niryuu/sni-each-converted huggingface dataset:
Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. 
 Question: How many questions are in the dataset?
Output: 2,714
In order to benchmark toxic comment detection, The Wikipedia Toxic Comments dataset (which we study in this work) was collected and extracted from Wikipedia Talk pages and featured in a Kaggle competition BIBREF12, BIBREF15.  
 Question: What datasets are used?
Output: The Wikipedia Toxic Comments dataset
Our dataset contains tweets about `ObamaCare' in USA collected during march 2010.  
 Question: What dataset of tweets is used?
Output: tweets about `ObamaCare' in USA collected during march 2010
For our classification experiments, we use the MH17 Twitter dataset introduced by BIBREF4, a dataset collected in order to study the flow of (dis)information about the MH17 plane crash on Twitter. It contains tweets collected based on keyword search that were posted between July 17, 2014 (the day of the plane crash) and December 9, 2016.

BIBREF4 provide annotations for a subset of the English tweets contained in the dataset. 
 Question: What languages are included in the dataset?
Output: English
Let all word vectors be normalized and $W$ be the word matrix. Inspired by BIBREF21 , where vector space models are used for evaluating topic coherence, we suggest to estimate the interpretability of $k$ th component as $ \operatorname{interp}_k W = \sum _{i,j=1}^N W_{i,k} W_{j,k} \left(W_i \cdot W_j \right). $  
 Question: How do they evaluate interpretability in this paper?
Output: we suggest to estimate the interpretability of $k$ th component as $ \operatorname{interp}_k W = \sum _{i,j=1}^N W_{i,k} W_{j,k} \left(W_i \cdot W_j \right). $
The best performing systems being for character unit the RNN-transducer with additional attention module, achieving 7.8% in terms of CER and 17.6% on WER. For subword units, classic RNN-transducer, RNN-transducer with attention and joint CTC-attention show comparable performance on subword error rate and WER, with the first one being slightly better on WER ($17.4\%$) and the last one having a lower error rate on subword ($14.5\%$). 
 Question: Which model have the smallest Character Error Rate and which have the smallest Word Error Rate?
Output: character unit the RNN-transducer with additional attention module For subword units, classic RNN-transducer, RNN-transducer with attention and joint CTC-attention show comparable performance
This algorithm finds the optimal number by minimising a cost function based on the eigenvector structure of the word similarity matrix. We refer the reader to the relevant literature for further details.

