niryuu/sni-each-converted huggingface dataset:
News Articles
Our dataset source of news articles is described in BIBREF2. This dataset was built from two different sources, for the trusted news (real news) they sampled news articles from the English Gigaword corpus. For the false news, they collected articles from seven different unreliable news sites. Twitter
For this dataset, we rely on a list of several Twitter accounts for each type of false information from BIBREF6. This list was created based on public resources that annotated suspicious Twitter accounts. The authors in BIBREF6 have built a dataset by collecting tweets from these accounts and they made it available.  
 Question: What datasets did they use?
Output: News Articles Twitter
BIBREF14 introduce EMPATHETICDIALOGUES dataset, a novel dataset containing 25k conversations include emotional contexts information to facilitate training and evaluating the textual conversational system. Then, work from BIBREF2 produce a dataset containing 1.5 million Twitter conversation, gathered by using Twitter API from customer care account of 62 brands across several industries. This dataset was used to build tone-aware customer care chatbot. Finally, BIBREF29 tried to enhance SEMAINE corpus BIBREF30 by using crowdsourcing scenario to obtain a human judgement for deciding which response that elicits positive emotion.  
 Question: What are the currently available datasets for EAC?
Output: EMPATHETICDIALOGUES dataset a dataset containing 1.5 million Twitter conversation, gathered by using Twitter API from customer care account of 62 brands across several industries SEMAINE corpus BIBREF30
The full dataset comprises both the sample mentioned above and the 819 pairs from our preliminary work, totalling 2677 pairs. We split the vSTS dataset into training, validation and test partitions sampling at random and preserving the overall score distributions. In total, we use 1338 pairs for training, 669 for validation, and the rest of the 670 pairs were used for the final testing. 
 Question: How big is vSTS training data?
Output: 1338 pairs for training
Puns are a form of wordplay jokes in which one sign (e.g. a word or a phrase) suggests two or more meanings by exploiting polysemy, homonymy, or phonological similarity to another sign, for an intended humorous or rhetorical effect BIBREF3 . 
 Question: What are puns?
Output: a form of wordplay jokes in which one sign (e.g. a word or a phrase) suggests two or more meanings by exploiting polysemy, homonymy, or phonological similarity to another sign, for an intended humorous or rhetorical effect
Note that we adopt a parsimonious design principle in our modelling: both Centroid and Naïve Bayes are parameter-free models, $k$NN only depends on the choice of $k$, and KDE uses a single bandwidth parameter $h$. A Centroid model summarizes each set of seed words by its expected vector in embedding space, and classifies concepts into the class of closest expected embedding in Euclidean distance following a softmax rule; A Naïve Bayes model considers both mean and variance, under the assumption of independence among embedding dimensions, by fitting a normal distribution with mean vector and diagonal covariance matrix to the set of seed words of each class; 
 Question: How does the parameter-free model work?
Output: A Centroid model summarizes each set of seed words by its expected vector in embedding space, and classifies concepts into the class of closest expected embedding in Euclidean distance following a softmax rule; A Naïve Bayes model considers both mean and variance, under the assumption of independence among embedding dimensions, by fitting a normal distribution with mean vector and diagonal covariance matrix to the set of seed words of each class;
We conducted the human evaluation using Amazon Mechanical Turk to assess subjective quality. We recruit master level workers (who have good prior approval rates) to perform a human comparison between generated responses from two systems (which are randomly sampled from comparison systems). The workers are required to judge each utterance from 1 (bad) to 3 (good) in terms of informativeness and naturalness. Informativeness indicates the extent to which generated utterance contains all the information specified in the dialog act. Naturalness denotes whether the utterance is as natural as a human does. To reduce judgement bias, we distribute each question to three different workers. Finally, we collected in total of 5800 judges. 
 Question: What was the criteria for human evaluation?
Output: to judge each utterance from 1 (bad) to 3 (good) in terms of informativeness and naturalness
In other words, there exist a few directions in the embedding space which disproportionately explain the variance. It is clear that the visual features in the case of How2 dataset are much more dominated by the "common" dimensions, when compared to the Multi30k dataset. Further, this analysis is still at the sentence level, i.e. the visual features are much less discriminative among individual sentences, further aggravating the problem at the token level. This suggests that the existing visual features aren't sufficient enough to expect benefits from the visual modality in NMT, since they won't provide discriminativeness among the vocabulary elements at the token level during prediction. 
 Question: What is result of their Principal Component Analysis?
Output: existing visual features aren't sufficient enough to expect benefits from the visual modality in NMT
In this section, we consider two kinds of relational classification tasks: (1) relation prediction and (2) relation extraction.  We hope to design a simple and clear experiment setup to conduct error analysis for relational prediction. Therefore, we consider a typical method TransE BIBREF3 as the subject as well as FB15K BIBREF3 as the dataset. For relation extraction, we consider the supervised relation extraction setting and TACRED dataset BIBREF10 . As for the subject model, we use the best model on TACRED dataset — position-aware neural sequence model.  
 Question: Which competitive relational classification models do they test?
Output: For relation prediction they test TransE and for relation extraction they test position aware neural sequence model
Second is our first random seed selected for the classifier which produces a 0.8083 result. While better than the NBSVM solution, we pick the best validation F1 from the 20 seeds we tried. This produced our final submission of 0.8099. Our best model achieved an five-fold average F1 of 0.8254 on the validation set shown in Figure FIGREF27 but a test set F1 of 0.8099 - a drop of 0.0155 in F1 for the true out-of-sample data. 
