humans (i.e. the reference questions), and models trained using pre-training (PT) or (RL), and all combinations of those methods.

Before submitting the samples for human evaluation, the questions were shuffled. Ratings were collected on a 1-to-5 likert scale, to measure to what extent the generated questions were: answerable by looking at their context; grammatically correct; how much external knowledge is required to answer; relevant to their context; and, semantically sound. The results of the human evaluation are reported in Table TABREF33. 
 Question: How they evaluate quality of generated output?
Output: Through human evaluation where they are asked to evaluate the generated output on a likert scale.
As Garimella et al. BIBREF23 have made their code public , we reproduced their best method Randomwalk on our datasets and measured the AUC ROC, obtaining a score of 0.935. An interesting finding was that their method had a poor performance over their own datasets. This was due to the fact (already explained in Section SECREF4) that it was not possible to retrieve the complete discussions, moreover, in no case could we restore more than 50% of the tweets. So we decided to remove these discussions and measure again the AUC ROC of this method, obtaining a 0.99 value. Our hypothesis is that the performance of that method was seriously hurt by the incompleteness of the data. We also tested our method on these datasets, obtaining a 0.99 AUC ROC with Walktrap and 0.989 with Louvain clustering. 
 Question: What are the state of the art measures?
Output: Randomwalk Walktrap Louvain clustering
The encoder is a bidirectional Long-Short Term Memory(LSTM) cell BIBREF14 and the decoder a single LSTM cell with attention mechanism.  
 Question: How many attention layers are there in their model?
Output: one
For model distillation BIBREF6 , we extract sentences from Wikipedia in languages for which public multilingual is pretrained. For each sentence, we use the open-source BERT wordpiece tokenizer BIBREF4 , BIBREF1 and compute cross-entropy loss for each wordpiece: INLINEFORM0

where INLINEFORM0 is the cross-entropy function, INLINEFORM1 is the softmax function, INLINEFORM2 is the BERT model's logit of the current wordpiece, INLINEFORM3 is the small BERT model's logits and INLINEFORM4 is a temperature hyperparameter, explained in Section SECREF11 .

To train the distilled multilingual model mMiniBERT, we first use the distillation loss above to train the student from scratch using the teacher's logits on unlabeled data. Afterwards, we finetune the student model on the labeled data the teacher is trained on. 
 Question: How do they compress the model?
Output: we extract sentences from Wikipedia in languages for which public multilingual is pretrained. For each sentence, we use the open-source BERT wordpiece tokenizer BIBREF4 , BIBREF1 and compute cross-entropy loss for each wordpiece: INLINEFORM0
Our idea is the BQ generation for MQ and, at the same time, we only want the minimum number of BQ to represent the MQ, so modeling our problem as $LASSO$ optimization problem is an appropriate way 
 Question: What they formulate the question generation as?
Output: LASSO optimization problem
BIBREF12 found in a Chinese corpus that the word label "End" has a better performance than "Begin". This motivates us to carry out a backward greedy search over each sentence's label sequence to identify word boundaries. If two words segmented in a sentence are identified as nouns, and one word is immediately before the other, we assemble their boundaries, creating a new word candidate for entity recognition. This strategy has the advantage to find named entities with long word length. It also reduces the influence caused by different segmentation criteria. 
 Question: What boundary assembling method is used?
Output: This motivates us to carry out a backward greedy search over each sentence's label sequence to identify word boundaries. If two words segmented in a sentence are identified as nouns, and one word is immediately before the other, we assemble their boundaries, creating a new word candidate for entity recognition.
In addition to EIN, we created a model (Emotion-based Model) that uses emotional features only and compare it to two baselines. Our aim is to investigate if the emotional features independently can detect false news. The two baselines of this model are Majority Class baseline (MC) and the Random selection baseline (RAN). 
 Question: What is the baseline?
Output: Majority Class baseline (MC)  Random selection baseline (RAN)
As explained in Section SECREF15 , the corruption introduced in Doc2VecC acts as a data-dependent regularization that suppresses the embeddings of frequent but uninformative words. In contrast, Doc2VecC manages to clamp down the representation of words frequently appear in the training set, but are uninformative, such as symbols and stop words. 
 Question: How do they determine which words are informative?
Output: Informative are those that will not be suppressed by regularization performed.
We adopt the mixture of two multilingual translation corpus as our training data: MultiUN BIBREF20 and OpenSubtitles BIBREF21. MultiUN consists of 463,406 official documents in six languages, containing around 300M words for each language. OpenSubtitles is a corpus consisting of movie and TV subtitles, which contains 2.6B sentences over 60 languages. We select four shared languages of the two corpora: English, Spanish, Russian and Chinese. Statistics of the training corpus are shown in Table TABREF14. 
 Question: What multilingual parallel data is used for training proposed model?
Output: MultiUN BIBREF20 OpenSubtitles BIBREF21
Our conversational agent uses two architectures to simulate a specialized reminiscence therapist. The block in charge of generating questions is based on the work Show, Attend and Tell BIBREF13. This work generates descriptions from pictures, also known as image captioning. 
 Question: Is machine learning system underneath similar to image caption ML systems?
Output: Yes
2) Answers are formulated by domain experts with legal training.  
 Question: Are the experts comparable to real-world users?
Output: No
These features are typically obtained by training a deep neural network jointly on several languages for which labelled data is available. The bottom layers of the network are normally shared across all training languages. The network then splits into separate parts for each of the languages, or has a single shared output. The final output layer has phone labels or HMM states as targets. The final shared layer often has a lower dimensionality than the input layer, and is therefore referred to as a `bottleneck'. 
 Question: What are bottleneck features?
Output: Bulgarian, Czech, French, German, Korean, Polish, Portuguese, Russian, Thai, Vietnamese South African English These features are typically obtained by training a deep neural network jointly on several languages for which labelled data is available. The final shared layer often has a lower dimensionality than the input layer, and is therefore referred to as a `bottleneck'.
For the first task, the human raters are asked to evaluate these outputs on the first three aspects, relevance, attractiveness, and language fluency on a Likert scale from 1 to 10 (integer values). For attractiveness, annotators are asked how attractive the headlines are. 
 Question: How is attraction score measured?
Output: annotators are asked how attractive the headlines are Likert scale from 1 to 10 (integer values)
We have observed that the conflict model is very sensitive to even minor differences and compensates in such cases where attention poses high bias towards similarities already there in the sequences.

