niryuu/sni-each-converted huggingface dataset:
This chapter describes the underlying formalism of the theorem provers, as it is visible during an interactive proof trace, and present the general strategy followed by the theorem provers.  The rest of this chapter is structured as follows. Section "Type-logical grammars" presents a general introduction to type-logical grammars and illustrates its basic concepts using the Lambek calculus, ending the section with some problems at the syntax-semantics interface for the Lambek calculus. Type-logical grammars are a family of grammar formalisms built on a foundation of logic and type theory. Type-logical grammars originated when BIBREF4 introduced his Syntactic Calculus (called the Lambek calculus, L, by later authors). Though Lambek built on the work of BIBREF5 , BIBREF6 and others, Lambek's main innovation was to cast the calculus as a logic, giving a sequent calculus and showing decidability by means of cut elimination. 
 Question: What formalism does Grail use?
Output: a family of grammar formalisms built on a foundation of logic and type theory. Type-logical grammars originated when BIBREF4 introduced his Syntactic Calculus (called the Lambek calculus, L, by later authors).
$D_a$ contained all of the tweets collected on the attack day of the five attacks mentioned in section 4.2. And $D_b$ contained all of the tweets collected before the five attacks. There are 1180 tweets in $D_a$ and 7979 tweets in $D_b$. The tweets on the attack days ($D_a$) are manually annotated and only 50 percent of those tweets are actually about a DDoS attack. 
 Question: Do twitter users tend to tweet about the DOS attack when it occurs? How much data supports this assumption?
Output: The dataset contains about 590 tweets about DDos attacks.
The MP framework is based on the core idea of recursive neighborhood aggregation. That is, at every iteration, the representation of each vertex is updated based on messages received from its neighbors. All spectral GNNs can be described in terms of the MP framework.

GNNs have been applied with great success to bioinformatics and social network data, for node classification, link prediction, and graph classification. However, a few studies only have focused on the application of the MP framework to representation learning on text. This paper proposes one such application. The concept of message passing over graphs has been around for many years BIBREF0, BIBREF1, as well as that of graph neural networks 
 Question: What is the message passing framework?
Output: It is a framework used to describe algorithms for neural networks represented as graphs. Main idea is that that representation of each vertex is updated based on messages from its neighbors.
The approaches we use and what we mean by `success' are thus guided by our research questions.

Domain experts and fellow researchers can provide feedback on questions and help with dynamically revising them. Sometimes we also hope to connect to multiple disciplines. Questions about potential “dual use” may also arise. 
 Question: What approaches do they use towards text analysis?
Output: Domain experts and fellow researchers can provide feedback on questions and help with dynamically revising them. connect to multiple disciplines dual use
The main set of experiments uses a Linear Support Vector Machine (Joachims, 1998) to classify dialects using CxG features. This paper has used data-driven language mapping to select national dialects of English to be included in a global dialect identification model.  
 Question: What do the models that they compare predict?
Output: national dialects of English
Overall, MMM has achieved a new SOTA, i.e., test accuracy of 88.9%, which exceeds the previous best by 16.9%. 
 Question: How big are improvements of MMM over state of the art?
Output: test accuracy of 88.9%, which exceeds the previous best by 16.9%
We evaluate the effectiveness of our LAN model on two typical knowledge graph completion tasks, i.e., link prediction and triplet classification. 
 Question: Which knowledge graph completion tasks do they experiment with?
Output: link prediction  triplet classification
In this work, we develop a technique to rapidly transfer an existing pre-trained model from English to other languages in an energy efficient way BIBREF8.  
 Question: How much training data from the non-English language is used by the system?
Output: No data. Pretrained model is used.
To demonstrate our method in a black-box setting, we focus our experiments on Google's machine translation system (GMT), accessed through its Cloud API. 
 Question: Which neural machine translation system is used?
Output: Google's machine translation system (GMT)
Text Representation.
The model, implemented as a deep neural network, learns to respond by training on hundreds of millions context-reply $(c,r)$ pairs. First, similar to Henderson:2017arxiv, raw text from both $c$ and $r$ is converted to unigrams and bigrams. Photo Representation.
Photos are represented using convolutional neural net (CNN) models pretrained on ImageNet BIBREF17. We use a MobileNet model with a depth multiplier of 1.4, and an input dimension of $224 \times 224$ pixels as in BIBREF18. 
 Question: How does PolyResponse architecture look like?
Output: Henderson:2017 MobileNet model
We modeled the relationship between word count and the two metrics of user engagement (overall rating, mean number of turns) in separate linear regressions.