 Question: What were their results on the classification and regression tasks
Output: F1 of 0.8099
For this reason as a baseline algorithm for English dataset we refer to results from BIBREF0, and as for Russian dataset, we used the probabilistic language model, described in BIBREF8. 
 Question: Is the RNN model evaluated against any baseline?
Output: Yes
For POS tagging, we observe error reductions of respectively 0.71% for GSD, 0.81% for Sequoia, 0.7% for Spoken and 0.28% for ParTUT. For parsing, we observe error reductions in LAS of 2.96% for GSD, 3.33% for Sequoia, 1.70% for Spoken and 1.65% for ParTUT. On the XNLI benchmark, CamemBERT obtains improved performance over multilingual language models on the TRANSLATE-TRAIN setting (81.2 vs. 80.2 for XLM) while using less than half the parameters (110M vs. 250M). However, its performance still lags behind models trained on the original English training set in the TRANSLATE-TEST setting, 81.2 vs. 82.91 for RoBERTa. Both improvements result in a 2.36 point increase in the F1 score with respect to the best SEM architecture (BiLSTM-CRF), giving CamemBERT the state of the art for NER on the FTB. 
 Question: How much better was results of CamemBERT than previous results on these tasks?
Output: 2.36 point increase in the F1 score with respect to the best SEM architecture on the TRANSLATE-TRAIN setting (81.2 vs. 80.2 for XLM) lags behind models trained on the original English training set in the TRANSLATE-TEST setting, 81.2 vs. 82.91 for RoBERTa For POS tagging, we observe error reductions of respectively 0.71% for GSD, 0.81% for Sequoia, 0.7% for Spoken and 0.28% for ParTUT For parsing, we observe error reductions in LAS of 2.96% for GSD, 3.33% for Sequoia, 1.70% for Spoken and 1.65% for ParTUT
A prototypical implementation, in which the words are assumed to be in the fundamental representation of the special orthogonal group, INLINEFORM0 , and are conditioned on losses sensitive to the relative actions of words, is the subject of another manuscript presently in preparation. 
 Question: Is there a formal proof that the RNNs form a representation of the group?
Output: No
he first dataset (WIKI) was the same set of 200 sentences from Wikipedia used in BIBREF7 .  The second dataset (SCI) was a set of 220 sentences from the scientific literature. We sourced the sentences from the OA-STM corpus. In total 2247 triples were extracted. In total, 11262 judgements were obtained after running the annotation process. 
 Question: What is the size of the released dataset?
Output: 440 sentences, 2247 triples extracted from those sentences, and 11262 judgements on those triples.
We use the Ultrax Typically Developing dataset (UXTD) from the publicly available UltraSuite repository BIBREF19 . This dataset contains synchronized acoustic and ultrasound data from 58 typically developing children, aged 5-12 years old (31 female, 27 male). The data was aligned at the phone-level, according to the methods described in BIBREF19 , BIBREF25 . The data was recorded using an Ultrasonix SonixRP machine using Articulate Assistant Advanced (AAA) software at INLINEFORM0 121fps with a 135 field of view. A single ultrasound frame consists of 412 echo returns from each of the 63 scan lines (63x412 raw frames). 
 Question: What are the characteristics of the dataset?
Output: synchronized acoustic and ultrasound data from 58 typically developing children, aged 5-12 years old (31 female, 27 male) data was aligned at the phone-level 121fps with a 135 field of view single ultrasound frame consists of 412 echo returns from each of the 63 scan lines (63x412 raw frames)
Instead, our analysis reveals that this behavior is caused by subtle artifacts arising from the translation process itself. In particular, we show that translating different parts of each instance separately (e.g. the premise and the hypothesis in NLI) can alter superficial patterns in the data (e.g. the degree of lexical overlap between them), which severely affects the generalization ability of current models. Based on the gained insights, we improve the state-of-the-art in XNLI, and show that some previous findings need to be reconsidered in the light of this phenomenon. The benchmark consists of a competence test, which evaluates the ability to understand antonymy relation and perform numerical reasoning, a distraction test, which evaluates the robustness to shallow patterns like lexical overlap and the presence of negation words, and a noise test, which evaluates robustness to spelling errors. Just as with previous experiments, we report results for the best epoch checkpoint in each test set. 
 Question: What are examples of these artificats?
Output: the degree of lexical overlap between them presence of negation words
Training was accomplished using the Kaldi Babel receipe using 198 hours of data in 10 languages (Bulgarian, Czech, French, German, Korean, Polish, Portuguese, Russian, Thai, Vietnamese) from GlobalPhone. 
 Question: What languages are considered?
Output: Bulgarian, Czech, French, German, Korean, Polish, Portuguese, Russian, Thai, Vietnamese
EmotionLines BIBREF6 is a dialogue dataset composed of two subsets, Friends and EmotionPush, according to the source of the dialogues. 
 Question: what datasets were used?
Output: Friends EmotionPush
We adopt several standard metrics widely used in existing works to measure the performance of dialogue generation models, including BLEU BIBREF24, embedding-based metrics (Average, Extrema, Greedy and Coherence) BIBREF25, BIBREF26, entropy-based metrics (Ent-{1,2}) BIBREF0 and distinct metrics (Dist-{1,2,3} and Intra-{1,2,3}) BIBREF1, BIBREF6. 
 Question: What automatic evaluation metrics are used?
Output: BLEU embedding-based metrics (Average, Extrema, Greedy and Coherence) , entropy-based metrics (Ent-{1,2}) distinct metrics (Dist-{1,2,3} and Intra-{1,2,3})
Baseline Experiments
In this section, we briefly describe a baseline and evaluation scripts that we release, with a detailed documentation, along with the corpus. 
 Question: What baseline system is proposed?
Output: Answer with content missing: (Baseline Method section) We implemented a simple approach inspired by previous work on concept map generation and keyphrase extraction.
