niryuu/sni-each-converted huggingface dataset:
Within ICU notes (e.g., text example in top-left box in Figure 2), we first identify all abbreviations using regular expressions and then try to find all possible expansions of these abbreviations from domain-specific knowledge base as candidates. 
 Question: Do they use any knowledge base to expand abbreviations?
Output: Yes-no
After averaging over all concepts, we lose information on the lexical variation that each concept presents but on the other hand one can now investigate which regions show similar geolectal variation, yielding well defined linguistic varieties. Those cells that have similar colors in either figure FIGREF16 or figure FIGREF17 are expected to be ascribed to the same dialect zone. Thus, we can distinguish two main regions or clusters in the maps. The purple background covers most of the map and represents rural regions with small, scattered population. Our analysis shows that this group of cells possesses more specific words in their lexicon. In contrast, the green and yellow cells form a second cluster that is largely concentrated on the center and along the coastline, which correspond to big cities and industrialized areas. In these cells, the use of standard Spanish language is widespread due probably to school education, media, travelers, etc. The character of its vocabulary is more uniform as compared with the purple group. While the purple cluster prefer particular utterances, the lexicon of the urban group includes most of the keywords.  
 Question: What are the characteristics of the rural dialect?
Output: Abstractive
For DQA four participants answered each question, therefore we took the average INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 values over the four evaluators as the result per question. The detailed answers by the participants and available online. To assess the correctness of the answers given both by participants in the DQA experiments, and by the QALD system, we use the classic information retrieval metrics of precision (P), recall (R), and F1. INLINEFORM0 measures the fraction of relevant (correct) answer (items) given versus all answers (answer items) given. 
 Question: Do they test performance of their approaches using human judgements?
Output: Yes-no
BIBREF5 adopt a machine learning approach for NER. Their NER system extracts medical problems, tests and treatments from discharge summaries and progress notes. The dataset used is the i2b2 2010 challenge dataset.  
 Question: Does the paper explore extraction from electronic health records?
Output: Yes-no
We make copies of the monolingual model for each language and add additional crosslingual latent variables (CLVs) to couple the monolingual models, capturing crosslingual semantic role patterns. Concretely, when training on parallel sentences, whenever the head words of the arguments are aligned, we add a CLV as a parent of the two corresponding role variables.  
 Question: Which additional latent variables are used in the model?
Output: Extractive
We also conduct two human evaluations in order to assess (a) which type of summary participants prefer (we compare extractive and abstractive systems) and (b) how much key information from the document is preserved in the summary (we ask participants to answer questions pertaining to the content in the document by reading system summaries). 
 Question: Do they use other evaluation metrics besides ROUGE?
Output: Yes-no
Then, it selects a subset of a 1,700-hour ( INLINEFORM2 1.1M instances) unlabeled dataset. 
 Question: How much data samples do they start with before obtaining the initial model labels?
Output: Extractive
A dialog turn from one speaker may not only be a direct response to the other speaker's query, but also likely to be a continuation of his own previous statement. Thus, when modeling turn $k$ in a dialog, we propose to connect the last RNN state of turn $k-2$ directly to the starting RNN state of turn $k$ , instead of letting it to propagate through the RNN for turn $k-1$ . 
 Question: How long of dialog history is captured?
Output: Abstractive
As pointed out in the section “sec:coalitionpolicy”, there is a strong separation in two blocks between supporters and opponents of European integration, which is even more clearly observed in Fig FIGREF42 B. In the area of State and Evolution of the Union we again observe a strong division in two blocks (see Fig FIGREF42 E). This is different to the Economic and monetary system, however, where we observe a far-left and far-right cooperation, where the division is along the traditional left-right axis.

The patterns of coalitions forming on Twitter closely resemble those in the European Parliament. 
 Question: Does the analysis find that coalitions are formed in the same way for different policy areas?
Output: Yes-no
Querying posts on Twitter with extracted lexicons led to a collection of $19,300$ tweets. In order to have lexical diversity, we added 2500 randomly sampled tweets to our dataset. 
 Question: How many tweets were collected?
Output: Extractive
Our baseline is TransE since that the score function of our models is based on TransE. 
 Question: What baselines are used for comparison?
Output: Extractive
Python package twitterscraper is used to scrap tweets from twitter. 10,478 tweets from the past two years from domains like `sports', `politics', `entertainment' were extracted. 
 Question: Where did the texts in the corpus come from?
Output: Extractive
Suppose there are $M$ total layers, and define a subset of these layers at which to apply the loss function: $K = \lbrace k_1, k_2, ..., k_L\rbrace \subseteq \lbrace 1,..,M-1\rbrace $. The total objective function is then defined as