Results showed that users who, on average, produced utterances with more words gave significantly higher ratings ($\beta $=0.01, SE=0.002, t=4.79, p$<$0.001)(see Figure 2) and engaged with Gunrock for significantly greater number of turns ($\beta $=1.85, SE=0.05, t=35.58, p$<$0.001) (see Figure 2). These results can be interpreted as evidence for Gunrock's ability to handle complex sentences, where users are not constrained to simple responses to be understood and feel engaged in the conversation – and evidence that individuals are more satisfied with the conversation when they take a more active role, rather than the system dominating the dialog. On the other hand, another interpretation is that users who are more talkative may enjoy talking to the bot in general, and thus give higher ratings in tandem with higher average word counts. 
 Question: How do they correlate user backstory queries to user satisfaction?
Output: modeled the relationship between word count and the two metrics of user engagement (overall rating, mean number of turns) in separate linear regressions
The Zurich Cognitive Language Processing Corpus (ZuCo) 2.0, including raw and preprocessed eye-tracking and electroencephalography (EEG) data of 18 subjects, as well as the recording and preprocessing scripts, is publicly available. It contains physiological data of each subject reading 739 English sentences from Wikipedia (see example in Figure FIGREF1). During the recording session, the participants read 739 sentences that were selected from the Wikipedia corpus provided by culotta2006integrating.  
 Question: What kind of sentences were read?
Output: sentences that were selected from the Wikipedia corpus provided by culotta2006integrating
We built a dataset for the task of hate speech detection in memes with 5,020 images that were weakly labeled into hate or non-hate memes, depending on their source. 
 Question: How is each instance of the dataset annotated?
Output: weakly labeled into hate or non-hate memes, depending on their source
BLEU: We use the Bilingual Evaluation Understudy (BLEU) BIBREF34 metric which is commonly used in machine translation tasks. The BLEU metric can be used to evaluate dialogue generation models as in BIBREF5, BIBREF35. The BLEU metric is a word-overlap metric which computes the co-occurrence of N-grams in the reference and the generated response and also applies the brevity penalty which tries to penalize far too short responses which are usually not desired in task-oriented chatbots. We compute the BLEU score using all generated responses of our systems.

Per-turn Accuracy: Per-turn accuracy measures the similarity of the system generated response versus the target response. Eric and Manning eric2017copy used this metric to evaluate their systems in which they considered their response to be correct if all tokens in the system generated response matched the corresponding token in the target response. This metric is a little bit harsh, and the results may be low since all the tokens in the generated response have to be exactly in the same position as in the target response.

Per-Dialogue Accuracy: We calculate per-dialogue accuracy as used in BIBREF8, BIBREF5. For this metric, we consider all the system generated responses and compare them to the target responses. A dialogue is considered to be true if all the turns in the system generated responses match the corresponding turns in the target responses. Note that this is a very strict metric in which all the utterances in the dialogue should be the same as the target and in the right order.

F1-Entity Score: Datasets used in task-oriented chores have a set of entities which represent user preferences. For example, in the restaurant domain chatbots common entities are meal, restaurant name, date, time and the number of people (these are usually the required entities which are crucial for making reservations, but there could be optional entities such as location or rating). Each target response has a set of entities which the system asks or informs the user about. Our models have to be able to discern these specific entities and inject them into the generated response. To evaluate our models we could use named-entity recognition evaluation metrics BIBREF36. The F1 score is the most commonly used metric used for the evaluation of named-entity recognition models which is the harmonic average of precision and recall of the model. We calculate this metric by micro-averaging over all the system generated responses. 
 Question: Is human evaluation performed?
Output: No
Among all available parameters to tune the D2V algorithm released by Gensim, six of them were selected for optimisation BIBREF14. The window_size parameter affects the size of the sliding window used to parse texts. The alpha parameter represents the learning rate of the network. The sample setting allows the model to reduce the importance given to high-frequency words. The dm parameter defines the training used architecture (PV-DM or PV-DBOW).  
 Question: What Doc2Vec architectures other than PV-DBOW have been tried?
Output: PV-DM
To make the annotated data publicly available, we selected 70 news articles from Arabic WikiNews site. These articles cover recent news from year 2013 to year 2015 in multiple genres (politics, economics, health, science and technology, sports, arts, and culture.) Articles contain 18,300 words, and they are evenly distributed among these 7 genres with 10 articles per each. 
 Question: Where did they collect their dataset from?
Output: from Arabic WikiNews site
The input for both tasks consists of news articles in free-text format, collected from 36 propagandist and 12 non-propagandist news outlets and then annotated by professional annotators.  
 Question: What dataset was used?
Output:  news articles in free-text format
In a recent work on sentiment analysis in Turkish BIBREF10, they learn embeddings using Turkish social media. They use the word2vec algorithm, create several unsupervised hand-crafted features, generate document vectors and feed them as input into the support vector machines (SVM) approach. We outperform this baseline approach using more effective word embeddings and supervised hand-crafted features. 
 Question: What baseline method is used?
Output: using word2vec to create features that are used as input to the SVM
The high nearest neighbours accuracy indicates that syntax information was successfully captured by the embeddings. 
 Question: Do they do quantitative quality analysis of learned embeddings?
Output: Yes
(Information Retrieval). This baseline has been successfully used for related tasks like Question Answering BIBREF39 . We create two versions of this baseline: one with the pool of perspectives INLINEFORM0 and one with the pool of evidences INLINEFORM1 . 
 Question: Which machine baselines are used?
Output: Information Retrieval
One of the several formats into which FHIR can be serialized is RDF. However, because RDF was designed as an abstract information model and FHIR was designed for operational use in a healthcare setting, there is the potential for a slight mismatch between the models. This comes up in two ways: One, RDF makes statements of fact, whereas FHIR makes records of events. Two, RDF is intended to have the property of monotonicity, meaning that previous facts cannot be invalidated by new facts. 
 Question: How are FHIR and RDF combined?
Output: RDF was designed as an abstract information model and FHIR was designed for operational use in a healthcare setting RDF makes statements of fact, whereas FHIR makes records of events RDF is intended to have the property of monotonicity, meaning that previous facts cannot be invalidated by new facts
We use the following cheap ways to generate pseudo-source texts:

copy: in this setting, the source side is a mere copy of the target-side data. copy-marked: another way to integrate copies without having to deal with OOVs is to augment the source vocabulary with a copy of the target vocabulary.  copy-dummies: instead of using actual copies, we replace each word with “dummy” tokens.  
 Question: what data simulation techniques were introduced?
Output: copy copy-marked copy-dummies
Our experiments on a goal-oriented dialog corpus, the personalized bAbI dialog dataset, show that leveraging personal information can significantly improve the performance of dialog systems.  
 Question: What datasets did they use?
Output: the personalized bAbI dialog dataset
These workers completed 1001 tasks: 496 tasks in the control and 505 in the AUI.  
 Question: How many responses did they obtain?
Output: 1001
To compare with our model, we select a number of strong summarization models as baseline systems. $\textsc {Lead-X}$ uses the top $X$ sentences as a summary BIBREF19. $\textsc {DRM}$ BIBREF10 leverages deep reinforcement learning for summarization. $\textsc {TConvS2S}$ BIBREF2 is based on convolutional neural networks. $\textsc {BottomUp}$ BIBREF11 uses a bottom-up approach to generate summarization. ABS BIBREF26 uses neural attention for summary generation. DRGD BIBREF27 is based on a deep recurrent generative decoder. To compare with our pretrain-only model, we include several unsupervised abstractive baselines: SEQ$^3$ BIBREF28 employs the reconstruction loss and topic loss for summarization. BottleSum BIBREF23 leverages unsupervised extractive and self-supervised abstractive methods. GPT-2 BIBREF7 is a large-scaled pretrained language model which can be directly used to generate summaries. 
 Question: What were the baselines?
Output: $\textsc {Lead-X}$ $\textsc {PTGen}$ $\textsc {DRM}$ $\textsc {TConvS2S}$  $\textsc {BottomUp}$ ABS DRGD SEQ$^3$ BottleSum GPT-2
We focus here on the Europarl domain, for which we have ample data in several languages, and use as in-domain training data the Europarl corpus BIBREF5 for two translation directions: English INLINEFORM0 German and English INLINEFORM1 French.  
 Question: what language is the data in?
Output: English  German French
We chose SVMhmm BIBREF111 implementation of Structural Support Vector Machines for sequence labeling. SVM implementation of   
 Question: Which machine learning methods are used in experiments?
Output: Structural Support Vector Machine
This results in three additional vectors corresponding to INLINEFORM3 , INLINEFORM4 and INLINEFORM5 difference vectors.

Results 
 Question: How are the EAU text spans annotated?
Output: Answer with content missing: (Data and pre-processing section) The data is suited for our experiments because the annotators were explicitly asked to provide annotations on a clausal level.
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: No
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: No
In the experiments, we compare model performance between GluonCV/NLP and other open source implementations with Caffe, Caffe2, Theano, and TensorFlow, including ResNet BIBREF8 and MobileNet BIBREF9 for image classification (ImageNet), Faster R-CNN BIBREF10 for object detection (COCO), Mask R-CNN BIBREF11 for instance segmentation, Simple Pose BIBREF12 for pose estimation (COCO), textCNN BIBREF13 for sentiment analysis (TREC), and BERT BIBREF14 for question answering (SQuAD 1.1), sentiment analysis (SST-2), natural langauge inference (MNLI-m), and paraphrasing (MRPC). 
 Question: Do they experiment with the toolkits?
Output: Yes
We used two datasets for the task - AMR Bank BIBREF10 and CNN-Dailymail ( BIBREF11 BIBREF12 ).  
 Question: What dataset is used in this paper?
Output: AMR Bank CNN-Dailymail
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
The first approach is based on hierarchical modeling BIBREF13 , which assumes that the group-specific embedding representations are tied through a global embedding. 
 Question: What hierarchical modelling approach is used?
Output: the group-specific embedding representations are tied through a global embedding
To deal with negative values, we propose clipped $\mathit {PMI}$,

