niryuu/sni-each-converted huggingface dataset:
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.
Output: What is the source of the "control" corpus?
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.
Output: how do they collect the comparable corpus?
We used dataset, HEOT obtained from one of the past studies done by Mathur et al. where they annotated a set of cleaned tweets obtained from twitter for the conversations happening in Indian subcontinent. A labelled dataset for a corresponding english tweets were also obtained from a study conducted by Davidson et al.
Output: What dataset is used?
We used dataset, HEOT obtained from one of the past studies done by Mathur et al. where they annotated a set of cleaned tweets obtained from twitter for the conversations happening in Indian subcontinent. A labelled dataset for a corresponding english tweets were also obtained from a study conducted by Davidson et al.
Output: How is the dataset collected?
A meta-research study found only 1 in 9 qualitative papers in Human-Computer Interaction reported inter-rater reliability metrics BIBREF31. Another related area are meta-research and methods papers focused on identifying or preventing low-effort responses from crowdworkers — sometimes called “spam” or “random” responses, or alternatively ”fraudsters” or ”cheaters.” Rates of “self-agreement” are often used, determining if the same person labels the same item differently at a later stage.
Output: What are the key issues around whether the gold standard data produced in such an annotation is reliable?
Despite this, JESSI performs second on Subtask A with an F1 score of 77.78% among 33 other team submissions. It also performs well on Subtask B with an F1 score of 79.59%.
Output: How did they do compared to other teams?
We apply attention probes to the task of identifying the existence and type of dependency relation between two words. Our first experiment is an exploratory visualization of how word sense affects context embeddings. 
Output: What linguistic features were probed for?
We can identify three main cases for which the model produces an error:

the correct class can be directly inferred from the text content easily, even without background knowledge

the correct class can be inferred from the text content, given that event-specific knowledge is provided

the correct class can be inferred from the text content if the text is interpreted correctly
Output: What type of errors do the classifiers use?
Results on LangID and NoLangID are compared to the system presented by deri2016grapheme, which is identified in our results as wFST.
Output: what was the baseline?
We assume that speech transcripts can be extracted from audio signals with high accuracy, given the advancement of ASR technologies BIBREF7 .
Output: Do they use datasets with transcribed text or do they determine text from the audio?
For training and testing we use the train-validation-test split of WikiTableQuestions BIBREF0 , a dataset containing 22,033 pairs of questions and answers based on 2,108 Wikipedia tables. This dataset is also used by our baselines, BIBREF0 , BIBREF3 .
Output: Does the dataset they use differ from the one used by Pasupat and Liang, 2015?
We assume that for each corpora INLINEFORM0 , we are given word embeddings for each word INLINEFORM1 , where INLINEFORM2 is the dimension of each word embedding. We are also given a classification task on documents that is represented by a parametric model INLINEFORM3 taking document embeddings as feature vectors. We construct a GAN model which combines different sets of word embeddings INLINEFORM4 , INLINEFORM5 , into a single set of word embeddings INLINEFORM6 . Note that INLINEFORM7 are given but INLINEFORM8 is trained. Here we consider INLINEFORM9 as the generator, and the goal of the discriminator is to distinguish documents represented by the original embeddings INLINEFORM10 and the same documents represented by the new embeddings INLINEFORM11 .
Output: Which GAN do they use?
A community with a very distinctive identity will tend to have distinctive interests, expressed through specialized language. Formally, we define the distinctiveness of a community INLINEFORM0 as the average specificity of all utterances in INLINEFORM1 
Output: How do the authors measure how distinctive a community is?
In order to make the corpus collection easier and faster, we adopted a semi-automatic procedure based on sequential neural models BIBREF19, BIBREF20. Instead, since tokens are transcribed at morpheme level, we split Arabish tokens into characters, and Arabic tokens into morphemes, and we treated each token itself as a sequence. Our model learns thus to map Arabish characters into Arabic morphemes. With this model we automatically transcribed into Arabic morphemes, roughly, 5,000 additional tokens, corresponding to the second annotation block.  Manual transcription plus a
Output: How does the semi-automatic construction process work?
he immediate impact of the explicit constraint is avoiding the collapse issue ($D_{KL}=0$) by setting a non-zero positive constraint ($C\ge 0$) on the KL term ($|D_{KL}\big (q_\phi ({z}|{x}) || p({z})\big )-C|$).
Output: Why does proposed term help to avoid posterior collapse?
The same 36 questions were answered using four QALD tools: WDAqua BIBREF0 , QAKiS BIBREF7 , gAnswer BIBREF6 and Platypus BIBREF8 .
Output: Which four QA systems do they use?
We evaluate our newly proposed models and related baselines in several low-resource settings across different languages with real, distantly supervised data with non-synthetic noise. 
Output: How they evaluate their approach?
To measure the effect of adding Bertram to BERT on downstream tasks, we apply the procedure described in Section SECREF4 to a commonly used textual entailment dataset as well as two text classification datasets: MNLI BIBREF21, AG's News BIBREF22 and DBPedia BIBREF23. 
Output: What are three downstream task datasets?
To evaluate real-world performance of our selected classifier (i.e., performance in the absence of model and parameter bias), we perform classification of the holdout set. On this set, our classifier had an accuracy and F1-score of 89.6% and 89.2%, respectively. 
Output: What was their system's performance?
We compare our approach with two baseline vision-based methods proposed in BIBREF6 , BIBREF7 , which measure the similarity of two sets of global visual features for bilingual lexicon induction:

CNN-mean: taking the similarity score of the averaged feature of the two image sets.

CNN-avgmax: taking the average of the maximum similarity scores of two image sets.
Output: Which vision-based approaches does this approach outperform?
We compare our approach with two baseline vision-based methods proposed in BIBREF6 , BIBREF7 , which measure the similarity of two sets of global visual features for bilingual lexicon induction:

CNN-mean: taking the similarity score of the averaged feature of the two image sets.

CNN-avgmax: taking the average of the maximum similarity scores of two image sets.
Output: What baseline is used for the experimental setup?
How the music taste of the audience of popular music changed in the last century? The trend lines of the MUSIC model features, reported in figure FIGREF12, reveal that audiences wanted products more and more contemporary, intense and a little bit novel or sophisticated, but less and less mellow and (surprisingly) unpretentious. In other words, the audiences of popular music are getting more demanding as the quality and variety of the music products increases.
Output: What trends are found in musical preferences?
the attention heads in the proposed adaptively sparse Transformer can specialize more and with higher confidence In particular, we analyzed how the attention heads in the proposed adaptively sparse Transformer can specialize more and with higher confidence.
Output: How does their model improve interpretability compared to softmax transformers?
The final annotated dataset consists of 1000 conversations composed of 6833 sentences and 88047 tokens. 
Output: What is the size of the dataset?
The retrieved pairs are compared to the gold standard and evaluated using precision at $k$ (P@$k$, evaluating how often the correct translation is within the $k$ retrieved nearest neighbours of the query). Throughout this work we report P@1, which is equivalent to accuracy, but we also provide results with P@5 and P@10 in the Appendix.
Output: What evaluation metrics did they use?
Our approach to FEVER is to fix the most obvious shortcomings of the baseline approaches to retrieval and entailment, and to train a sharp entailment classifier that can be used to filter a broad set of retrieved potential evidence. For the entailment classifier we compare Decomposable Attention BIBREF2 , BIBREF3 as implemented in the official baseline, ESIM BIBREF4 , and a transformer network with pre-trained weights BIBREF5 . The transformer network naturally supports out-of-vocabulary words and gives substantially higher performance than the other methods.
Output: What baseline do they compare to?
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.
Output: Was the approach used in this work to detect fake news fully supervised?
Fortunately, 180221 of 231162 author names could be matched successfully. There are many reasons for the remaining uncovered cases. 9073 Latin names could not be found in the name dictionary ENAMDICT and 14827 name matchings between the names' Latin and kanji representations did not succeed. These names might be missing at all in the dictionary, delivered in a very unusual format that the tool does not cover, or might not be Japanese or human names at all. Of course, Japanese computer scientists sometimes also cooperate with foreign colleagues but our tool expects Japanese names and is optimized for them.
Output: How successful are they at matching names of authors in Japanese and English?
Email classifier using machine learning ::: Machine learning approach ::: Feature selection
Ngrams are a continuous sequence of n items from a given sample of text. From title, body and OCR text words are selected. Ngrams of 3 nearby words are extracted with Term Frequency-Inverse Document Frequency (TF-IDF) vectorizing, then features are filtered using chi squared the feature scoring method. Email classifier using machine learning ::: Machine learning approach ::: Random forest
Random Forest is a bagging Algorithm, an ensemble learning method for classification that operates by constructing a multitude of decision trees at training time and outputting the class that has highest mean majority vote of the classesBIBREF14. Email classifier using machine learning ::: Machine learning approach ::: XGBoost
XGBoost is a decision-tree-based ensemble Machine Learning algorithm that uses a gradient boosting framework. It is used commonly in the classification problems involving unstructured dataBIBREF5. Email classifier using machine learning ::: Machine learning approach ::: Hierarchical Model