where $Z_{k_l}$ is the $k_l$-th Transformer layer activations, $Y$ is the ground-truth transcription for CTC and context dependent states for hybrid ASR, and $Loss(P, Y)$ can be defined as CTC objective BIBREF11 or cross entropy for hybrid ASR. T 
 Question: Do they just sum up all the loses the calculate to end up with one single loss?
Output: Yes-no
In order to derive the discourse features, an entity grid is constructed by feeding the document through an NLP pipeline to identify salient entities. Two flavors of discourse features are created by populating the entity grid with either (i) grammatical relations (GR) or (ii) RST discourse relations (RST).  Denoting a feature as such with INLINEFORM4 , we construct the pooling vector INLINEFORM5 for the char-bigrams, and concatenate INLINEFORM6 to INLINEFORM7 before feeding the resulting vector to the softmax layer. 
 Question: How are discourse features incorporated into the model?
Output: Abstractive
Lastly, the attention model, MDREA, also outperforms the best existing research results (WAP 0.690 to 0.688) BIBREF19 . 
 Question: By how much does their model outperform the state of the art results?
Output: Extractive
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: Abstractive
The embeddings for words and POS tags were pre-trained on a large unannotated corpus consisting of the first 1 billion characters from Wikipedia. 
 Question: Do they use pretrained models as part of their parser?
Output: Yes-no
We call this lifelong interactive learning and inference (LiLi). Lifelong learning is reflected by the facts that the newly acquired facts are retained in the KB and used in inference for future queries, and that the accumulated knowledge in addition to the updated KB including past inference performances are leveraged to guide future interaction and learning. 
 Question: In what way does LiLi imitate how humans acquire knowledge and perform inference during an interactive conversation?
Output: Extractive
We train Word2Vec with vector size $d_\mathrm {W2V} = d_\mathrm {LM} = 768$ on PubMed+PMC (see Appendix for details). Then, we follow the procedure described in Section SECREF3 to update the wordpiece embedding layer and tokenizer of general-domain BERT. 
 Question: What in-domain text did they use?
Output: Extractive
We perform a direct comparison of expert and crowd contributors, for 1000 term groups based on the number of total annotations(200 term groups with 2 total annotations, 200 term groups with 3 total annotations, and so on up to term groups with 6 total annotations).  Evaluators were given a summary of the annotations received for the term group in the form of:The term group "inequality inequity" received annotations as 50.0% sadness, 33.33% disgust, 16.67% anger. Then, they were asked to evaluate on a scale from 1 to 5, how valid these annotations were considered. 
 Question: How do they compare lexicons?
Output: Abstractive
Our first baseline is ROUGE-L BIBREF1 , since it is the most commonly used metric for compression tasks. We compare to the best n-gram-overlap metrics from toutanova2016dataset; We further compare to the negative LM cross-entropy, i.e., the log-probability which is only normalized by sentence length. Our next baseline is perplexity, which corresponds to the exponentiated cross-entropy: Due to its popularity, we also performed initial experiments with BLEU BIBREF17 .  
 Question: Is ROUGE their only baseline?
Output: Yes-no
The BD-4SK-ASR Dataset ::: The Language Model
We created the language from the transcriptions. The model was created using CMUSphinx in which (fixed) discount mass is 0.5, and backoffs are computed using the ratio method. The model includes 283 unigrams, 5337 bigrams, and 6935 trigrams. 
 Question: What are the results of the experiment?
Output: Abstractive
But we also showed that in some cases the saliency maps seem to not capture the important input features.  The second observation we can make is that the saliency map doesn't seem to highlight the right things in the input for the summary it generates 
 Question: Is the explanation from saliency map correct?
Output: Yes-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-no
The results show that NCEL consistently outperforms various baselines with a favorable generalization ability. 
 Question: How effective is their NCEL approach overall?
Output: Extractive
Motivated by this, we introduce resolution mode variables $\Pi = \lbrace \pi _1, \ldots , \pi _n\rbrace $ , where for each mention $j$ the variable $\pi _j \in \lbrace str, prec, attr\rbrace $ indicates in which mode the mention should be resolved. In our model, we define three resolution modes — string-matching (str), precise-construct (prec), and attribute-matching (attr) — and $\Pi $ is deterministic when $D$ is given (i.e. $P(\Pi |D)$ is a point distribution). We determine $\pi _j$ for each mention $m_j$ in the following way:

$\pi _j = str$ , if there exists a mention $m_i, i < j$ such that the two mentions satisfy the String Match sieve, the Relaxed String Match sieve, or the Strict Head Match A sieve in the Stanford multi-sieve system BIBREF1 .

$\pi _j = prec$ , if there exists a mention $m_i, i < j$ such that the two mentions satisfy the Speaker Identification sieve, or the Precise Constructs sieve.