which is equivalent to $\mathit {PPMI}$ when $z = 0$. In practice we find this works poorly if done symmetrically, so we introduce a variant called $\mathit {NNEGPMI}$ which only normalizes $\mathit {\texttt {-}PMI}$: 
 Question: What novel PMI variants are introduced?
Output: clipped PMI; NNEGPMI
For the evaluation of performance of the proposed method on the NLI task, SNLI BIBREF22 and MultiNLI BIBREF23 datasets are used. We use Quora Question Pairs dataset BIBREF24 in evaluating the performance of our method on the PI task. In evaluating sentiment classification performance, the Stanford Sentiment Treebank (SST) BIBREF25 is used. 
 Question: Which datasets were used?
Output: SNLI BIBREF22 and MultiNLI BIBREF23 Quora Question Pairs dataset BIBREF24  Stanford Sentiment Treebank (SST) BIBREF25
hierarchical In this paper, we work with the dataset of the 2019 GermEval shared task on hierarchical text classification BIBREF0 and use the predefined set of labels to evaluate our approach to this classification task. 
 Question: What dataset do they use?
Output: 2019 GermEval shared task on hierarchical text classification
The test-set accuracies obtained by different learning methods, including the current state-of-the-art results, are presented in Table TABREF11 . 
 Question: what models did they compare to?
Output: High-order CNN, Tree-LSTM, DRNN, DCNN, CNN-MC, NBoW and SVM
For dataset collection, we utilize the headlines collected in qin2018automatic, lin2019learning from Tencent News, one of the most popular Chinese news websites, as the positive samples. We follow the same data split as the original paper. As some of the links are not available any more, we get 170,754 training samples and 4,511 validation samples. For the negative training samples collection, we randomly select generated headlines from a pointer generator BIBREF0 model trained on LCSTS dataset BIBREF5 and create a balanced training corpus which includes 351,508 training samples and 9,022 validation samples. To evaluate our trained classifier, we construct a test set by randomly sampling 100 headlines from the test split of LCSTS dataset and the labels are obtained by 11 human annotators. We use LCSTS BIBREF5 as our dataset to train the summarization model. The dataset is collected from the Chinese microblogging website Sina Weibo. It contains over 2 million Chinese short texts with corresponding headlines given by the author of each text. The dataset is split into 2,400,591 samples for training, 10,666 samples for validation and 725 samples for testing. We tokenize each sentence with Jieba and a vocabulary size of 50000 is saved. 
 Question: Did they used dataset from another domain for evaluation?
Output: No
Following BIBREF3, we hypothesize that a sentence representation in mBERT is composed of a language-specific component, which identifies the language of the sentence, and a language-neutral component, which captures the meaning of the sentence in a language-independent way. We assume that the language-specific component is similar across all sentences in the language.