Results and Discussion 
 Question: What are the six target languages?
Output: Answer with content missing: (3 Experimental Setup) We experiment with six target languages: French (FR), Brazilian Portuguese (PT), Italian (IT), Polish (PL), Croatian (HR), and Finnish (FI).
Our system was ranked second in the competition only 0.3 BLEU points behind the winning team UPC-TALP. The relative low BLEU and high TER scores obtained by all teams are due to out-of-domain data provided in the competition which made the task equally challenging to all participants. The fact that out-of-domain data was provided by the organizers resulted in a challenging but interesting scenario for all participants. 
 Question: Does the use of out-of-domain data improve the performance of the method?
Output: No
While the performances of a purely content-based model naturally stays stable, the performance of the other systems decrease notably – they perform worse than the content-based model. 
 Question: How do they demonstrate the robustness of their results?
Output: performances of a purely content-based model naturally stays stable
We proposed to use visual renderings of documents to capture implicit document quality indicators, such as font choices, images, and visual layout, which are not captured in textual content. We applied neural network models to capture visual features given visual renderings of documents. Experimental results show that we achieve a 2.9% higher accuracy than state-of-the-art approaches based on textual features over Wikipedia, and performance competitive with or surpassing state-of-the-art approaches over arXiv. We further proposed a joint model, combining textual and visual representations, to predict the quality of a document. Experimental results show that our joint model outperforms the visual-only model in all cases, and the text-only model on Wikipedia and two subsets of arXiv. These results underline the feasibility of assessing document quality via visual features, and the complementarity of visual and textual document representations for quality assessment. 
 Question: Which is more useful, visual or textual features?
Output: It depends on the dataset. Experimental results over two datasets reveal that textual and visual features are complementary.
This study focuses on Switchboard-300, a standard 300-hour English conversational speech recognition task. As a contrast to our best results on Switchboard-300, we also train a seq2seq model on the 2000-hour Switchboard+Fisher data.  
 Question: How much bigger is Switchboard-2000 than Switchboard-300 database?
Output: Switchboard-2000 contains 1700 more hours of speech data.
The corpus consists of 53 documents, which contain an average number of 156.1 sentences per document, each with 19.55 tokens on average. 
 Question: How many documents are in the new corpus?
Output: 53 documents
To adapt the FactorCell BIBREF4 for our purposes, we replace user embeddings with a low-dimensional image representation. Thus, we are able to modify each query completion to be personalized to a specific image representation. 
 Question: How they complete a user query prefix conditioned upon an image?
Output: we replace user embeddings with a low-dimensional image representation
ACL-ARC is a dataset of citation intents released by BIBREF7 . The dataset is based on a sample of papers from the ACL Anthology Reference Corpus BIBREF15 and includes 1,941 citation instances from 186 papers and is annotated by domain experts in the NLP field. 
 Question: What is the size of ACL-ARC datasets?
Output: includes 1,941 citation instances from 186 papers
The dataset is composed of 90 annotated documents with 5 citations each ranked 1 to 5, where 1 is least relevant and 5 is most relevant for a total of 450 annotated citations. 
 Question: what is the size of this built corpus?
Output: 90 annotated documents with 5 citations each ranked 1 to 5, where 1 is least relevant and 5 is most relevant for a total of 450 annotated citations
Since we do not have an end-to-end dataset, the generated English poem may not work well with Shakespeare style transfer as shown in Figure FIGREF12 for "Starry Night" with a low average content score. This happens when the style transfer dataset does not have similar words in the training set of sentences. A solution would be to expand the style transfer dataset, for a better representation of the poem data. 
 Question: What limitations do the authors demnostrate of their model?
Output: Since we do not have an end-to-end dataset, the generated English poem may not work well with Shakespeare style transfer
For the MT task, we use the WMT 2014 En $\leftrightarrow $ Fr parallel corpus. The dataset contains 36 million En $\rightarrow $ Fr sentence pairs. We swapped the source and target sentences to obtain parallel data for the Fr $\rightarrow $ En translation task. We use these two datasets (72 million sentence pairs) to train a single multilingual NMT model to learn both these translation directions simultaneously.  
 Question: What data were they used to train the multilingual encoder?
Output: WMT 2014 En-Fr parallel corpus
Our presented QA-CTS task unifies the output format of the traditional CTS task and make the training data shareable, thus enriching the training data. All question-answer pairs are annotated and reviewed by four clinicians with three types of questions, namely tumor size, proximal resection margin and distal resection margin.  Experimental results on real-world dataset demonstrate that our proposed model competes favorably with strong baseline models in all three specific tasks. 
 Question: What are the specific tasks being unified?
Output:  three types of questions, namely tumor size, proximal resection margin and distal resection margin
We experimented with four different classifiers, namely, support vector machine BIBREF18 , random forest, extra tree and naive bayes classifier BIBREF19 . Chi square feature selection algorithm is applied to reduces the size of our feature vector. For training our system classifier, we used Scikit-learn BIBREF19 . 
 Question: What type of system does the baseline classification use?
Output: support vector machine BIBREF18 , random forest, extra tree and naive bayes classifier BIBREF19
In this paper, we use three data sets from the literature to train and evaluate our own classifier. Data collected by BIBREF3 , which we term the Sexist/Racist (SR) data set, was collected using an initial Twitter search followed by analysis and filtering by the authors and their team who identified 17 common phrases, hashtags, and users that were indicative of abusive speech. BIBREF4 collected the HATE dataset by searching for tweets using a lexicon provided by Hatebase.org. The final data set we used, which we call HAR, was collected by BIBREF9 ; we removed all retweets reducing the dataset to 20,000 tweets. Tweets were labeled as “Harrassing” or “Non-Harrassing”; hate speech was not explicitly labeled, but treated as an unlabeled subset of the broader “Harrassing” category BIBREF9 . Many of the false negatives we see are specific references to characters in the TV show “My Kitchen Rules”, rather than something about women in general.  While this may be a limitation of considering only the content of the tweet, it could also be a mislabel.

Debra are now my most hated team on #mkr after least night's ep. Snakes in the grass those two.

Along these lines, we also see correct predictions of innocuous speech, but find data mislabeled as hate speech:

@LoveAndLonging ...how is that example "sexism"?