$\pi _j = attr$ , if there is no mention $m_i, i < j$ satisfies the above two conditions. 
 Question: What are resolution model variables?
Output: Abstractive
Only the information concerned with the dictionary definitions are used there, discarding the polarity scores. However, when we utilise the supervised score (+1 or -1), words of opposite polarities (e.g. “happy" and “unhappy") get far away from each other as they are translated across coordinate regions. 
 Question: How are the supervised scores of the words calculated?
Output: Extractive
Given a news Twitter account, we read its tweets from the account's timeline. Then we sort the tweets by the posting date in ascending way and we split them into $N$ chunks. Each chunk consists of a sorted sequence of tweets labeled by the label of its corresponding account. Consequently, we investigate ways to detect suspicious accounts by considering their tweets in groups (chunks). Our hypothesis is that suspicious accounts have a unique pattern in posting tweet sequences. Since their intention is to mislead, the way they transition from one set of tweets to the next has a hidden signature, biased by their intentions. Therefore, reading these tweets in chunks has the potential to improve the detection of the fake news accounts. 
 Question: How are chunks defined?
Output: Abstractive
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 textual data? 
Output: Abstractive
To demonstrate the value of building a dedicated version of BERT for French, we first compare CamemBERT to the multilingual cased version of BERT (designated as mBERT). 
 Question: Was CamemBERT compared against multilingual BERT on these tasks?
Output: Yes-no
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: Abstractive
This corpus has multiple versions, and we choose the following two versions as their test dataset has significantly larger number of instances of multiple relation tuples with overlapping entities. (i) The first version is used by BIBREF6 (BIBREF6) (mentioned as NYT in their paper) and has 24 relations. We name this version as NYT24. (ii) The second version is used by BIBREF11 (BIBREF11) (mentioned as NYT10 in their paper) and has 29 relations. 
 Question: Are there datasets with relation tuples annotated, how big are datasets available?
Output: Yes-no
The experiment settings from this paper and evaluation protocol for the Mboshi corpus (Boundary F-scores using the ZRC speech reference) are the same from BIBREF8. Table presents the results for bilingual UWS and multilingual leveraging.  
 Question: Does the paper report the accuracy of the model?
Output: Yes-no
Here a small portion of the large parallel corpus for English-German is used as a simulation for the scenario where we do not have much parallel data: Translating texts in English to German.  
 Question: Which languages do they test on for the under-resourced scenario?
Output: Extractive
For the competition, we provided 1,000 labeled dialogues from each dataset for training, and 240 unlabeled dialogues from each dataset for evaluation.  
 Question: What is the size of the second dataset?
Output: Abstractive
Results and Analysis 
 Question: what quantitative analysis is done?
Output: Abstractive
The most intuitive way to evaluate the text answer is to directly compute the Exact Match (EM) and Macro-averaged F1 scores (F1) between the predicted text answer and the ground-truth text answer. 
 Question: What evaluation metrics were used?
Output: Extractive
For instance, question and aux are correlated because main-clause questions in English require subject-aux inversion and in many cases the insertion of auxiliary do (Do lions meow?).  Expletives, or “dummy” arguments, are semantically inert arguments. The most common expletives in English are it and there, although not all occurrences of these items are expletives. 
 Question: Do they report results only on English data?
Output: Yes-no
We propose a novel problem of relationship recommendation (RSR). Different from the reciprocal recommendation problem on DSNs, our RSR task operates on regular social networks (RSN), estimating long-term and serious relationship compatibility based on social posts such as tweets. 
 Question: Is this a task other people have worked on?
Output: Yes-no
First we are comparing BiLSTMBIBREF14, BiLSTM+CNNBIBREF20, BiLSTM+CRFBIBREF1, BiLSTM+CNN+CRFBIBREF2 models with CNN modelBIBREF0 and Stanford CRF modelBIBREF21. 
 Question: Which models are used to solve NER for Nepali?
Output: Extractive
This study assumes that a robot does not have any vocabularies in advance but can recognize syllables or phonemes. 
 Question: Does their model start with any prior knowledge of words?
Output: Yes-no
We have also performed preliminary experiments on the Arabic portion of the SemEval-2016 cQA task.  
 Question: Did they experimnet in other languages?
Output: Yes-no
Both beat the non-attention baseline by a significant margin. For the attention baseline, we use the standard parametrized attention BIBREF2 . 
 Question: Which baseline methods are used?
Output: Abstractive
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-no
We create two models both of which constitutes of three main parts: encoder, interaction and classifier and take two sequences as input. The encoder is shared among the sequences simply uses two stacked GRU layers. The interaction part consists of only attention for one model while for the another one it consists of attention and conflict combined as shown in (eqn.11) . The classifier part is simply stacked fully-connected layers.  
 Question: Which neural architecture do they use as a base for their attention conflict mechanisms?
Output: Abstractive
To report the result for answer selection, MAP and MRR are used; however, the answer triggering task is evaluated by F1-score. The result for MULT Method is reported in Table. 1. 
 Question: Is accuracy the only metric they used to compare systems?
Output: Yes-no
We use two popular knowledge bases: WordNet BIBREF0 and Freebase BIBREF1 in this paper. Specifically, we use WN18 (a subset of WordNet) BIBREF24 and FB15K (a subset of Freebase) BIBREF2 since their text descriptions are easily publicly available. Table 1 lists statistics of the two datasets. 
 Question: What datasets are used to evaluate this paper?
Output: Extractive
When predicting anger, joy, or valence, the number of systems consistently giving higher scores to sentences with female noun phrases (21-25) is markedly higher than the number of systems giving higher scores to sentences with male noun phrases (8-13). (Recall that higher valence means more positive sentiment.) In contrast, on the fear task, most submissions tended to assign higher scores to sentences with male noun phrases (23) as compared to the number of systems giving higher scores to sentences with female noun phrases (12). When predicting sadness, the number of submissions that mostly assigned higher scores to sentences with female noun phrases (18) is close to the number of submissions that mostly assigned higher scores to sentences with male noun phrases (16). These results are in line with some common stereotypes, such as females are more emotional, and situations involving male agents are more fearful BIBREF27  The majority of the systems assigned higher scores to sentences with African American names on the tasks of anger, fear, and sadness intensity prediction. On the joy and valence tasks, most submissions tended to assign higher scores to sentences with European American names. These tendencies reflect some common stereotypes that associate African Americans with more negative emotions BIBREF28 . 