What is the impact of pre-trained representations with less transcribed data? In order to get a better understanding of this, we train acoustic models with different amounts of labeled training data and measure accuracy with and without pre-trained representations (log-mel filterbanks). The pre-trained representations are trained on the full Librispeech corpus and we measure accuracy in terms of WER when decoding with a 4-gram language model. Figure shows that pre-training reduces WER by 32% on nov93dev when only about eight hours of transcribed data is available. Pre-training only on the audio data of WSJ ( WSJ) performs worse compared to the much larger Librispeech ( Libri). This further confirms that pre-training on more data is crucial to good performance. 
 Question: Do they explore how much traning data is needed for which magnitude of improvement for WER? 
Output: Yes
For each task, the training data that was made available by the organizers is used, which is a selection of tweets with for each tweet a label describing the intensity of the emotion or sentiment BIBREF1 .  Since the present study focuses on Spanish tweets, all tweets from the English datasets were translated into Spanish. This new set of “Spanish” data was then added to our original training set. Again, the machine translation platform Apertium BIBREF5 was used for the translation of the datasets. 
 Question: What dataset did they use?
Output:  Selection of tweets with for each tweet a label describing the intensity of the emotion or sentiment provided by organizers and  tweets translated form English to Spanish.
First, we simply calculate the average unique predictions produced by both INLINEFORM0 and INLINEFORM1 in experiments shown in Section SECREF36 . 
 Question: What two metrics are proposed?
Output: average unique predictions randomly sample 2000 decoder hidden states at INLINEFORM4 steps following a delimiter ( INLINEFORM5 ) and apply an unsupervised clustering method (t-SNE BIBREF35 )
In this paper, we describe and evaluate Nefnir BIBREF0 , a new open source lemmatizer for Icelandic. Nefnir uses suffix substitution rules derived (learned) from the Database of Modern Icelandic Inflection (DMII) BIBREF1 , which contains over 5.8 million inflectional forms. 
 Question: How are the substitution rules built?
Output: from the Database of Modern Icelandic Inflection (DMII) BIBREF1
To establish a baseline, we consider a random method that predicts the positive label with 0.15 probability (the base rate of positive instances). Overall performance (Figure FIGREF28). Although our word-level task is heavily imbalanced, all of our models outperform the random baseline by a wide margin. As expected, content words are much more difficult to predict than stopwords, but the best F1 score in content words more than doubles that of the random baseline (0.286 vs. 0.116). Notably, although we strongly improve on our random baseline, even our best F1 scores are relatively low, and this holds true regardless of the model used. Despite involving more tokens than standard tagging tasks (e.g., BIBREF41 and BIBREF42), predicting whether a word is going to be echoed in explanations remains a challenging problem. 
 Question: What are overall baseline results on new this new task?
Output: all of our models outperform the random baseline by a wide margin he best F1 score in content words more than doubles that of the random baseline (0.286 vs. 0.116)
We use hyperopt library to select the hyper-parameters on the following values: LSTM layer size (16, 32, 64), dropout ($0.0-0.9$), activation function ($relu$, $selu$, $tanh$), optimizer ($sgd$, $adam$, $rmsprop$) with varying the value of the learning rate (1e-1,..,-5), and batch size (4, 8, 16) 
 Question: What activation function do they use in their model?
Output: relu selu tanh
Given a pair of ultrasound and audio segments we can calculate the distance between them using our model. To predict the synchronisation offset for an utterance, we consider a discretised set of candidate offsets, calculate the average distance for each across utterance segments, and select the one with the minimum average distance.  
 Question: Does their neural network predict a single offset in a recording?
Output: Yes
The dataset at HASOC 2019 were given in three languages: Hindi, English, and German. Dataset in Hindi and English had three subtasks each, while German had only two subtasks. We participated in all the tasks provided by the organisers and decided to develop a single model that would be language agnostic. We used the same model architecture for all the three languages. The training dataset of Hindi dataset was more balanced than English or German dataset. Hence, the results were around 0.78. As the dataset in German language was highly imbalanced, the results drops to 0.62. 
 Question: What are the languages used to test the model?
Output: Hindi, English and German (German task won)
Our data set contains $3,209$ reviews about 553 different cars from 49 different car manufacturers. 
 Question: How big is dataset of car-speak language?
Output: $3,209$ reviews
Therefore, we implement reinforcement learning and elaborately design two rewards to describe the irony accuracy and sentiment preservation, respectively. 
 Question: What is the combination of rewards for reinforcement learning?
Output: irony accuracy sentiment preservation
In this research, we briefly discuss the steps of QA system and compare the performance of seven machine learning based classifiers (Multi-Layer Perceptron (MLP), Naive Bayes Classifier (NBC), Support Vector Machine (SVM), Gradient Boosting Classifier (GBC), Stochastic Gradient Descent (SGD), K Nearest Neighbour (K-NN) and Random Forest (RF)) in classifying Bengali questions to classes based on their anticipated answers. 
 Question: what ml based approaches were compared?
Output: Multi-Layer Perceptron (MLP), Naive Bayes Classifier (NBC), Support Vector Machine (SVM), Gradient Boosting Classifier (GBC), Stochastic Gradient Descent (SGD), K Nearest Neighbour (K-NN) and Random Forest (RF)
To achieve this, a method is proposed that transforms the goal-labels of the used dataset (DSTC2) into labels whose behaviour can be replicated during deployment. 
 Question: what corpus is used to learn behavior?
Output: DSTC2
The regressor and classifier used Support Vector Regression (SVR) and Support Vector Classification (SVC) respectively. The bottom section of Table TABREF26 shows the results of several variants of the neural architecture. The table includes a neural regressor (NNR) and a neural classifier (NNC).  
 Question: What approaches without reinforcement learning have been tried?
Output: classification, regression, neural methods
For the overlapped speech recognition problem, the conditional independence assumption in the output label streams is still made as in Equation ( 5 ). Then the cross-entropy based PIT can be transformed to sequence discriminative criterion based PIT as below,