@amberhasalamb ...in what way? 
 Question: Do they report results only on English data?
Output: Yes
We compare our approaches with related approaches of pivoting, multilingual NMT (MNMT) BIBREF19, and cross-lingual transfer without pretraining BIBREF16.  The results show that our approaches consistently outperform other approaches across languages and datasets, especially surpass pivoting, which is a strong baseline in the zero-shot scenario that multilingual NMT systems often fail to beat BIBREF19, BIBREF20, BIBREF23. 
 Question: which multilingual approaches do they compare with?
Output: BIBREF19 BIBREF20
We built a phrase-based Chinese-to-English SMT system by using Moses BIBREF18 .  In the end, the final parallel text consists of around 8.8M sentence pairs, 228M Chinese tokens, and 254M English tokens (a token can be a word or punctuation symbol).  The total number of words in these two corpora is 1.81M for Chinese and 2.03M for English. 
 Question: Did they only experiment with one language pair?
Output: Yes
Given a news Twitter account, we read its tweets from the account's timeline. Then we sort the tweets by the posting date in ascending way and we split them into $N$ chunks. Each chunk consists of a sorted sequence of tweets labeled by the label of its corresponding account. Consequently, we investigate ways to detect suspicious accounts by considering their tweets in groups (chunks). Our hypothesis is that suspicious accounts have a unique pattern in posting tweet sequences. Since their intention is to mislead, the way they transition from one set of tweets to the next has a hidden signature, biased by their intentions. Therefore, reading these tweets in chunks has the potential to improve the detection of the fake news accounts. 
 Question: How are chunks defined?
Output: Chunks is group of tweets from single account that  is consecutive in time - idea is that this group can show secret intention of malicious accounts.
To answer RQ2, which is concerned with in-domain ATSC performance, we see in tab:results that for the in-domain training case, our models BERT-ADA Lapt and BERT-ADA Rest achieve performance close to state-of-the-art on the laptops dataset and new state-of-the-art on the restaurants dataset with accuracies of $79.19\%$ and $87.14\%$, respectively. In general, the ATSC task generalizes well cross-domain, with about 2-$3\%$ drop in accuracy compared to in-domain training. 
 Question: What are the performance results?
Output: results that for the in-domain training case, our models BERT-ADA Lapt and BERT-ADA Rest achieve performance close to state-of-the-art on the laptops dataset new state-of-the-art on the restaurants dataset with accuracies of $79.19\%$ and $87.14\%$, respectively.
. It is evident from Table TABREF17 that GM$\_$KL achieves better correlation than existing approaches for various metrics on SCWS dataset. 
 Question: How does this approach compare to other WSD approaches employing word embeddings?
Output: GM$\_$KL achieves better correlation than existing approaches for various metrics on SCWS dataset.
We tried Glove BIBREF2 and Twitter word2vec BIBREF3 code for training embeddings for the processed tweets. 
 Question: What embeddings do they use?
Output: Glove Twitter word2vec
To answer RQ3, which is concerned with domain adaptation, we can see in the grayed out cells in tab:results, which correspond to the cross-domain adaption case where the BERT language model is trained on the target domain, that domain adaptation works well with $2.2\%$ absolute accuracy improvement on the laptops test set and even $3.6\%$ accuracy improvement on the restaurants test set compared to BERT-base. 
 Question: By how much does their model outperform the baseline in the cross-domain evaluation?
Output: $2.2\%$ absolute accuracy improvement on the laptops test set $3.6\%$ accuracy improvement on the restaurants test set
We perform a direct comparison of expert and crowd contributors, for 1000 term groups based on the number of total annotations(200 term groups with 2 total annotations, 200 term groups with 3 total annotations, and so on up to term groups with 6 total annotations).  Evaluators were given a summary of the annotations received for the term group in the form of:The term group "inequality inequity" received annotations as 50.0% sadness, 33.33% disgust, 16.67% anger. Then, they were asked to evaluate on a scale from 1 to 5, how valid these annotations were considered. 
 Question: How do they compare lexicons?
Output: Human evaluators were asked to evaluate on a scale from 1 to 5 the validity of the lexicon annotations made by the experts and crowd contributors.
Another insight gained from these charts is that a random summarizer resulted in scores more than 50% in all measures, and without using document-aware features, the model achieves a small improvement over a random summarizer. 
 Question: Is new approach tested against state of the art?
Output: No
We evaluate our method on IWSLT16 German-English (DE-EN) translation in both directions, WMT15 English-German (EN-DE) translation in both directions, and NIST Chinese-to-English (ZH$\rightarrow $EN) translation. 
 Question: Which languages do they experiment on?
Output: German English Chinese
pretrained word embeddings (WE): For the PDT experiments, we generate the word embeddings with word2vec on a concatenation of large raw Czech corpora available from the LINDAT/CLARIN repository. For UD Czech, we use FastText word embeddings BIBREF27 of dimension 300, which we pretrain on Czech Wikipedia using segmentation and tokenization trained from the UD data.

BERT BIBREF1: Pretrained contextual word embeddings of dimension 768 from the Base model. We average the last four layers of the BERT model to produce the embeddings. Because BERT utilizes word pieces, we decompose UD words into appropriate subwords and then average the generated embeddings over subwords belonging to the same word.

Flair BIBREF2: Pretrained contextual word embeddings of dimension 4096. 
 Question: What data is used to build the embeddings?
Output: large raw Czech corpora available from the LINDAT/CLARIN repository Czech Wikipedia
We conduct experiments on our self-collected CAIS to evaluate the generalizability in different language. We apply two baseline models for comparison, one is the popular BiLSTMs + CRF architecture BIBREF36 for sequence labeling task, and the other one is the more powerful sententce-state LSTM BIBREF21. The results listed in Table TABREF50 demonstrate the generalizability and effectiveness of our CM-Net when handling various domains and different languages. 
 Question: What were the baselines models?
Output: BiLSTMs + CRF architecture BIBREF36 sententce-state LSTM BIBREF21
PERPLEXITY 
 Question: Does the paper consider the use of perplexity in order to identify text anomalies?
Output: No
Our submissions ranked second (EI-Reg), second (EI-Oc), fourth (V-Reg) and fifth (V-Oc), demonstrating that the proposed method is accurate in automatically determining the intensity of emotions and sentiment of Spanish tweets. 
 Question: What subtasks did they participate in?
Output: Answer with content missing: (Subscript 1: "We did not participate in subtask 5 (E-c)") Authors participated in EI-Reg, EI-Oc, V-Reg and V-Oc subtasks.
These sentiment scores were automatically obtained through the topic extraction and sentiment analysis pipeline introduced in our prior work BIBREF15 and pretrained on in-house psychiatric EHR text. 
 Question: How do they extract topics?
Output:  automatically obtained through the topic extraction and sentiment analysis pipeline introduced in our prior work BIBREF15
Inspired by the StackLite tag recommendation task on Kaggle, we build a new benchmark based on the public StackExchange data. We use questions with titles as source, and user-assigned tags as target keyphrases.