 Question: Which race and gender are given higher sentiment intensity predictions?
Output: Abstractive
GE-FL reduces the heavy load of instance annotation and performs well when we provide prior knowledge with no bias. In our experiments, we observe that comparable numbers of labeled features for each class have to be supplied. We randomly select $t \in [1, 20]$ features from the feature pool for one class, and only one feature for the other. Our methods are also evaluated on datasets with different unbalanced class distributions. We manually construct several movie datasets with class distributions of 1:2, 1:3, 1:4 by randomly removing 50%, 67%, 75% positive documents. Incorporating KL divergence is robust enough to control unbalance both in the dataset and in labeled features while the other three methods are not so competitive. 
 Question: How do they define robustness of a model?
Output: Abstractive
Following previous studies BIBREF1, we collect event-related microposts from Twitter using 11 and 8 seed events (see Section SECREF2) for CyberAttack and PoliticianDeath, respectively. 
 Question: Which real-world datasets are used?
Output: Abstractive
The corpus used for sentiment analysis is the IMDb dataset of movie reviews by BIBREF11 while that for NER is Groningen Meaning Bank (GMB) by BIBREF12, containing 47,959 sentence samples.  
 Question: What Named Entity Recognition dataset is used?
Output: Extractive
We use the additional INLINEFORM0 training articles labeled by publisher as an unsupervised data set to further train the BERT model. We first investigate the impact of pre-training on BERT-BASE's performance.  On the same computer, fine tuning the model on the small training set took only about 35 minutes for sequence length 100.  
 Question: How are the two different models trained?
Output: Abstractive
The ratio of correct `translations' (matches) was used as an evaluation measure. 
 Question: What evaluation metric do they use?
Output: Abstractive
Our visual model is based on fine-tuning an Inception V3 model BIBREF1 over visual renderings of documents, while our textual model is based on a hierarchical biLSTM. We further combine the two into a joint model.  We applied neural network models to capture visual features given visual renderings of documents. Experimental results show that we achieve a 2.9% higher accuracy than state-of-the-art approaches based on textual features over Wikipedia, and performance competitive with or surpassing state-of-the-art approaches over arXiv. We further proposed a joint model, combining textual and visual representations, to predict the quality of a document.  
 Question: What kind of model do they use?
Output: Extractive
Our experiments showed that using the objects color or shape to uniquely identify an object allows the robot successfully complete the binning task in 97.6% and 96.0% of the cases. However, using the shape alone as a unique identifier, the task could only be completed in 79.0% of the cases. 
 Question: What is task success rate achieved? 
Output: Abstractive
We compute our measures over the comments written by users in a community in time windows of months, for each sufficiently active month, and manually remove communities where the bulk of the contributions are in a foreign language.  
 Question: Do they report results only on English data?
Output: Yes-no
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: Extractive
Although the candidates agreed to the use of their interviews, the dataset will not be released to public outside of the scope of this study due to the videos being personal data subject to high privacy constraints. 
 Question: Have the candidates given their consent to have their videos used for the research?
Output: Yes-no
Experiment 1 directly tested the hypothesis that speakers increase their specificity in contexts with asymmetry in visual access. We found that speakers are not only context-sensitive in choosing referring expressions that distinguish target from distractors in a shared context, but are occlusion-sensitive, adaptively compensating for uncertainty. These results strongly suggest that the speaker's informativity influences listener accuracy. In support of this hypothesis, we found a strong negative correlation between informativity and error rates across items and conditions: listeners make fewer errors when utterances are a better fit for the target relative to the distractor ( $\rho = -0.81$ , bootstrapped 95% CI $= [-0.9, -0.7]$ ; Fig. 6 B). This result suggests that listener behavior is driven by an expectation of speaker informativity: listeners interpret utterances proportionally to how well they fit objects in context. Our Rational Speech Act (RSA) formalization of cooperative reasoning in this context predicts that speakers (directors) naturally increase the informativity of their referring expressions to hedge against the increased risk of misunderstanding; Exp. 1 presents direct evidence in support of this hypothesis. Exp. 2 is consistent with this hypothesis; when directors used underinformative scripted instructions (taken from prior work), listeners made significantly more errors than when speakers were allowed to provide referring expressions at their natural level of informativity, and speaker informativeness strongly modulated listener error rates. 
 Question: Did participants behave unexpectedly?
Output: Yes-no
We use three creative English datasets with distinct linguistic characteristics: (1) A corpus of 740 classical and contemporary English poems, (2) a corpus of 14950 metaphor sentences retrieved from a metaphor database website and (3) a corpus of 1500 song lyrics ranging across genres.  
 Question: Do they report results only on English data?
Output: Yes-no
The evaluation results are quite favorable for both targets and particularly higher for Target-1, considering the fact that they are the initial experiments on the data set. 
 Question: Which SVM approach resulted in the best performance?
Output: Extractive
We compare our method to the following baselines: (1) Single-task CNN: training a CNN model for each task individually; (2) Single-task FastText: training one FastText model BIBREF23 with fixed embeddings for each individual task; (3) Fine-tuned the holistic MTL-CNN: a standard transfer-learning approach, which trains one MTL-CNN model on all the training tasks offline, then fine-tunes the classifier layer (i.e. $\mathrm {M}^{(cls)}$ Figure 1 (a)) on each target task; (4) Matching Network: a metric-learning based few-shot learning model trained on all training tasks; (5) Prototypical Network: a variation of matching network with different prediction function as Eq. 9 ; (6) Convex combining all single-task models: training one CNN classifier on each meta-training task individually and taking the encoder, then for each target task training a linear combination of all the above single-task encoders with Eq. ( 24 ).  
 Question: Do they compare with the MAML algorithm?
Output: Yes-no
For both POS tagging and dependency parsing, we run experiments on the Wall Street Journal (WSJ) portion of the Penn Treebank. 
 Question: Do they evaluate only on English datasets?
Output: Yes-no
Twitter data: We used the Twitter API to scrap tweets with hashtags. All non-English tweets were filtered out by the API. 
 Question: Do they evaluate only on English datasets?
Output: Yes-no
Baselines. As none of the existing KBC methods can solve the OKBC problem, we choose various versions of LiLi as baselines.