We thus try to remove the language-specific information from the representations by centering the representations of sentences in each language so that their average lies at the origin of the vector space. We then analyze the semantic properties of both the original and the centered representations using a range of probing tasks. 
 Question: How they show that mBERT representations can be split into a language-specific component and a language-neutral component?
Output: We thus try to remove the language-specific information from the representations by centering the representations of sentences in each language so that their average lies at the origin of the vector space.
We draw our data from news publications, wine reviews, and Reddit, which in addition to large volume, also let us characterize binomials in new ways, and analyze differences in binomial orderings across communities and over time.  
 Question: What online text resources are used to test binomial lists?
Output: news publications, wine reviews, and Reddit
Wav2vec BIBREF22 proposed a multi-layer convolutional neural network optimized via a noise contrastive binary classification and was applied to WSJ ASR tasks. Our experiments consisted of three different setups: 1) a fully-supervised system using all labeled data; 2) an SSL system using wav2vec features; 3) an SSL system using our proposed DeCoAR features. All models used were based on deep BLSTMs with the CTC loss criterion. 
 Question: What are baseline models on WSJ eval92 and LibriSpeech test-clean?
Output: Wav2vec BIBREF22 a fully-supervised system using all labeled data
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
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: PubMed+PMC
In our final system, after pre-training the forward and backward LMs separately, we remove the top layer softmax and concatenate the forward and backward LM embeddings to form bidirectional LM embeddings, i.e., INLINEFORM0 .  
 Question: how are the bidirectional lms obtained?
Output: They pre-train forward and backward LMs separately, remove top layer softmax, and concatenate to obtain the bidirectional LMs.
In the pyramid scoring, the content units in the gold human written summaries are organized in a pyramid. In this pyramid, the content units are organized in tiers and higher tiers of the pyramid indicate higher importance.  
 Question: What manual Pyramid scores are used?
Output:  higher tiers of the pyramid
In our methodology to design new evaluation metrics for comparing reference summaries/translations to hypothesis ones, we established first-principles criteria on what a good evaluator should do. The first one is that it should be highly correlated with human judgement of similarity. The second one is that it should be able to distinguish sentences which are in logical contradiction, logically unrelated or in logical agreement. The third one is that a robust evaluator should also be able to identify unintelligible sentences. The last criteria is that a good evaluation metric should not give high scores to semantically distant sentences and low scores to semantically related sentences. 
 Question: What is the criteria for a good metric?
Output: The first one is that it should be highly correlated with human judgement of similarity. The second one is that it should be able to distinguish sentences which are in logical contradiction, logically unrelated or in logical agreement. The third one is that a robust evaluator should also be able to identify unintelligible sentences. The last criteria is that a good evaluation metric should not give high scores to semantically distant sentences and low scores to semantically related sentences.
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: No
We construct the embeddings for each document using BERT models finetuned on MLDoc. We mean-pool each document embedding to create a single vector per document. We then calculate the cosine similarity between the embeddings for the English document and its translation. In Table TABREF21 , we observe that the median cosine similarity increases dramatically with adversarial training, which suggests that the embeddings became more language-independent. 
 Question: How do they quantify alignment between the embeddings of a document and its translation?
Output: median cosine similarity
The real-time tweets scores were calculated in the same way as the historical data and summed up for a minute and sent to the machine learning model with the Bitcoin price in the previous minute and the rolling average price. It predicted the next minute's Bitcoin price from the given data. After the actual price arrived, the RMS value was calculated and the machine learning model updated itself to predict with better understanding the next value. 
 Question: What experimental evaluation is used?
Output: root mean square error between the actual and the predicted price of Bitcoin for every minute
A group of 50 native people who were well-versed in both English and Tamil languages acted as annotators for the evaluation. A collection of samples of about 100 sentences were taken from the test set results for comparison. This set included a randomized selection of the translation results to ensure the objectivity of evaluation. Fluency and adequacy results for the RNNMorph results are tabulated. Adequacy rating was calculated on a 5-point scale of how much of the meaning is conveyed by the translation (All, Most, Much, Little, None). The fluency rating was calculated based on grammatical correctness on a 5-point scale of (Flawless, Good, Non-native, Disfluent, Incomprehensive). For the comparison process, the RNNMorph and the RNNSearch + Word2Vec models’ sentence level translations were individually ranked between each other, permitting the two translations to have ties in the ranking. 
 Question: How were the human judgements assembled?
Output: 50 human annotators ranked a random sample of 100 translations by Adequacy, Fluency and overall ranking on a 5-point scale.
To ground moral sentiment in text, we leverage the Moral Foundations Dictionary BIBREF27. The MFD is a psycholinguistic resource that associates each MFT category with a set of seed words, which are words that provide evidence for the corresponding moral category in text. To implement the first tier of our framework and detect moral relevance, we complement our morally relevant seed words with a corresponding set of seed words approximating moral irrelevance based on the notion of valence, i.e., the degree of pleasantness or unpleasantness of a stimulus. We refer to the emotional valence ratings collected by BIBREF28 for approximately 14,000 English words, and choose the words with most neutral valence rating that do not occur in the MFD as our set of morally irrelevant seed words, for an equal total number of morally relevant and morally irrelevant words. We divide historical time into decade-long bins, and use two sets of embeddings provided by BIBREF30, each trained on a different historical corpus of English:

Google N-grams BIBREF31: a corpus of $8.5 \times 10^{11}$ tokens collected from the English literature (Google Books, all-genres) spanning the period 1800–1999.

COHA BIBREF32: a smaller corpus of $4.1 \times 10^8$ tokens from works selected so as to be genre-balanced and representative of American English in the period 1810–2009. 
 Question: Which datasets are used in the paper?
Output: Google N-grams
COHA
Moral Foundations Dictionary (MFD)
To generate it, we explore two implicit user-feedback labelling strategies: five-minute reuse and one-day return.  
 Question: What feedback labels are used?
Output: five-minute reuse and one-day return
The parameters of the entire MSD (auxiliary-task) decoder are shared across languages.