Sequence 1: What are the best ways to learn French ?

Sequence 2: How do I learn french genders ?

Attention only: 1

Attention+Conflict: 0

Ground Truth: 0

Sequence 1: How do I prevent breast cancer ?

Sequence 2: Is breast cancer preventable ?

Attention only: 1

Attention+Conflict: 0

Ground Truth: 0

We provide two examples with predictions from the models with only attention and combination of attention and conflict. Each example is accompanied by the ground truth in our data. 
 Question: Do they show on which examples how conflict works better than attention?
Output: Yes
As one of our aims is to compare coreference chain properties in automatic translation with those of the source texts and human reference, we derive data from ParCorFull, an English-German corpus annotated with full coreference chains BIBREF46. 
 Question: What languages are seen in the news and TED datasets?
Output: English German
the basic model yields good performance for recognizing explicit discourse relations as well, which is comparable with previous best result (92.05% macro F1-score and 93.09% accuracy as reported in BIBREF11 ). After untying parameters in the softmax prediction layer, implicit discourse relation classification performance was improved across all four relations, meanwhile, the explicit discourse relation classification performance was also improved. Then we also created ensemble models by applying majority voting to combine results of ten runs. From table 5 , each ensemble model obtains performance improvements compared with single model. The full model achieves performance boosting of (51.84 - 48.82 = 3.02) and (94.17 - 93.21 = 0.96) in macro F1-scores for predicting implicit and explicit discourse relations respectively.  
 Question: What discourse relations does it work best/worst for?
Output: explicit discourse relations
With a single text, we were already able to predict the electricity consumption with a relative error of less than 5% for both data sets. 
 Question: How accurate is model trained on text exclusively?
Output: Relative error is less than 5%
Since the input of our method is textual data, we follow the approach of BIBREF15 and map the text into a fixed-size vector representation. To this end, we use word embeddings that were successfully applied in other domains. We follow BIBREF5 and use pre-trained GloVe word vectors BIBREF16 to initialize the embedding layer (also known as look-up table). Section SECREF18 discusses the embedding layer in more details. 
 Question: Which pretrained word vectors did they use?
Output:  pre-trained GloVe word vectors
We performed experiments on synthetically generated dataset since it gives us a better control over the distribution of the data. Specifically we compared the gains obtained using our approach versus the variance of the distribution. We created dataset from the following generative process. [H] Generative Process [1] Generate data