Single: Version of LiLi where we train a single prediction model INLINEFORM0 for all test relations.

Sep: We do not transfer (past learned) weights for initializing INLINEFORM0 , i.e., we disable LL.

F-th): Here, we use a fixed prediction threshold 0.5 instead of relation-specific threshold INLINEFORM0 .

BG: The missing or connecting links (when the user does not respond) are filled with “@-RelatedTo-@" blindly, no guessing mechanism.

w/o PTS: LiLi does not ask for additional clues via past task selection for skillset improvement. 
 Question: What baseline is used in the experiments?
Output: Extractive
To tackle this issue, we present a new evaluation dataset that covers a wide range of monotonicity reasoning that was created by crowdsourcing and collected from linguistics publications (Section "Dataset" ). 
 Question: Do they release MED?
Output: Yes-no
Word embeddings have risen in popularity for NLP applications due to the success of models designed specifically for the big data setting.  Note that “big” datasets are not always available, particularly in computational social science NLP applications, where the data of interest are often not obtained from large scale sources such as the internet and social media, but from sources such as press releases BIBREF11 , academic journals BIBREF10 , books BIBREF12 , and transcripts of recorded speech BIBREF13 , BIBREF14 , BIBREF15  have proposed a model-based method for training interpretable corpus-specific word embeddings for computational social science, using mixed membership representations, Metropolis-Hastings-Walker sampling, and NCE. Experimental results for prediction, supervised learning, and case studies on state of the Union addresses and NIPS articles, indicate that high-quality embeddings and topics can be obtained using the method. The results highlight the fact that big data is not always best, as domain-specific data can be very valuable, even when it is small. 
 Question: Why is big data not appropriate for this task?
Output: Abstractive
The dataset includes about 6,000 triples, comprised of videos, questions, and answer spans manually collected from screencast tutorial videos with spoken narratives for a photo-editing software.  
 Question: What kind of instructional videos are in the dataset?
Output: Abstractive
As a baseline for the RNN models, we apply a uni-directional RNN which predicts the relation after processing the whole sentence. 
 Question: Which variant of the recurrent neural network do they use?
Output: Extractive
Conditional Random Fields (CRF) BIBREF15 have been extensively used for tasks of sequential nature. In this paper, we propose as one of the competitive baselines a CRF classifier trained with sklearn-crfsuite for Python 3.5 and the following configuration: algorithm = lbfgs; maximum iterations = 100; c1 = c2 = 0.1; all transitions = true; optimise = false. spaCy is a widely used NLP library that implements state-of-the-art text processing pipelines, including a sequence-labelling pipeline similar to the one described by strubell2017fast. spaCy offers several pre-trained models in Spanish, which perform basic NLP tasks such as Named Entity Recognition (NER). In this paper, we have trained a new NER model to detect NUBes-PHI labels. As the simplest baseline, a sensitive data recogniser and classifier has been developed that consists of regular-expressions and dictionary look-ups. For each category to detect a specific method has been implemented. For instance, the Date, Age, Time and Doctor detectors are based on regular-expressions; Hospital, Sex, Kinship, Location, Patient and Job are looked up in dictionaries. The dictionaries are hand-crafted from the training data available, except for the Patient's case, for which the possible candidates considered are the 100 most common female and male names in Spain according to the Instituto Nacional de Estadística (INE; Spanish Statistical Office). 
 Question: What are the other algorithms tested?
Output: Extractive
We aim to find such content in the social media focusing on the tweets. 
 Question: Does the dataset contain content from various social media platforms?
Output: Yes-no
To the best of our knowledge, the only scientific summarization benchmark is from TAC 2014 summarization track. For evaluating the effectiveness of Rouge variants and our metric (Sera), we use this benchmark, which consists of 20 topics each with a biomedical journal article and 4 gold human written summaries. Consider the following example:

Endogeneous small RNAs (miRNA) were genetically screened and studied to find the miRNAs which are related to tumorigenesis. 
 Question: Do the authors report results only on English data?
Output: Yes-no
Our novelties include:

Using self-play learning for the neural response ranker (described in detail below).

Optimizing neural models for specific metrics (e.g. diversity, coherence) in our ensemble setup.

Training a separate dialog model for each user, personalizing our socialbot and making it more consistent.

Using a response classification predictor and a response classifier to predict and control aspects of responses such as sentiment, topic, offensiveness, diversity etc.

Using a model predictor to predict the best responding model, before the response candidates are generated, reducing computational expenses.

Using our entropy-based filtering technique to filter all dialog datasets, obtaining higher quality training data BIBREF3.

Building big, pre-trained, hierarchical BERT and GPT dialog models BIBREF6, BIBREF7, BIBREF8.