Since a grouping of the languages based on language family would have left several languages in single-member groups (e.g. Russian is the sole representative of the Slavic family), we experiment with random groupings of two to three languages. Multilingual training is performed by randomly alternating between languages for every new minibatch. 
 Question: How do they perform multilingual training?
Output: Multilingual training is performed by randomly alternating between languages for every new minibatch
We used Amazon Mechanical Turk (MTurk) to collect new labels and explanations for SNLI-VE. 2,060 workers participated in the annotation effort, with an average of 1.98 assignments per worker and a standard deviation of 5.54. 
 Question: How many annotators are used to write natural language explanations to SNLI-VE-2.0?
Output: 2,060 workers
Violations The most challenging features are all related to Violations. Low performance on Infl/Agr Violations, which marks morphological violations (He washed yourself, This is happy), is especially striking because a relatively high proportion (29%) of these sentences are Simple. These models are likely to be deficient in encoding morphological features is that they are word level models, and do not have direct access sub-word information like inflectional endings, which indicates that these features are difficult to learn effectively purely from lexical distributions. 
 Question: Do the authors have a hypothesis as to why morphological agreement is hardly learned by any model?
Output: These models are likely to be deficient in encoding morphological features is that they are word level models, and do not have direct access sub-word information like inflectional endings, which indicates that these features are difficult to learn effectively purely from lexical distributions.
It has been shown that one can significantly increase the semantic information carried by a NER system when we successfully linking entities from a deep learning method to the related entities from a knowledge base BIBREF26 , BIBREF27 . Redirection: For the Wikidata linking element, we recognize that the lookup will be constrained by the most common lookup name for each entity.  
 Question: How do they combine a deep learning model with a knowledge base?
Output: Entities from a deep learning model are linked to the related entities from a knowledge base by a lookup.
Since the present study focuses on Spanish tweets, all tweets from the English datasets were translated into Spanish.  
 Question: What other languages did they translate the data from?
Output: English
Our second submission obtains 82.4 $F_1$ on the official test set, and ranks $4th$ on this shared task. 
 Question: Where did this model place in the final evaluation of the shared task?
Output: $4th$
Conditional Random Fields
Conditional Random Fields (CRF) BIBREF10 are a standard approach when dealing with sequential data in the context of sequence labeling. BiLSTM-CRF
Prior to the emergence of deep neural language models, BiLSTM-CRF models BIBREF11 had achieved state-of-the-art results for the task of sequence labeling. Multi-Task Learning
Multi-Task Learning (MTL) BIBREF15 has become popular with the progress in deep learning. BioBERT
Deep neural language models have recently evolved to a successful method for representing text. In particular, Bidirectional Encoder Representations from Transformers (BERT) outperformed previous state-of-the-art methods by a large margin on various NLP tasks BIBREF17. 
 Question: What baseline systems are proposed?
Output: Conditional Random Fields BiLSTM-CRF Multi-Task Learning BioBERT
Vanilla ST baseline: The vanilla ST BIBREF9 has only a speech encoder and a decoder. It is trained from scratch on the ST-TED corpus.

Pre-training baselines: We conduct three pre-training baseline experiments: 1) encoder pre-training, in which the ST encoder is initialized from an ASR model; 2) decoder pre-training, in which the ST decoder is initialized from an MT model; and 3) encoder-decoder pre-training, where both the encoder and decoder are pre-trained. The ASR model has the same architecture with vanilla ST model, trained on the mixture of ST-TED and TED-LIUM2 corpus. The MT model has a text encoder and decoder with the same architecture of which in TCEN. It is first trained on WMT data (out-of-domain) and then fine-tuned on in-domain data.

Multi-task baselines: We also conduct three multi-task baseline experiments including one-to-many setting, many-to-one setting, and many-to-many setting. In the first two settings, we train the model with $\alpha _{st}=0.75$ while $\alpha _{asr}=0.25$ or $\alpha _{mt}=0.25$. For many-to-many setting, we use $\alpha _{st}=0.6, \alpha _{asr}=0.2$ and $\alpha _{mt}=0.2$.. For MT task, we use only in-domain data.

Many-to-many+pre-training: We train a many-to-many multi-task model where the encoders and decoders are derived from pre-trained ASR and MT models. Triangle+pre-train: BIBREF18 DBLP:conf/naacl/AnastasopoulosC18 proposed a triangle multi-task strategy for speech translation. Their model solves the subnet waste issue by concatenating an ST decoder to an ASR encoder-decoder model. Notably, their ST decoder can consume representations from the speech encoder as well as the ASR decoder. For a fair comparison, the speech encoder and the ASR decoder are initialized from the pre-trained ASR model. The Triangle model is fine-tuned under their multi-task manner. 
 Question: What are the baselines?
Output: Vanilla ST baseline encoder pre-training, in which the ST encoder is initialized from an ASR model decoder pre-training, in which the ST decoder is initialized from an MT model encoder-decoder pre-training, where both the encoder and decoder are pre-trained many-to-many multi-task model where the encoders and decoders are derived from pre-trained ASR and MT models Triangle+pre-train: BIBREF18 DBLP:conf/naacl/AnastasopoulosC18 proposed a triangle multi-task strategy for speech translation
We collected Japanese-Vietnamese parallel data from TED talks extracted from WIT3's corpus BIBREF15 .  
 Question: did they collect their own data?
Output: No
The DeepMine database consists of three parts. The first one contains fixed common phrases to perform text-dependent speaker verification. The second part consists of random sequences of words useful for text-prompted speaker verification, and the last part includes phrases with word- and phoneme-level transcription, useful for text-independent speaker verification using a random phrase (similar to Part4 of RedDots). DeepMine Database Parts ::: Part1 - Text-dependent (TD)
This part contains a set of fixed phrases which are used to verify speakers in text-dependent mode. Each speaker utters 5 Persian phrases, and if the speaker can read English, 5 phrases selected from Part1 of the RedDots database are also recorded.