Since oftentimes the questions on StackExchange contain less information than in scientific publications, there are fewer keyphrases per data point in StackEx. Furthermore, StackExchange uses a tag recommendation system that suggests topic-relevant tags to users while submitting questions; therefore, we are more likely to see general terminology such as Linux and Java. This characteristic challenges models with respect to their ability to distill major topics of a question rather than selecting specific snippets from the text. 
 Question: How was the StackExchange dataset collected?
Output: they obtained computer science related topics by looking at titles and user-assigned tags
For the English version, we performed both a thorough manual analysis and automatic evaluation across three commonly used TS datasets from two different domains  For the English version, we performed both a thorough manual analysis and automatic evaluation across three commonly used TS datasets from two different domains  The evaluation of the German version is in progress. 
 Question: What are the corpora used for the task?
Output: For the English version, we performed both a thorough manual analysis and automatic evaluation across three commonly used TS datasets from two different domains The evaluation of the German version is in progress.
Each game's video ranges from 30 to 50 minutes in length which contains image and chat data linked to the specific timestamp of the game. 
 Question: What is the average length of the recordings?
Output: 40 minutes
Majority: the text picks the label of the largest size.

 ESA: A dataless classifier proposed in BIBREF0. It maps the words (in text and label names) into the title space of Wikipedia articles, then compares the text with label names. This method does not rely on train.

We implemented ESA based on 08/01/2019 Wikipedia dump. There are about 6.1M words and 5.9M articles. Word2Vec BIBREF23: Both the representations of the text and the labels are the addition of word embeddings element-wisely. Then cosine similarity determines the labels. This method does not rely on train either. Binary-BERT: We fine-tune BERT on train, which will yield a binary classifier for entailment or not; then we test it on test – picking the label with the maximal probability in single-label scenarios while choosing all the labels with “entailment” decision in multi-label cases. 
 Question: What are their baseline models?
Output: Majority ESA Word2Vec  Binary-BERT
There are two aspects that we defer to future work. First, the systems designed here assumed that the input are valid claim sentences. To make use of such systems, one needs to develop mechanisms to recognize valid argumentative structures. In addition, we ignore trustworthiness and credibility issues, important research issues that are addressed in other works. 
 Question: What challenges are highlighted?
Output: one needs to develop mechanisms to recognize valid argumentative structures we ignore trustworthiness and credibility issues
The split methods (beyond random split) are the following:

Output length: Variation of the setup described by BIBREF2 where the train set consists of examples with output (sparql query or action sequence) length $\le \hspace{-2.5pt} N$, while the test set consists of examples with output length $> \hspace{-2.5pt} N$. For CFQ, we use $N = 7$ constraints. For scan, we use $N = 22$ actions.

Input length: Variation of the above setup, in which the train set consists of examples with input (question or command) length $\le N$, while test set consists of examples with input length $> N$. For CFQ, we use $N=19$ grammar leaves. For SCAN, we use $N=8$ tokens.

Output pattern: Variation of setup described by BIBREF8, in which the split is based on randomly assigning clusters of examples sharing the same output (query or action sequence) pattern. Query patterns are determined by anonymizing entities and properties; action sequence patterns collapse primitive actions and directions.

Input pattern: Variation of the previous setup in which the split is based on randomly assigning clusters of examples sharing the same input (question or command) pattern. Question patterns are determined by anonymizing entity and property names ; command patterns collapse verbs and the interchangeable pairs left/right, around/opposite, twice/thrice. 
 Question: What are other approaches into creating compositional generalization benchmarks?
Output: random  Output length Input length Output pattern Input pattern
We use the publicly available dataset KVRET BIBREF5 in our experiments. There are 2,425 dialogues for training, 302 for validation and 302 for testing, as shown in the upper half of Table TABREF12. 
 Question: What is the size of the dataset?
Output: 3029
To design an interpretable word embedding model for small corpora, we identify novel connections between word embeddings and topic models, and adapt advances from topic modeling. Following the distributional hypothesis BIBREF23 , the skip-gram's word embeddings parameterize discrete probability distributions over words INLINEFORM0 which tend to co-occur, and tend to be semantically coherent – a property leveraged by the Gaussian LDA model of BIBREF21  We thus reinterpret the skip-gram as a parameterization of a certain supervised naive Bayes topic model (Table TABREF2 , top-right). As in LDA, this model can be improved by replacing the naive Bayes assumption with a mixed membership assumption. By applying the mixed membership representation to this topic model version of the skip-gram, we obtain the model in the bottom-right of Table TABREF2 . After once again parameterizing this model with word embeddings, we obtain our final model, the mixed membership skip-gram (MMSG)  
 Question: Which techniques for word embeddings and topic models are used?
Output:  skip-gram LDA
Using DSC loss improves the F1 score by +0.58 for MRPC and +0.73 for QQP. 
 Question: What are method improvements of F1 for paraphrase identification?
Output: Using DSC loss improves the F1 score by +0.58 for MRPC and +0.73 for QQP
Since none of the datasets from previous works have been published, we decide to build a new one. We randomly collect INLINEFORM0 cases from China Judgments Online, among which INLINEFORM1 cases are for training, INLINEFORM2 each for validation and testing. 
 Question: what datasets are used in the experiment?
Output: build a new one collect INLINEFORM0 cases from China Judgments Online
Evaluation is done using the development set, consisting of 22 documents and 1112 PIE candidates, and the test set, which consists of 23 documents and 1127 PIE candidates. For each method the best set of parameters and/or options is determined using the development set, after which the best variant by F1-score of each method is evaluated on the test set.