Constantly monitoring the user input through our automatic metrics, ensuring that the user stays engaged. 
 Question: What is novel in author's approach?
Output: Abstractive
This version highly surpasses the previous state of the art while still having fewer parameters than previous work. 
 Question: Do they compare results against state-of-the-art language models?
Output: Yes-no
Methodology ::: Corpus-based Approach
Contextual information is informative in the sense that, in general, similar words tend to appear in the same contexts.  In the corpus-based approach, we capture both of these characteristics and generate word embeddings specific to a domain. Methodology ::: Dictionary-based Approach
In Turkish, there do not exist well-established sentiment lexicons as in English. In this approach, we made use of the TDK (Türk Dil Kurumu - “Turkish Language Institution”) dictionary to obtain word polarities. 
 Question: What word-based and dictionary-based feature are used?
Output: Extractive
We compare the predictions of logistic regression models based on unigram bag-of-words features (BOW), sentiment signals (SENT), the linguistic features from our earlier analyses (LING), and combinations of these features. 
 Question: What predictive model do they build?
Output: Extractive
is the standard word counting method whereby the feature vector represents the term frequency of the words in a sentence. is similar to BoW, except that it is derived by the counting of the words in the sentence weighted by individual word's term-frequency and inverse-document-frequency BIBREF31 . is derived from clustering of words-embeddings with k-means into 5000 clusters, and follow by BoW representation of the words in 5000 clusters. 
 Question: What other non-neural baselines do the authors compare to? 
Output: Abstractive
Specifically, we firstly extract multiple relations with an off-the-shelf Open Information Extraction (OpenIE) toolbox BIBREF7, then we select the relation that is most relevant to the answer with carefully designed heuristic rules. 
 Question: How they extract "structured answer-relevant relation"?
Output: Abstractive
We suspect the absence of decoding in maximum likelihood estimation as a cause behind this inconsistency, and suggest investigating sequence-level learning as an alternative in the future. Inconsistency may arise from the lack of decoding in solving this optimization problem. Maximum likelihood learning fits the model $p_{\theta }$ using the data distribution, whereas a decoded sequence from the trained model follows the distribution $q_{\mathcal {F}}$ induced by a decoding algorithm. Based on this discrepancy, we make a strong conjecture: we cannot be guaranteed to obtain a good consistent sequence generator using maximum likelihood learning and greedy decoding. 
 Question: Is infinite-length sequence generation a result of training with maximum likelihood?
Output: Abstractive
In this work, we have restricted ourselves to the same datasets as BIBREF7 . These include nine (real-valued) numerical features, which are latitude, longitude, elevation, population, and five climate related features (avg. temperature, avg. precipitation, avg. solar radiation, avg. wind speed, and avg. water vapor pressure). In addition, 180 categorical features were used, which are CORINE land cover classes at level 1 (5 classes), level 2 (15 classes) and level 3 (44 classes) and 116 soil types (SoilGrids). Note that each location should belong to exactly 4 categories: one CORINE class at each of the three levels and a soil type. 
 Question: what dataset is used in this paper?
Output: Extractive
We segment a hashtag into meaningful English phrases. In order to achieve this, we use a dictionary of English words. 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 .  
 Question: Do the authors report only on English language data?
Output: Yes-no
Concretely, we selected gold standards that fit our problem definition and were published in the years 2016 to 2019, have at least $(2019 - publication\ year) \times 20$ citations, and bucket them according to the answer selection styles as described in Section SECREF4 
 Question: What modern MRC gold standards are analyzed?
Output: Extractive
We obtained a corpus of approximately 300,000 sentences, from which roughly 1.5 million single-quiz question training examples were derived.  
 Question: What is the size of the dataset?
Output: Abstractive
In this work, we use the datasets released by BIBREF1 and HEOT dataset provided by BIBREF0 .  Both the HEOT and BIBREF1 datasets contain tweets which are annotated in three categories: offensive, abusive and none (or benign) 
 Question: Do they perform some annotation?
Output: Yes-no
The National Gang Threat Assessment Report confirms that at least tens of thousands of gang members are using social networking websites such as Twitter and video sharing websites such as YouTube in their daily life BIBREF0 . They are very active online; the 2007 National Assessment Center's survey of gang members found that 25% of individuals in gangs use the Internet for at least 4 hours a week BIBREF4 . 
 Question: Do the authors provide evidence that 'most' street gang members use Twitter to intimidate others?
Output: Yes-no
In Spanish, serigos2017using extracted anglicisms from a corpus of Argentinian newspapers by combining dictionary lookup (aided by TreeTagger and the NLTK lemmatizer) with automatic filtering of capitalized words and manual inspection. In serigos2017applying, a character n-gram module was added to estimate the probabilities of a word being English or Spanish. moreno2018configuracion used different pattern-matching filters and lexicon lookup to extract anglicism cadidates from a corpus of tweets in US Spanish. 