We have created three experimental setups with different numbers of speakers in the evaluation set. Similar to the text-dependent case, three experimental setups with different number of speaker in the evaluation set are defined (corresponding to the rows in Table TABREF16). However, different strategy is used for defining trials: Depending on the enrollment condition (1- to 3-sess), trials are enrolled on utterances of all words from 1 to 3 different sessions (i.e. 3 to 9 utterances). Further, we consider two conditions for test utterances: seq test utterance with only 3 or 4 words and full test utterances with all words (i.e. same words as in enrollment but in different order). Based on the recording sessions, we created two experimental setups for speaker verification. In the first one, respondents with at least 17 recording sessions are included to the evaluation set, respondents with 16 sessions to the development and the rest of respondents to the background set (can be used as training data). In the second setup, respondents with at least 8 sessions are included to the evaluation set, respondents with 6 or 7 sessions to the development and the rest of respondents to the background set.  two experimental setups for speaker verification 
 Question: what evaluation protocols are provided?
Output: three experimental setups with different numbers of speakers in the evaluation set three experimental setups with different number of speaker in the evaluation set are defined  first one, respondents with at least 17 recording sessions are included to the evaluation set, respondents with 16 sessions to the development and the rest of respondents to the background set second setup, respondents with at least 8 sessions are included to the evaluation set, respondents with 6 or 7 sessions to the development and the rest of respondents to the background set
To better demonstrate the effectiveness of the proposed model, we compare with baselines and show the results in Table TABREF12 . 
 Question: What was the score of the proposed model?
Output: Best results authors obtain is EM 51.10 and F1 63.11
In this section, we present the experimental setting for this task

Corpus: SEAME (South East Asia Mandarin-English), a conversational Mandarin-English code-switching speech corpus consists of spontaneously spoken interviews and conversations BIBREF8 .  
 Question: What languages are explored in the work?
Output: Mandarin English
We extend the Amazon Conversational Bot Toolkit (CoBot) BIBREF6 which is a flexible event-driven framework. CoBot provides ASR results and natural language processing pipelines through the Alexa Skills Kit (ASK) BIBREF7. Gunrock corrects ASR according to the context (asr) and creates a natural language understanding (NLU) (nlu) module where multiple components analyze the user utterances. A dialog manager (DM) (dm) uses features from NLU to select topic dialog modules and defines an individual dialog flow. Each dialog module leverages several knowledge bases (knowledge). Then a natural language generation (NLG) (nlg) module generates a corresponding response. While we provide an overview of the system in the following sections, for detailed system implementation details, please see the technical report BIBREF1. 
 Question: Do they specify the model they use for Gunrock?
Output: No
Comparing the natural and artificial sources of our parallel data wrt. several linguistic and distributional properties, we observe that (see Fig. FIGREF21 - FIGREF22 ):

artificial sources are on average shorter than natural ones: when using BT, cases where the source is shorter than the target are rarer; cases when they have the same length are more frequent.