$$\begin{split} \mathcal {J}_{\text{SEQ-PIT}}=\sum _u \min _{s^{\prime }\in \mathbf {S}} \frac{1}{N} \sum _{n\in [1,N]}-\mathcal {J}_{\text{SEQ}}(\mathbf {L}_{un}^{(s^{\prime })},\mathbf {L}_{un}^{(r)}) \end{split}$$ (Eq. 44)

Different from Equation ( 7 ), the best permutation is decided by $\mathcal {J}_{\text{SEQ}}(\mathbf {L}_{un}^{(s^{\prime })},\mathbf {L}_{un}^{(r)})$ , which is the sequence discriminative criterion of taking the $s^{\prime }$ -th permutation in $n$ -th output inference stream at utterance $u$ . Similar to CE-PIT, $\mathcal {J}_{\text{SEQ}}$ of all the permutations are calculated and the minimum permutation is taken to do the optimization. 
 Question: How is the discriminative training formulation different from the standard ones?
Output: the best permutation is decided by $\mathcal {J}_{\text{SEQ}}(\mathbf {L}_{un}^{(s^{\prime })},\mathbf {L}_{un}^{(r)})$ , which is the sequence discriminative criterion of taking the $s^{\prime }$ -th permutation in $n$ -th output inference stream at utterance $u$
We calculated inter-annotator agreement by Krippendorff's Alpha BIBREF23, which accounts for different annotator pairs and empty values. To also zoom in on the particular agreement by category, we calculated mutual F-scores for each of the categories. This metric is typically used to evaluate system performance by category on gold standard data, but could also be applied to annotation pairs by alternating the roles of the two annotators between classifier and ground truth. While both the Relevance and Subject categorizations are annotated at a percent agreement of $0.71$ and $0.70$, their agreement scores are only fair, at $\alpha =0.27$ and $\alpha =0.29$. The percent agreement on Stance and Sentiment, which carry more categories than the former two, is $0.54$ for both. Their agreement scores are also fair, at $\alpha =0.35$ and $\alpha =0.34$. The mutual F-scores show marked differences in agreement by category, where the categories that were annotated most often typically yield a higher score. This holds for the Relevant category ($0.81$), the Vaccine category ($0.79$) and the Positive category ($0.64$). The Negative category yields a mutual F-score of $0.42$, which is higher than the more frequently annotated categories Neutral ($0.23$) and Not clear ($0.31$). We found that these categories are often confused. After combining the annotations of the two, the stance agreement would be increased to $\alpha =0.43$. 
 Question: What is the agreement score of their annotated dataset?
Output:  Relevance and Subject categorizations are annotated at a percent agreement of $0.71$ and $0.70$, their agreement scores are only fair, at $\alpha =0.27$ and $\alpha =0.29$ Stance and Sentiment, which carry more categories than the former two, is $0.54$ for both. Their agreement scores are also fair, at $\alpha =0.35$ and $\alpha =0.34$ This holds for the Relevant category ($0.81$), the Vaccine category ($0.79$) and the Positive category ($0.64$)  The Negative category yields a mutual F-score of $0.42$, which is higher than the more frequently annotated categories Neutral ($0.23$) and Not clear ($0.31$).
During inference, we adopt beam search with a beam size of 5 for De-En, En-Fr and En-Vi translation tasks. 
 Question: What are three main machine translation tasks?
Output: De-En, En-Fr and En-Vi translation tasks
We attribute the significant performance gain of our model over the baseline to a combination of several factors as described below: 
 Question: Do they train a different training method except from scheduled sampling?
Output: Answer with content missing: (list missing) 
Scheduled sampling: In our experiments, we found that models trained with scheduled sampling performed better (about 0.004 BLEU-4 on validation set) than the ones trained using teacher-forcing for the AVSD dataset. Hence, we use scheduled sampling for all the results we report in this paper.