 Question: Does the paper mention other works proposing methods to detect anglicisms in Spanish?
Output: Yes-no
Among all single models, LFT performs the best, followed by MinAvgOut. RL is also comparable with previous state-of-the-art models VHRED (attn) and Reranking-RL. We think that this is because LFT exerts no force in pulling the model predictions away from the ground-truth tokens, but rather just makes itself aware of how dull each response is. Consequently, its responses appear more relevant than the other two approaches. Moreover, the hybrid model (last row) outperforms all other models by a large margin.  
 Question: Which one of the four proposed models performed best?
Output: Abstractive
We proposed to use visual renderings of documents to capture implicit document quality indicators, such as font choices, images, and visual layout, which are not captured in textual content. We applied neural network models to capture visual features given visual renderings of documents. Experimental results show that we achieve a 2.9% higher accuracy than state-of-the-art approaches based on textual features over Wikipedia, and performance competitive with or surpassing state-of-the-art approaches over arXiv. We further proposed a joint model, combining textual and visual representations, to predict the quality of a document. Experimental results show that our joint model outperforms the visual-only model in all cases, and the text-only model on Wikipedia and two subsets of arXiv. These results underline the feasibility of assessing document quality via visual features, and the complementarity of visual and textual document representations for quality assessment. 
 Question: Which is more useful, visual or textual features?
Output: Abstractive
Our work is further connected to the topic of interpretability and transparency of machine learning models BIBREF11, BIBREF35, BIBREF12, for which humans are increasingly involved, for instance for post-hoc evaluations of the model's interpretability.  
 Question: How are the interpretability merits of the approach demonstrated?
Output: Abstractive
Notably, this increase is observed after the conclusion of the US presidential primaries and during the period of the Democratic and Republican National Conventions and does not reduce even after the conclusion of the US presidential elections held on November 8. 
 Question: What other political events are included in the database?
Output: Extractive
On the other hand, in the case of MHD, instead of the integration at attention level, we assign multiple decoders for each head and then integrate their outputs to get a final output. 
 Question: Does each attention head in the decoder calculate the same output?
Output: Yes-no
Using this annotation model, we create a new large publicly available dataset of English tweets. 
 Question: In what language are the tweets?
Output: Extractive
Since our objective is to compare character-based and word-based approaches, we have also implemented a simple word-level encoder for tweets. The input tweet is first split into tokens along white-spaces. A more sophisticated tokenizer may be used, but for a fair comparison we wanted to keep language specific preprocessing to a minimum. The encoder is essentially the same as tweet2vec, with the input as words instead of characters. A lookup table stores word vectors for the $V$ (20K here) most common words, and the rest are grouped together under the `UNK' token. 
 Question: What is the word-level baseline?
Output: Extractive
Besides, we report the sentiment accuracy (Senti ACC) which measures whether the output sentence has the same sentiment polarity as the input sentence based on our standardized sentiment classifiers. The BLEU score BIBREF25 between the input sentences and the output sentences is calculated to evaluate the content preservation performance. In order to evaluate the overall performance of different models, we also report the geometric mean (G2) and harmonic mean (H2) of the sentiment accuracy and the BLEU score. As for the irony accuracy, we only report it in human evaluation results because it is more accurate for the human to evaluate the quality of irony as it is very complicated. 
 Question: Who judged the irony accuracy, sentiment preservation and content preservation?
Output: Abstractive
We evaluate our approach for six target languages: French (fr), Russian (ru), Arabic (ar), Chinese (zh), Hindi (hi), and Vietnamese (vi).  
 Question: What languages are the model transferred to?
Output: Extractive
(1) AutoJudge consistently and significantly outperforms all the baselines, including RC models and other neural text classification models, which shows the effectiveness and robustness of our model. (2) RC models achieve better performance than most text classification models (excluding GRU+Attention), which indicates that reading mechanism is a better way to integrate information from heterogeneous yet complementary inputs. (3) Comparing with conventional RC models, AutoJudge achieves significant improvement with the consideration of additional law articles. 
 Question: what are their results on the constructed dataset?
Output: Extractive
To control the quality, we ensured that a single annotator annotates maximum 120 headlines (this protects the annotators from reading too many news headlines and from dominating the annotations). Secondly, we let only annotators who geographically reside in the U.S. contribute to the task.