automatic word alignments between artificial sources tend to be more monotonic than when using natural sources, as measured by the average Kendall INLINEFORM0 of source-target alignments BIBREF22 : for French-English the respective numbers are 0.048 (natural) and 0.018 (artificial); for German-English 0.068 and 0.053.  The intuition is that properties (i) and (ii) should help translation as compared to natural source, while property (iv) should be detrimental. 
 Question: what is their explanation for the effectiveness of back-translation?
Output: when using BT, cases where the source is shorter than the target are rarer; cases when they have the same length are more frequent automatic word alignments between artificial sources tend to be more monotonic than when using natural sources
Datasets
For training English-Hindi NMT systems, we use the IITB English-Hindi parallel corpus BIBREF22 ( INLINEFORM0 sentences from the training set) and the ILCI English-Hindi parallel corpus ( INLINEFORM1 sentences). The ILCI (Indian Language Corpora Initiative) multilingual parallel corpus BIBREF23 spans multiple Indian languages from the health and tourism domains. We use the 520-sentence dev-set of the IITB parallel corpus for validation. For each child task, we use INLINEFORM2 sentences from ILCI corpus as the test set. 
 Question: Which dataset(s) do they experiment with?
Output: IITB English-Hindi parallel corpus BIBREF22 ILCI English-Hindi parallel corpus
We distributed Dryhootch chosen surveys among 1,200 users (305 users are self claimed PTSD sufferers and rest of them are randomly chosen from previous 2,423 users) and received 210 successful responses.  Dryhootch defines the intensity of PTSD into four categories based on the weekly survey results of all three clinical survey tools (DOSPERT, BSSS and VIAS ) 
 Question: Which clinically validated survey tools are used?
Output: DOSPERT, BSSS and VIAS
In addition, we include state-of-the-art models : RegSum BIBREF0 and GCN+PADG BIBREF3. We outperform both in terms of ROUGE-1. For ROUGE-2 scores we achieve better results than GCN+PADG but without any use of domain-specific hand-crafted features and a much smaller and simpler model. Finally, RegSum achieves a similar ROUGE-2 score but computes sentence saliences based on word scores, incorporating a rich set of word-level and domain-specific features. 
 Question: How better are state-of-the-art results than this model? 
Output: we achieve better results than GCN+PADG but without any use of domain-specific hand-crafted features  RegSum achieves a similar ROUGE-2 score
Data was collected from a 10% uniform sample of Twitter posts made during 2013, specifically the Gardenhose API. Control documents were also selected. These documents did not contain any of `caused', `causing', or `causes', nor any bidirectional words, and are further matched temporally to obtain the same number of control documents as causal documents in each fifteen-minute period during 2013. Control documents were otherwise selected randomly; causal synonyms may be present. 
 Question: What is the source of the "control" corpus?
Output: Randomly selected from a Twitter dump, temporally matched to causal documents
For each candidate mention span, we retrieve the top 10 entities according to the Freebase API. We then create a lattice in which the nodes correspond to mention-entity pairs, scored by their Freebase API scores, and the edges encode the fact that no joint assignment of entities to mentions can contain overlapping spans. We take the top 10 paths through the lattice as possible entity disambiguations. For each possibility, we generate $n$ -best paraphrases that contains the entity mention spans. In the end, this process creates a total of $10n$ paraphrases.  
 Question: How many paraphrases are generated per question?
Output: 10*n paraphrases, where n depends on the number of paraphrases that contain the entity mention spans
In contrast, our models are trained only on the 76K questions in the training set. 
 Question: Do the authors also try the model on other datasets?
Output: No
We use the word embeddings for Vietnamese that created by Kyubyong Park and Edouard Grave at al:

Kyubyong Park: In his project, he uses two methods including fastText and word2vec to generate word embeddings from wikipedia database backup dumps. Edouard Grave et al BIBREF11: They use fastText tool to generate word embeddings from Wikipedia. 
 Question: What word embeddings were used?
Output: Kyubyong Park Edouard Grave et al BIBREF11
As the baseline, we simply judge the input token as IOCs on the basis of the spelling features described in BIBREF12 . 
 Question: What is used a baseline?
Output: As the baseline, we simply judge the input token as IOCs on the basis of the spelling features described in BIBREF12
At the same time, some information about inflected word forms in the context can be useful, but it is lost during lemmatization, and this leads to the decreased score. Arguably, this means that lemmatization brings along both advantages and disadvantages for WSD with ELMo.  
 Question: Do the authors mention any downside of lemmatizing input before training ELMo?
Output: Yes
Statistical analysis of tweets ::: Unigram, Bigram an Trigram word frequency analysis
Three forms of tokens of words have been considered for the study viz. unigram, bigram and trigram. 
 Question: Which word frequencies reflect on the psychology of the twitter users, according to the authors?
Output: unigram, bigram and trigram
In Table TABREF1, we summarize the quantitative results of the above previous studies. It includes the model basis, feature description, languages classified and the used dataset along with accuracy obtained. The table also lists the overall results of our proposed models (at the top). The languages used by various authors along with their acronyms are English (En), Spanish (Es), French (Fr), German (De), Russian (Ru), Italian (It), Bengali (Ben), Hindi (Hi) and Telegu (Tel). 
 Question: Is the performance compared against a baseline model?
Output: Yes
First, we propose a class of recurrent-like neural networks for NLP tasks that satisfy the differential equation DISPLAYFORM0

where DISPLAYFORM0

and where INLINEFORM0 and INLINEFORM1 are learned functions. INLINEFORM2 corresponds to traditional RNNs, with INLINEFORM3 . For INLINEFORM4 , this takes the form of RNN cells with either nested internal memories or dependencies that extend temporally beyond the immediately previous hidden state.  
 Question: What novel class of recurrent-like networks is proposed?
Output: A network, whose learned functions satisfy a certain equation. The  network contains RNN cells with either nested internal memories or dependencies that extend temporally beyond the immediately previous hidden state.