Yes.
As a result, transfer training with only 1000 hours data can match equivalent performance for full training with 7300 hours data. 
 Question: how small of a dataset did they train on?
Output: 1000 hours data
BIBREF4 contains a variety of news-related information such as images, captions, geo-location information and comments which could be used as a proxy for article popularity. The articles in this dataset were collected between January and December 2014. Although we tried to retrieve the entire dataset, we were able to download only 38,182 articles due to the dead links published in the dataset. The retrieved articles were published in main news channels, such as Yahoo News, The Guardian or The Washington Post. Similarly, to The NowThisNews dataset we normalize the data by grouping articles per publisher, and classifying them as popular, when the number of comments exceeds the median value for given publisher. 
 Question: What is the source of the news articles?
Output: main news channels, such as Yahoo News, The Guardian or The Washington Post
We focus on three main issues, including, how to incorporate affective information into chatbots, what are resources that available and can be used to build EAC, and how to evaluate EAC performance. 
 Question: What are the research questions posed in the paper regarding EAC studies?
Output: how to incorporate affective information into chatbots, what are resources that available and can be used to build EAC, and how to evaluate EAC performance
Also, we compared our models with other existing works on this dataset including OpATT BIBREF6 and Neural Content Planning with conditional copy (NCP+CC) BIBREF4. 
 Question: What is the state-of-the-art model for the task?
Output: OpATT BIBREF6 Neural Content Planning with conditional copy (NCP+CC) BIBREF4
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: No
We compare our model with other strong competitive methods on the SQuAD leaderboard and TriviaQA leaderboard. 
 Question: What other solutions do they compare to?
Output:  strong competitive methods on the SQuAD leaderboard and TriviaQA leaderboard
While each of the city descriptions is relatively short, Calvino's writing is filled with rare words, complex syntactic structures, and figurative language. Capturing the essential components of each city in a single vector is thus not as simple as it is with more standard forms of text. Nevertheless, we hope that representations from language models trained over billions of words of text can extract some meaningful semantics from these descriptions. We experiment with three different pretrained representations: ELMo BIBREF5 , BERT BIBREF6 , and GloVe BIBREF18 . To produce a single city embedding, we compute the TF-IDF weighted element-wise mean of the token-level representations. For all pretrained methods, we additionally reduce the dimensionality of the city embeddings to 40 using PCA for increased compatibility with our clustering algorithm. 
 Question: How do they model a city description using embeddings?
Output: We experiment with three different pretrained representations: ELMo BIBREF5 , BERT BIBREF6 , and GloVe BIBREF18 . To produce a single city embedding, we compute the TF-IDF weighted element-wise mean of the token-level representations. For all pretrained methods, we additionally reduce the dimensionality of the city embeddings to 40 using PCA for increased compatibility with our clustering algorithm.
Table TABREF14 presents the distribution of the tweets by country before and after the filtering process. A large portion of the samples is from India because the MeToo movement has peaked towards the end of 2018 in India. There are very few samples from Russia likely because of content moderation and regulations on social media usage in the country. 
 Question: Do the tweets come from a specific region?
Output: No
As of January 2020, the latest CoVo 2019-06-12 release includes 29 languages. CoVoST is currently built on that release and covers the following 11 languages: French, German, Dutch, Russian, Spanish, Italian, Turkish, Persian, Swedish, Mongolian and Chinese. Common Voice BIBREF10 is a crowdsourcing speech recognition corpus with an open CC0 license. Contributors record voice clips by reading from a bank of donated sentences 
 Question: How was the dataset collected?
Output: Contributors record voice clips by reading from a bank of donated sentences.
We provide three simple baselines for the RUN task: (1) NO-MOVE: the only position considered is the starting point; (2) RANDOM: As in BIBREF4, turn to a randomly selected heading, then execute a number of *WALK actions of an average route; (3) JUMP: at each sentence, extract entities from the map and move between them in the order they appear.  
 Question: What is the baseline?
Output: NO-MOVE RANDOM JUMP
This dataset is aimed to contribute to developing a partisan news detector. There are several ways that the dataset can be used to devise the system. For example, it is possible to train the detector using publisher-level labels and test with article-level labels. It is also possible to use semi-supervised learning and treat the publisher-level part as unsupervised, or use only the article-level part. We also released the raw survey data so that new mechanisms to decide the article-level labels can be devised. 
 Question: What examples of applications are mentioned?
Output: partisan news detector
The first data batch consists of tweets relevant to blizzards, hurricanes, and wildfires, under the constraint that they are tweeted by “influential" tweeters, who we define as individuals certain to have a classifiable sentiment regarding the topic at hand. For example, we assume that any tweet composed by Al Gore regarding climate change is a positive sample, whereas any tweet from conspiracy account @ClimateHiJinx is a negative sample. The assumption we make in ensuing methods (confirmed as reasonable in Section SECREF2 ) is that influential tweeters can be used to label tweets in bulk in the absence of manually-labeled tweets.  
 Question: What methodology is used to compensate for limited labelled data?
Output: Influential tweeters ( who they define as individuals certain to have a classifiable sentiment regarding the topic at hand) is used to label tweets in bulk in the absence of manually-labeled tweets.
we use a common sense definition of racist language, including all negative utterances, negative generalizations and insults concerning ethnicity, nationality, religion and culture. 
 Question: how did they ask if a tweet was racist?
Output: if it includes  negative utterances, negative generalizations and insults concerning ethnicity, nationality, religion and culture.
Across all languages, 145 human annotators were asked to score all 1,888 pairs (in their given language). We finally collect at least ten valid annotations for each word pair in each language. All annotators were required to abide by the following instructions:

1. Each annotator must assign an integer score between 0 and 6 (inclusive) indicating how semantically similar the two words in a given pair are. A score of 6 indicates very high similarity (i.e., perfect synonymy), while zero indicates no similarity.

2. Each annotator must score the entire set of 1,888 pairs in the dataset. The pairs must not be shared between different annotators.

3. Annotators are able to break the workload over a period of approximately 2-3 weeks, and are able to use external sources (e.g. dictionaries, thesauri, WordNet) if required.

4. Annotators are kept anonymous, and are not able to communicate with each other during the annotation process. 
 Question: How were the datasets annotated?
Output: 1. Each annotator must assign an integer score between 0 and 6 (inclusive) indicating how semantically similar the two words in a given pair are. A score of 6 indicates very high similarity (i.e., perfect synonymy), while zero indicates no similarity. 2. Each annotator must score the entire set of 1,888 pairs in the dataset.  able to use external sources (e.g. dictionaries, thesauri, WordNet) if required not able to communicate with each other during the annotation process
We use Waseem and Hovy BIBREF5, Davidson et al. BIBREF9, and Waseem et al. BIBREF10 as baselines and compare the results with our different fine-tuning strategies using pre-trained BERTbase model.  
 Question: What existing approaches do they compare to?
Output: Waseem and Hovy BIBREF5, Davidson et al. BIBREF9, and Waseem et al. BIBREF10
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: No
We first expand upon previous work and generic dialogue act taxonomies, developing a fine-grained set of dialogue acts for customer service, and conducting a systematic user study to identify these acts in a dataset of 800 conversations from four Twitter customer service accounts (i.e. four different companies in the telecommunication, electronics, and insurance industries). 
 Question: Which Twitter customer service industries are investigated?
Output:  four different companies in the telecommunication, electronics, and insurance industries
The dataset consists of 5000 reviews in bahasa Indonesia. 
 Question: Does the dataset contain non-English reviews?
Output: Yes
Mathur et al. in their paper for detecting offensive tweets proposed a Ternary Trans-CNN model where they train a model architecture comprising of 3 layers of Convolution 1D having filter sizes of 15, 12 and 10 and kernel size of 3 followed by 2 dense fully connected layer of size 64 and 3. The first dense FC layer has ReLU activation while the last Dense layer had Softmax activation. They were able to train this network on a parallel English dataset provided by Davidson et al. The authors were able to achieve Accuracy of 83.9%, Precision of 80.2%, Recall of 69.8%.

The approach looked promising given that the dataset was merely 3189 sentences divided into three categories and thus we replicated the experiment but failed to replicate the results. The results were poor than what the original authors achieved. But, most of the model hyper-parameter choices where inspired from this work. 
 Question: What is the previous work's model?
Output: Ternary Trans-CNN
We find that the visual attention is very sparse, in that just one source encoding is attended to (the maximum visual attention over source encodings, across the test set, has mean 0.99 and standard deviation 0.015), thereby limiting the use of modulation. In both cases, we find that the visual component of the attention hasn't learnt any variation over the source encodings, again suggesting that the visual embeddings do not lend themselves to enhancing token-level discriminativess during prediction. We find this to be consistent across sentences of different lengths. 
 Question: What is result of their attention distribution analysis?
Output: visual attention is very sparse  visual component of the attention hasn't learnt any variation over the source encodings
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.  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.  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 .  
 Question: What lexico-syntactic cues are used to retrieve sarcastic utterances?
Output: adjective and adverb patterns verb, subject, and object arguments verbal patterns
We develop a null model to determine how much variation in binomial orderings we might expect across communities and across time, if binomial orderings were randomly ordered according to global asymmetry values.  
 Question: What new model is proposed for binomial lists?
Output: null model
We propose the following challenge to the community: We must develop formal definition and evaluation for faithfulness that allows us the freedom to say when a method is sufficiently faithful to be useful in practice.