Since these documents in the corpus are exhaustively annotated for PIEs (see Section SECREF40), we can calculate true and false positives, and false negatives, and thus precision, recall and F1-score. 
 Question: Are PIEs extracted automatically subjected to human evaluation?
Output: Yes
The method is evaluated on the BERTbase model, which has 12 layers, 12 self-attention heads with a hidden size of 768. 
 Question: What BERT model do they test?
Output: BERTbase
As introduced above, the `One Million Post' corpus provides annotation labels for more than 11,000 user comments. Although there is no directly comparable category capturing `offensive language' as defined in the shared task, there are two closely related categories. From the resource, we extract all those comments in which a majority of the annotators agree that they contain either `inappropriate' or `discriminating' content, or none of the aforementioned. We treat the first two cases as examples of `offense' and the latter case as examples of `other'. 
 Question: What are the near-offensive language categories?
Output: inappropriate discriminating
Overall, we observe a positive correlation between retweeting and co-voting, which is significantly different from zero. The strongest positive correlations are in the areas Area of freedom, security and justice, External relations of the Union, and Internal markets. Weaker, but still positive, correlations are observed in the areas Economic, social and territorial cohesion, European citizenship, and State and evolution of the Union. The only exception, with a significantly negative coefficient, is the area Economic and monetary system. This implies that in the area Economic and monetary system we observe a significant deviation from the usual co-voting patterns. 
 Question: What is the relationship between the co-voting and retweeting patterns?
Output: we observe a positive correlation between retweeting and co-voting strongest positive correlations are in the areas Area of freedom, security and justice, External relations of the Union, and Internal markets Weaker, but still positive, correlations are observed in the areas Economic, social and territorial cohesion, European citizenship, and State and evolution of the Union significantly negative coefficient, is the area Economic and monetary system
Experiments ::: Main Results and Analysis ::: Aspect-Category Sentiment Analysis Task Compared with GCAE, our AGDT improves the performance by 2.4% and 1.6% in the “DS” part of the two dataset, respectively. These results demonstrate that our AGDT can sufficiently exploit the given aspect to generate the aspect-guided sentence representation, and thus conduct accurate sentiment prediction. The “HDS”, which is designed to measure whether a model can detect different sentiment polarities in a sentence, consists of replicated sentences with different sentiments towards multiple aspects. Our AGDT surpasses GCAE by a very large margin (+11.4% and +4.9% respectively) on both datasets. Experiments ::: Main Results and Analysis ::: Aspect-Term Sentiment Analysis Task In the “HDS” part, the AGDT model obtains +3.6% higher accuracy than GCAE on the restaurant domain and +4.2% higher accuracy on the laptop domain, which shows that our AGDT has stronger ability for the multi-sentiment problem against GCAE. These results further demonstrate that our model works well across tasks and datasets. 
 Question: How big is the improvement over the state-of-the-art results?
Output: AGDT improves the performance by 2.4% and 1.6% in the “DS” part of the two dataset Our AGDT surpasses GCAE by a very large margin (+11.4% and +4.9% respectively) on both datasets In the “HDS” part, the AGDT model obtains +3.6% higher accuracy than GCAE on the restaurant domain and +4.2% higher accuracy on the laptop domain
After Korektor, there are 4262 unique sentences (including 150 seed sentences) that form the COSTRA 1.0 dataset. 
 Question: How many sentence transformations on average are available per unique sentence in dataset?
Output: 27.41 transformation on average of single seed sentence is available in dataset.
Experimental Setup 
 Question: What domains are detected in this paper?
Output: Answer with content missing: (Experimental setup not properly rendered) In our experiments we used seven target domains: “Business and Commerce” (BUS), “Government and Politics” (GOV), “Physical and Mental Health” (HEA), “Law and Order” (LAW),
“Lifestyle” (LIF), “Military” (MIL), and “General Purpose” (GEN). Exceptionally, GEN does
not have a natural root category.
Experimentally, on three benchmark datasets for machine translation – WMT2014, WMT2016 and IWSLT-2014, FlowSeq achieves comparable performance with state-of-the-art non-autoregressive models, and almost constant decoding time w.r.t. the sequence length compared to a typical left-to-right Transformer model, which is super-linear. 
 Question: What are three neural machine translation (NMT) benchmark datasets used for evaluation?
Output: WMT2014, WMT2016 and IWSLT-2014
Baseline Results ::: Speech Synthesis
In our previous work on building speech systems on found data in 700 languages, BIBREF7, we addressed alignment issues (when audio is not segmented into turn/sentence sized chunks) and correctness issues (when the audio does not match the transcription). We used the same techniques here, as described above.