We test the annotators on a set of $1,100$ test questions for the first phase (about 10% of the data) and 500 for the second phase. Annotators were required to pass 95%. 
 Question: How is quality of annotation measured?
Output: Abstractive
Therefore, we used the crowdsourcing platform CrowdFlower (CF) for our data collection. 
 Question: How was this data collected?
Output: Extractive
It was collected using crowdsourcing BIBREF4. The data collection was done using an Android application. 
 Question: what is the source of the data?
Output: Extractive
The ensembles were formed by simply averaging the predictions from the constituent single models. 
 Question: How does their ensemble method work?
Output: Extractive
The reviews we used have been extracted from TripAdvisor and originally proposed in BIBREF10 , BIBREF11 .  
 Question: Where are the hotel reviews from?
Output: Extractive
We experiment with two image captioning models: the Show&Tell model BIBREF0 and the LRCN1u model BIBREF1. Both models follow the basic encoder-decoder architecture design that uses a CNN encoder to condense the visual information into an image embedding, which in turn conditions an LSTM decoder to generate a natural language caption. The main difference between the two models is the way they condition the decoder. The Show&Tell model feeds the image embedding as the “predecessor word embedding” to the first produced word, while the LRCN1u model concatenates the image features with the embedded previous word as the input to the sequence model at each time step. 
 Question: Which existing models are evaluated?
Output: Extractive
Our second approach incorporates shallow syntactic information in downstream tasks via token-level chunk label embeddings. Task training (and test) data is automatically chunked, and chunk boundary information is passed into the task model via BIOUL encoding of the labels. We add randomly initialized chunk label embeddings to task-specific input encoders, which are then fine-tuned for task-specific objectives. 
 Question: Which syntactic features are obtained automatically on downstream task data?
Output: Extractive
The encoder is a Convolutional Neural Network (CNN) and the decoder is a Long Short-Term Memory (LSTM) network, as presented in Figure 2 . The image is passed through the encoder generating the image representation that is used by the decoder to know the content of the image and generate the description word by word. 
 Question: What model is used to encode the images?
Output: Extractive
We compare the performance of our model (Table 2 ) with traditional Bag of Words (BoW), TF-IDF, and n-grams features based classifiers. We also compare against averaged Skip-Gram BIBREF29 , Doc2Vec BIBREF30 , CNN BIBREF23 , Hierarchical Attention (HN-ATT) BIBREF24 and hierarchical network (HN) models. HN it is similar to our model HN-SA but without any self attention. We further investigated the performance on SWBD2 by examining the confusion matrix of the model. Figure 2 shows the heatmap of the normalized confusion matrix of the model on SWBD2. 
 Question: Do the authors do manual evaluation?
Output: Yes-no