Pick k points INLINEFORM0 as domain -1 means and a corresponding set of k points INLINEFORM1 as domain-2 means, and covariance matrices INLINEFORM2

iter INLINEFORM0 upto num INLINEFORM1 samples Sample class INLINEFORM2 Sample INLINEFORM3 Sample INLINEFORM4 Add q and a so sampled to the list of q,a pairs We generated the dataset from the above sampling process with means selected on a 2 dimensional grid of size INLINEFORM5 with variance set as INLINEFORM6 in each dimension.10000 sample points were generated. The parameter INLINEFORM7 of the above algorithm was set to 0.5 and k was set to 9 (since the points could be generated from one of the 9 gaussians with centroids on a INLINEFORM8 grid). 
 Question: How do they generate the synthetic dataset?
Output: using generative process
The first classifier model acts as a filter for the second stage of classification. We use both SVM and NB to compare the results and choose SVM later for stage one classification model, owing to a better F-score. The training is performed on tweets labeled with classes , and based on unigrams as features. We create word vectors of strings in the tweet using a filter available in the WEKA API BIBREF9 , and perform cross validation using standard classification techniques. 
 Question: What classifier is used for emergency detection?
Output: SVM
In addition, we propose road exam as a new metric to reveal a model's robustness against exposure bias. 
 Question: What is the road exam metric?
Output: a new metric to reveal a model's robustness against exposure bias
Each tweet is annotated as no evidence of depression (e.g., “Citizens fear an economic depression") or evidence of depression (e.g., “depressed over disappointment"). If a tweet is annotated evidence of depression, then it is further annotated with one or more depressive symptoms, for example, depressed mood (e.g., “feeling down in the dumps"), disturbed sleep (e.g., “another restless night"), or fatigue or loss of energy (e.g., “the fatigue is unbearable") BIBREF10 . 
 Question: How is the dataset annotated?
Output: no evidence of depression depressed mood disturbed sleep fatigue or loss of energy
The final result submitted in public leaderboard is 0.73019 and in private leaderboard is 0.58455. It is quite different in bad way. That maybe is the result of the model too overfit on train set tuning on public test set. 
 Question: What is public dashboard?
Output: Public dashboard where competitors can see their results during competition, on part of the test set (public test set).
The Bi-LSTM model consists of a Bi-LSTM layer followed by a linear layer to extract input features. The Bi-LSTM layer has a 300-dimensional hidden state for each direction. For the final classification, an additional linear layer is added to output predicted class distributions. The BERT model is obtained by fine-tuning the pre-trained BERT embeddings on NER data with an additional untrained CRF classifier. We fine-tuned all the parameters of BERT including that of the CRF end-to-end. 
 Question: What classifiers were used in experiments?
Output: Bi-LSTM BERT
he first, OpenIE 4 BIBREF5 , descends from two popular OIE systems OLLIE BIBREF10 and Reverb BIBREF10 . The second was MinIE BIBREF7 , which is reported as performing better than OLLIE, ClauseIE BIBREF9 and Stanford OIE BIBREF9 . 
 Question: Which OpenIE systems were used?
Output: OpenIE4 and MiniIE
The behavior model was implemented using an RNN with LSTM units and trained with the Couples Therapy Corpus. Out of the 33 behavioral codes included in the corpus we applied the behaviors Acceptance, Blame, Negativity, Positivity, and Sadness to train our models. This is motivated from previous works which showed good separability in these behaviors as well as being easy to interpret. The behavior model is pre-trained to identify the presence of each behavior from a sequence of words using a multi-label classification scheme. The pre-trained portion of the behavior model was implemented using a single layer RNN with LSTM units with dimension size 50. 
 Question: How is module that analyzes behavioral state trained?
Output: pre-trained to identify the presence of behavior from a sequence of word using the Couples Therapy Corpus
All the datasets consist of videos where only one speaker is in front of the camera. The descriptors we used for each of the modalities are as follows: Language All the datasets provide manual transcriptions. Vision Facet BIBREF26 is used to extract a set of features including per-frame basic and advanced emotions and facial action units as indicators of facial muscle movement. Acoustic We use COVAREP BIBREF27 to extract low level acoustic features including 12 Mel-frequency cepstral coefficients (MFCCs), pitch tracking and voiced/unvoiced segmenting features, glottal source parameters, peak slope parameters and maxima dispersion quotients. 
 Question: What modalities are being used in different datasets?
Output: Language Vision Acoustic