We note two possible approaches to this end:

Across models and tasks: The degree (as grayscale) of faithfulness at the level of specific models and tasks. Perhaps some models or tasks allow sufficiently faithful interpretation, even if the same is not true for others.

For example, the method may not be faithful for some question-answering task, but faithful for movie review sentiment, perhaps based on various syntactic and semantic attributes of those tasks.

Across input space: The degree of faithfulness at the level of subspaces of the input space, such as neighborhoods of similar inputs, or singular inputs themselves. If we are able to say with some degree of confidence whether a specific decision's explanation is faithful to the model, even if the interpretation method is not considered universally faithful, it can be used with respect to those specific areas or instances only. 
 Question: What approaches they propose?
Output: Across models and tasks: The degree (as grayscale) of faithfulness at the level of specific models and tasks. Across input space: The degree of faithfulness at the level of subspaces of the input space, such as neighborhoods of similar inputs, or singular inputs themselves.
We present two use cases on which Seshat was developped: clinical interviews, and daylong child-centered recordings. 
 Question: Did they experiment with the tool?
Output: Yes
Table 1 shows the official leaderboard on SQuAD test set when we submitted our system. Our model achieves a 68.73% EM score and 77.39% F1 score, which is ranked among the state of the art single models (without model ensembling). 
 Question: What is the exact performance on SQUAD?
Output: Our model achieves a 68.73% EM score and 77.39% F1 score
Given that words from texts of the same class belong to the same context, it is possible to model word vectors of each class as word subspaces and efficiently compare them in terms of similarity by using canonical angles between the word subspaces.  
 Question: What can word subspace represent?
Output: Word vectors, usually in the context of others within the same class
word embedding, input encoder, alignment, aggregation, and prediction 
 Question: what is the architecture of the baseline model?
Output: word embedding, input encoder, alignment, aggregation, and prediction.
First we are comparing BiLSTMBIBREF14, BiLSTM+CNNBIBREF20, BiLSTM+CRFBIBREF1, BiLSTM+CNN+CRFBIBREF2 models with CNN modelBIBREF0 and Stanford CRF modelBIBREF21. 
 Question: What is the baseline?
Output: CNN modelBIBREF0 Stanford CRF modelBIBREF21
To build the classifiers we used three different learning algorithms, namely Logistic Regression (LR), Random Forest (RF), and Support Vector Machines (SVM). 
 Question: Which supervised learning algorithms are used in the experiments?
Output: Logistic Regression (LR) Random Forest (RF) Support Vector Machines (SVM)
We first use predictors based on rules that have previously been proposed in the literature: word length, number of phonemes, number of syllables, alphabetical order, and frequency. We collect all binomials but make predictions only on binomials appearing at least 30 times total, stratified by subreddit. However, none of these features appear to be particularly predictive across the board (Table TABREF15). A simple linear regression model predicts close to random, which bolsters the evidence that these classical rules for frozen binomials are not predictive for general binomials. 
 Question: How was performance of previously proposed rules at very large scale?
Output:  close to random,
We evaluate the TN topic model quantitatively with standard topic model measures such as test-set perplexity, likelihood convergence and clustering measures. Qualitatively, we evaluate the model by visualizing the topic summaries, authors' topic distributions and by performing an automatic labeling task. 
 Question: What are the measures of "performance" used in this paper?
Output: test-set perplexity, likelihood convergence and clustering measures visualizing the topic summaries, authors' topic distributions and by performing an automatic labeling task
jiant provides support for cutting-edge sentence encoder models, including support for Huggingface's Transformers. Supported models include: BERT BIBREF17, RoBERTa BIBREF27, XLNet BIBREF28, XLM BIBREF29, GPT BIBREF30, GPT-2 BIBREF31, ALBERT BIBREF32 and ELMo BIBREF33. jiant also supports the from-scratch training of (bidirectional) LSTMs BIBREF34 and deep bag of words models BIBREF35, as well as syntax-aware models such as PRPN BIBREF36 and ON-LSTM BIBREF37. jiant also supports word embeddings such as GloVe BIBREF38. 
 Question: Is jiant compatible with models in any programming language?
Output: Yes
The first step is to pre-train the model on one MCQA dataset referred to as the source task, which usually contains abundant training data. The second step is to fine-tune the same model on the other MCQA dataset, which is referred to as the target task, that we actually care about, but that usually contains much less training data.  
 Question: How different is the dataset size of source and target?
Output: the training dataset is large while the target dataset is usually much smaller
We find that communities that are characterized by specialized, constantly-updating content have higher user retention rates, but also exhibit larger linguistic gaps that separate newcomers from established members. More closely examining factors that could contribute to this linguistic gap, we find that especially within distinctive communities, established users have an increased propensity to engage with the community's specialized content, compared to newcomers (Section SECREF5 ).  
 Question: What patterns do they observe about how user engagement varies with the characteristics of a community?
Output: communities that are characterized by specialized, constantly-updating content have higher user retention rates, but also exhibit larger linguistic gaps that separate newcomers from established members within distinctive communities, established users have an increased propensity to engage with the community's specialized content, compared to newcomers
Furthermore, we employ two different parsers for comparison: 1) the PCFGLA-based parser, viz. Berkeley parser BIBREF5 , and 2) a minimal span-based neural parser BIBREF6 . 
 Question: What is the baseline model for the agreement-based mode?
Output: PCFGLA-based parser, viz. Berkeley parser BIBREF5 minimal span-based neural parser BIBREF6
Table TABREF46 shows that our Open model achieves more than 3 points of f1-score than the state-of-the-art result, and RelAwe with DepPath&RelPath achieves the best in both Closed and Open settings. 
 Question: How big is improvement over the old  state-of-the-art performance on CoNLL-2009 dataset?
Output: our Open model achieves more than 3 points of f1-score than the state-of-the-art result
E2E NLG challenge Dataset: The training set of the E2E challenge dataset which consists of 42K samples was partitioned into a 10K paired and 32K unpaired datasets by a random process.  The Wikipedia Company Dataset: The Wikipedia company dataset presented in Section SECREF18 was filtered to contain only companies having abstracts of at least 7 words and at most 105 words. The dataset was then divided into: a training set (35K), a development set (4.3K) and a test set (4.3K). The training set was also partitioned in order to obtain the paired and unpaired datasets.  
 Question: What non-annotated datasets are considered?
Output: E2E NLG challenge Dataset The Wikipedia Company Dataset
As the baseline model (BASE-4L) for IWSLT14 German-English and Turkish-English, we use a 4-layer encoder, 4-layer decoder, residual-connected model, with embedding and hidden size set as 256 by default. 
 Question: what are the baselines?
Output:  4-layer encoder, 4-layer decoder, residual-connected model, with embedding and hidden size set as 256
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
To compare our neural models with the traditional approaches, we experimented with a number of existing models including: Support Vector Machine (SVM), a discriminative max-margin model; Logistic Regression (LR), a discriminative probabilistic model; and Random Forest (RF), an ensemble model of decision trees. 
 Question: what was their baseline comparison?
Output: Support Vector Machine (SVM) Logistic Regression (LR) Random Forest (RF)
CIC is a suitable complementary metric to ROUGE because it accounts for the most important information within each dialog domain. CIC can be applied to any summarization task with predefined essential entities. 
 Question: Is new evaluation metric extension of ROGUE?
Output: No
In total around 500 different workers were involved in the annotation. about 500 
 Question: How many people participated in their evaluation study of table-to-text models?
Output: about 500
We validate our approach on the Wikipedia toxic comments dataset BIBREF18 . 
 Question: Which datasets do they use?
Output:  Wikipedia toxic comments
Meanwhile, attending colleges in the Northeast, West and South regions increases the possibility of posting about sexual harassment (positive coefficients), over the Midwest region.  
 Question: Which geographical regions correlate to the trend?
Output: Northeast U.S., West U.S. and South U.S.
Baselines. We compare our approach (FacTweet) to the following set of baselines:

[leftmargin=4mm]

LR + Bag-of-words: We aggregate the tweets of a feed and we use a bag-of-words representation with a logistic regression (LR) classifier.

Tweet2vec: We use the Bidirectional Gated recurrent neural network model proposed in BIBREF20. We keep the default parameters that were provided with the implementation. To represent the tweets, we use the decoded embedding produced by the model. With this baseline we aim at assessing if the tweets' hashtags may help detecting the non-factual accounts.

LR + All Features (tweet-level): We extract all our features from each tweet and feed them into a LR classifier. Here, we do not aggregate over tweets and thus view each tweet independently.

LR + All Features (chunk-level): We concatenate the features' vectors of the tweets in a chunk and feed them into a LR classifier.

FacTweet (tweet-level): Similar to the FacTweet approach, but at tweet-level; the sequential flow of the tweets is not utilized. We aim at investigating the importance of the sequential flow of tweets.

Top-$k$ replies, likes, or re-tweets: Some approaches in rumors detection use the number of replies, likes, and re-tweets to detect rumors BIBREF21. Thus, we extract top $k$ replied, liked or re-tweeted tweets from each account to assess the accounts factuality. We tested different $k$ values between 10 tweets to the max number of tweets from each account. Figure FIGREF24 shows the macro-F1 values for different $k$ values. It seems that $k=500$ for the top replied tweets achieves the highest result. Therefore, we consider this as a baseline. 
 Question: What baselines do they compare to?
Output: LR + Bag-of-words Tweet2vec LR + All Features (tweet-level) LR + All Features (chunk-level) FacTweet (tweet-level) Top-$k$ replies, likes, or re-tweets