Since the number of target labels are high, achieving the higher accuracy is difficult, while keeping all the categories under same feature selection method. Some categories performs well with lower TF-IDF vectorizing range and higher n grams features even though they showed lower accuracy in the overall single model. Therefore, hierarchical machine learning models are built to classify 31 categories in the first classification model and remaining categories are named as low-accu and predicted as one category. In the next model, predicted low-accu categories are again classified into 47 categories. Comparatively this hierarchical model works well since various feature selection methods are used for various categoriesBIBREF5.
Output: What are all machine learning approaches compared in this work?
We show the statistics of the dataset we use in tab:statistics, and the construction procedures will be introduced in this section. In Wikidata BIBREF8 , facts can be described as (Head item/property, Property, Tail item/property).  We first choose the most common 202 relations and 120000 entities from Wikidata as our initial data. ReVerb BIBREF9 is a program that automatically identifies and extracts binary relationships from English sentences. We use the extractions from running ReVerb on Wikipedia. FB15K BIBREF3 is a subset of freebase. TACRED BIBREF10 is a large supervised relation extraction dataset obtained via crowdsourcing. We directly use these two dataset, no extra processing steps were applied.
Output: Which knowledge bases do they use?
The final embedding dimensionality is equal to the number of unique word labels in the training set, which is 1061.
Output: Which dimensionality do they use for their embeddings?
However, a significant bottleneck that has been overlooked by previous efforts is the input vocabulary size and its corresponding word embedding matrix, often accounting for a significant proportion of all model parameters. For instance, the embedding table of the BERTBASE model, comprising over 30K WordPiece tokens BIBREF14, accounts for over $21\%$ of the model size. While there has been existing work on reducing NLP model vocabulary sizes BIBREF15, distillation techniques cannot utilize these, since they require the student and teacher models to share the same vocabulary and output space. This profoundly limits their potential to further reduce model sizes.
Output: Why are prior knowledge distillation techniques models are ineffective in producing student models with vocabularies different from the original teacher models?
In light of this, we suggest that care is needed when applying graph embeddings in NLP pipelines, and work needed to develop robust methods to debias such embeddings.
Output: Do they propose any solution to debias the embeddings?
We extract data from the WMT'14 English-French (En-Fr) and English-German (En-De) datasets. To create a larger discrepancy between the tasks, so that there is a clear dataset size imbalance, the En-De data is artificially restricted to only 1 million parallel sentences, while the full En-Fr dataset, comprising almost 40 million parallel sentences, is used entirely. 
Output: What datasets are used for experiments?
Validated transcripts were sent to professional translators. In order to control the quality of the professional translations, we applied various sanity checks to the translations BIBREF11. We also sanity check the overlaps of train, development and test sets in terms of transcripts and voice clips (via MD5 file hashing), and confirm they are totally disjoint.
Output: How is the quality of the data empirically evaluated?
We use OpenNMT BIBREF24 as the implementation of the NMT system for all experiments BIBREF5 . PBMT-R is a phrase-based method with a reranking post-processing step BIBREF18 . Hybrid performs sentence splitting and deletion operations based on discourse representation structures, and then simplifies sentences with PBMT-R BIBREF25 . SBMT-SARI BIBREF19 is syntax-based translation model using PPDB paraphrase database BIBREF26 and modifies tuning function (using SARI). Dress is an encoder-decoder model coupled with a deep reinforcement learning framework, and the parameters are chosen according to the original paper BIBREF20 .
Output: what state of the art methods did they compare with?
Knowledge Base Question Answering (KBQA) systems answer questions by obtaining information from KB tuples BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5
Output: What does KBQA abbreviate for