For the best quality speech synthesis we need a few hours of phonetically-balanced, single-speaker, read speech. Our first step was to use the start and end points for each turn in the dialogues, and select those of the most frequent speaker, nmlch. This gave us around 18250 segments. We further automatically removed excessive silence from the start, middle and end of these turns (based on occurrence of F0). This gave us 13 hours and 48 minutes of speech.

We phonetically aligned this data and built a speech clustergen statistical speech synthesizer BIBREF9 from all of this data. For speech recognition (ASR) we used Kaldi BIBREF11. We built neural end-to-end machine translation systems between Mapudungun and Spanish in both directions, using state-of-the-art Transformer architecture BIBREF14 with the toolkit of BIBREF15. 
 Question: What are the models used for the baseline of the three NLP tasks?
Output: state-of-the-art Transformer architecture Kaldi speech clustergen statistical speech synthesizer
Bi-LSTM BIBREF11 is a baseline for neural models. Bi-LSTM$_{+ att. + LEX + POS}$ BIBREF10 is a multi-task learning framework for WSD, POS tagging, and LEX with self-attention mechanism, which converts WSD to a sequence learning task. GAS$_{ext}$ BIBREF12 is a variant of GAS which is a gloss-augmented variant of the memory network by extending gloss knowledge. CAN$^s$ and HCAN BIBREF13 are sentence-level and hierarchical co-attention neural network models which leverage gloss knowledge. 
 Question: How does the neural network architecture accomodate an unknown amount of senses per word?
Output: converts WSD to a sequence learning task  leverage gloss knowledge by extending gloss knowledge
Three metrics in text simplification are chosen in this paper. BLEU BIBREF5 is one traditional machine translation metric to assess the degree to which translated simplifications differed from reference simplifications. FKGL measures the readability of the output BIBREF23 . A small FKGL represents simpler output. SARI is a recent text-simplification metric by comparing the output against the source and reference simplifications BIBREF20 . 
 Question: what evaluation metrics did they use?
Output: BLEU  FKGL  SARI
In terms of speed, our system was able to lemmatize 7.4 million words on a personal laptop in almost 2 minutes compared to 2.5 hours for MADAMIRA, i.e. 75 times faster.  
 Question: How was speed measured?
Output: how long it takes the system to lemmatize a set number of words
n this section, we first describe the phenomenon of mention of political parties names in the profile attributes of users. This is followed by the analysis of profiles that make specific mentions of political handles in their profile attributes. Both of these constitute an organic way of showing support to a party and does not involve any direct campaigning by the parties We opine that the whole campaign of adding Chowkidar to the profile attributes show an inorganic behavior, with political leaders acting as the catalyst. We believe, the effect of changing the profile attribute in accordance with Prime Minister's campaign is an example of inorganic behavior contagion BIBREF6, BIBREF9.   However, we argue that the addition of election campaign related keywords to the profile is a form of inorganic behavior.  
 Question: What are the organic and inorganic ways to show political affiliation through profile changes?
Output: Organic: mention of political parties names in the profile attributes, specific mentions of political handles in the profile attributes.
Inorganic:  adding Chowkidar to the profile attributes, the effect of changing the profile attribute in accordance with Prime Minister's campaign, the addition of election campaign related keywords to the profile.
Two datasets are exploited in this article. Both datasets consist of plain text containing clinical narrative written in Spanish, and their respective manual annotations of sensitive information in BRAT BIBREF13 standoff format. NUBes BIBREF4 is a corpus of around 7,000 real medical reports written in Spanish and annotated with negation and uncertainty information. In order to avoid confusion between the two corpus versions, we henceforth refer to the version relevant in this paper as NUBes-PHI (from `NUBes with Personal Health Information'). The organisers of the MEDDOCAN shared task BIBREF3 curated a synthetic corpus of clinical cases enriched with sensitive information by health documentalists 
 Question: What are the clinical datasets used in the paper?
Output: MEDDOCAN NUBes-PHI
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 types of various community texts have been investigated for exploring global structure of binomials?
Output: news publications, wine reviews, and Reddit
In view of the differences between English and Chinese, we made adaptations for the PDTB-3 scheme such as removing AltLexC and adding Progression into our sense hierarchy. 
 Question: How are resources adapted to properties of Chinese text?
Output: removing AltLexC and adding Progression into our sense hierarchy
To compare our results, we use the provided baseline, which is a non-parameter optimized linear-kernel SVM that uses TF-IDF bag-of-word vectors as inputs.  
 Question: What was the baseline model?
Output: a non-parameter optimized linear-kernel SVM that uses TF-IDF bag-of-word vectors as inputs
Fourteen such feature extractors have been implemented which can be clubbed into 3 major categories:

[noitemsep]

Lexicon Features

Word Vectors

Syntax Features 
 Question: how many total combined features were there?
Output: Fourteen
Further, FSDM has a new module called response slot binary classifier that adds extra supervision to generate the slots that will be present in the response more precisely before generating the final textual agent response (see Section "Methodology" for details). 
 Question: How do slot binary classifiers improve performance?
Output: by adding extra supervision to generate the slots that will be present in the response
The words are represented as fixed length vectors INLINEFORM0 resulting from the concatenation of GloVe pre-trained embeddings and DepecheMood BIBREF19 lexicon representation. Since we cannot directly concatenate token-based embeddings (provided in GloVe) with the lemma#PoS-based representation available in DepecheMood, we proceeded to re-build the latter in token-based form, applying the exact same methodology albeit with two differences: we started from a larger dataset (51.9K news articles instead of 25.3K) and used a frequency cut-off, i.e. keeping only those tokens that appear at least 5 times in the corpus. 
 Question: How do they incorporate lexicon into the neural network?
Output: concatenation of GloVe pre-trained embeddings and DepecheMood BIBREF19 lexicon representation cannot directly concatenate  re-build the latter in token-based form
We see that gated architectures almost always outperform recurrent, attention and linear models BoW, TFIDF, PV. This is largely because while training and testing on same domains, these models especially recurrent and attention based may perform better. However, for Domain Adaptation, as they lack gated structure which is trained in parallel to learn importance, their performance on target domain is poor as compared to gated architectures. As gated architectures are based on convolutions, they exploit parallelization to give significant boost in time complexity as compared to other models. The effectiveness of gated architectures rely on the idea of training a gate with sole purpose of identifying a weightage. In the task of sentiment analysis this weightage corresponds to what weights will lead to a decrement in final loss or in other words, most accurate prediction of sentiment. In doing so, the gate architecture learns which words or n-grams contribute to the sentiment the most, these words or n-grams often co-relate with domain independent words. On the other hand the gate gives less weightage to n-grams which are largely either specific to domain or function word chunks which contribute negligible to the overall sentiment. This is what makes gated architectures effective at Domain Adaptation. 
 Question: Does the fact that GCNs can perform well on this tell us that the task is simpler than previously thought?
Output: No
The grammar induction method works on the premise of curriculum learning BIBREF7 , where the parser first learns to parse simple sentences, then proceeds to learn more complex ones. The induction method is iterative, semi-automatic and based on frequent patterns. A context-free grammar (CFG) is induced from the text, which is represented by several layers of semantic annotations. 
 Question: How did they induce the CFG?
Output: the parser first learns to parse simple sentences, then proceeds to learn more complex ones. The induction method is iterative, semi-automatic and based on frequent patterns
Hot spots were originally annotated with 8 levels and degrees, ranging from `not hot' to `luke warm' to `hot +'. Every utterance was labeled with one of these discrete labels by a single annotator. Hightened involvement is rare, being marked on only 1% of utterances. 
 Question: What annotations are available in ICSI meeting corpus?
Output: 8 levels and degrees, ranging from `not hot' to `luke warm' to `hot +'. Every utterance was labeled with one of these discrete labels by a single annotator
We evaluate our approach by training an autocomplete system on 500K randomly sampled sentences from Yelp reviews BIBREF6 (see Appendix for details). We quantify the efficiency of a communication scheme $(q_{\alpha },p_{\beta })$ by the retention rate of tokens, which is measured as the fraction of tokens that are kept in the keywords. The accuracy of a scheme is measured as the fraction of sentences generated by greedily decoding the model that exactly matches the target sentence. 
 Question: How are models evaluated in this human-machine communication game?
Output: by training an autocomplete system on 500K randomly sampled sentences from Yelp reviews
We used Convolutional Neural Network (CNN) network whose structure is similar to the one proposed by BIBREF29.  
 Question: What neural architectures are used?
Output: Convolutional Neural Network (CNN)
The following different sense inventories have been used during the evaluation: Watlink, a word sense network constructed automatically. It uses the synsets induced in an unsupervised way by the Watset[CWnolog, MCL] method BIBREF2 and the semantic relations from such dictionaries as Wiktionary referred as Joint INLINEFORM0 Exp INLINEFORM1 SWN in Ustalov:17:dialogue. 
 Question: Which corpus of synsets are used?
Output: Wiktionary
While BERT-QC achieves large gains over existing methods on the ARC dataset, here we demonstrate that BERT-QC also matches state-of-the-art performance on TREC BIBREF6 , while surpassing state-of-the-art performance on the GARD corpus of consumer health questions BIBREF3 and MLBioMedLAT corpus of biomedical questions BIBREF4 . 
 Question: Which datasets are used for evaluation?
Output: ARC  TREC GARD  MLBioMedLAT
To compare fairly, we train and evaluate these systems on the VLSP corpora. In particular, we conduct experiments on Viet Treebank corpus for POS tagging and chunking tasks, and on VLSP shared task 2016 corpus for NER task. All of these corpora are converted to CoNLL format. The corpus of POS tagging task consists of two columns namely word, and POS tag. For chunking task, there are three columns namely word, POS tag, and chunk in the corpus. The corpus of NER task consists of four columns. The order of these columns are word, POS tag, chunk, and named entity. While NER corpus has been separated into training and testing parts, the POS tagging and chunking data sets are not previously divided. For this reason, we use INLINEFORM0 of these data sets as a training set, and the remaining as a testing set. Because our system adopts early stopping method, we use INLINEFORM1 of these data sets from the training set as a development set when training NNVLP system. Table TABREF24 and Table TABREF25 shows the statistics of each corpus. 
 Question: What datasets do they use for the tasks?
Output:  Viet Treebank corpus for POS tagging and chunking tasks, and on VLSP shared task 2016 corpus for NER task
Stacked LSTMs Cell-aware Stacked LSTMs
Now we extend the stacked LSTM formulation defined above to address the problem noted in the previous subsection. Sentence Encoders
The sentence encoder network we use in our experiments takes INLINEFORM0 words (assumed to be one-hot vectors) as input. Top-layer Classifiers
For the natural language inference experiments, we use the following heuristic function proposed by BIBREF36 in feature extraction: DISPLAYFORM0

where INLINEFORM0 means vector concatenation, and INLINEFORM1 and INLINEFORM2 are applied element-wise. 
 Question: Which models did they experiment with?
Output: Stacked LSTMs Cell-aware Stacked LSTMs Sentence Encoders Top-layer Classifiers
While many focus on giving a vector representation of a word, an increasing number now exist that will give a vector representation of a entire sentence or text. Following on from this work, we seek to devise a system that can run online, performing text clustering on the embeddings of texts one at a time 
 Question: How is an incoming claim used to retrieve similar factchecked claims?
Output: text clustering on the embeddings of texts
Subsequently, an LSTM decoder generates the characters in the output word form using encoder states and an attention mechanism. 
 Question: Does the model have attention?
Output: Yes
While neural machine translation (NMT) has achieved impressive performance in high-resource data conditions, becoming dominant in the field BIBREF0 , BIBREF1 , BIBREF2 , recent research has argued that these models are highly data-inefficient, and underperform phrase-based statistical machine translation (PBSMT) or unsupervised methods in low-data conditions BIBREF3 , BIBREF4 .  
 Question: what pitfalls are mentioned in the paper?
Output: highly data-inefficient underperform phrase-based statistical machine translation
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. When measuring out-of-domain performance, we will use the WMT newstest 2014. or French we use samples from News-Commentary-11 and Wikipedia from WMT 2014 shared translation task, as well as the Multi-UN BIBREF9 and EU-Bookshop BIBREF10 corpora. For German, we use samples from News-Commentary-11, Rapid, Common-Crawl (WMT 2017) and Multi-UN (see table TABREF5 ).  
 Question: what dataset is used?
Output: Europarl corpus  WMT newstest 2014 News-Commentary-11 Wikipedia from WMT 2014 Multi-UN EU-Bookshop Rapid Common-Crawl (WMT 2017)
Experiments ::: Baselines
For comparison, we select several public models as baselines including semantic parsing models: BiDAF BIBREF3, an MRC model which utilizes a bi-directional attention flow network to encode the question and passage;

QANet BIBREF12, which utilizes convolutions and self-attentions as the building blocks of encoders to represent the question and passage;

BERT BIBREF23, a pre-trained bidirectional Transformer-based language model which achieves state-of-the-art performance on lots of public MRC datasets recently;

and numerical MRC models: NAQANet BIBREF6, a numerical version of QANet model.

NAQANet+, an enhanced version of NAQANet implemented by ourselves, which further considers real number (e.g. “2.5”), richer arithmetic expression, data augmentation, etc. Syn Dep BIBREF6, the neural semantic parsing model (KDG) BIBREF22 with Stanford dependencies based sentence representations;

OpenIE BIBREF6, KDG with open information extraction based sentence representations;

SRL BIBREF6, KDG with semantic role labeling based sentence representations;

and traditional MRC models: 
 Question: what are the existing models they compared with?
Output: Syn Dep OpenIE SRL BiDAF QANet BERT NAQANet NAQANet+
Three baseline classification methods: Support Vector Machines (SVM), Adaboost, and Random Forests are adopted to evaluate our extracted features. On each dataset, the employed classifiers are trained with individual feature first, and then with the combination of the two features. 
 Question: LDA is an unsupervised method; is this paper introducing an unsupervised approach to spam detection?
Output: No
Yet, we noticed a common misbehavior for all our multimodal models: if the attention loose track of the objects in the picture and "gets lost", the model still takes it into account and somehow overrides the information brought by the text-based annotations. The translation is then totally mislead.  
 Question: What misbehavior is identified?
Output: if the attention loose track of the objects in the picture and "gets lost", the model still takes it into account and somehow overrides the information brought by the text-based annotations