In this paper, in spite of previous statements, we present a system that uses rule-based and dictionary-based methods combined (in a way we prefer to call resource-based). 
Output: What does their system consist of?
We found that properties of the emerging lexicon related to the combinatoriality, namely the words length distribution, the frequency of use of the different forms and a measure for the combinatoriality itself, reflect both qualitatively and quantitatively the corresponding properties as measured in human languages, provided that the memory parameter $\tau $ is sufficiently high, that is that a sufficiently high effort is required in order to understand and learn brand new forms.
Output: What empirical data are the Blending Game predictions compared to?
he first, OpenIE 4 BIBREF5 , descends from two popular OIE systems OLLIE BIBREF10 and Reverb BIBREF10 . The second was MinIE BIBREF7 , which is reported as performing better than OLLIE, ClauseIE BIBREF9 and Stanford OIE BIBREF9 .
Output: Which OpenIE systems were used?
Our method is tested on Twitter datasets. This microblogging platform has been widely used to analyze discussions and polarization BIBREF12, BIBREF13, BIBREF14, BIBREF15, BIBREF2. It is a natural choice for these kind of problems, as it represents one of the main fora for public debate in online social media BIBREF15, it is a common destination for affiliative expressions BIBREF16 and is often used to report and read news about current events BIBREF17. An extra advantage of Twitter for this kind of studies is the availability of real-time data generated by millions of users. Other social media platforms offer similar data-sharing services, but few can match the amount of data and the accompanied documentation provided by Twitter. One last asset of Twitter for our work is given by retweets, whom typically indicate endorsement BIBREF18 and hence become a useful concept to model discussions as we can set “who is with who". However, our method has a general approach and it could be used a priori in any social network. In this work we report excellent result tested on Twitter but in future work we are going to test it in other social networks.
Output: What social media platform is observed?
Overall, this resulted in a total of 34,432 user conversations. Together, these users gave Gunrock an average rating of 3.65 (median: 4.0), which was elicited at the end of the conversation (“On a scale from 1 to 5 stars, how do you feel about talking to this socialbot again?").
Output: What is the sample size of people used to measure user satisfaction?
Despite these shortcomings in using WER as a metric of success, the WER can reflect our model's effectiveness in generating questions that are similar to those of SQuAD based on the given reading passage and answer. WER can be used for initial analyses that can lead to deeper insights as discussed further below.
Output: Why did they choose WER as evaluation metric?
Using sequence-to-sequence networks, the approach here is jointly training annotated utterance-level intents and slots/intent keywords by adding / tokens to the beginning/end of each utterance, with utterance-level intent-type as labels of such tokens. Our approach is an extension of BIBREF2 , in which only an term is added with intent-type tags associated to this sentence final token, both for LSTM and Bi-LSTM cases. However, we experimented with adding both and terms as Bi-LSTMs will be used for seq2seq learning, and we observed that slightly better results can be achieved by doing so. The idea behind is that, since this is a seq2seq learning problem, at the last time step (i.e., prediction at ) the reverse pass in Bi-LSTM would be incomplete (refer to Fig. FIGREF24 (a) to observe the last Bi-LSTM cell). Therefore, adding token and leveraging the backward LSTM output at first time step (i.e., prediction at ) would potentially help for joint seq2seq learning. An overall network architecture can be found in Fig. FIGREF30 for our joint models. We will report the experimental results on two variations (with and without intent keywords) as follows:

Joint-1: Seq2seq Bi-LSTM for utterance-level intent detection (jointly trained with slots)

Joint-2: Seq2seq Bi-LSTM for utterance-level intent detection (jointly trained with slots & intent keywords)
Output: What is shared in the joint model?
Following previous studies BIBREF1, we collect event-related microposts from Twitter using 11 and 8 seed events (see Section SECREF2) for CyberAttack and PoliticianDeath, respectively. Unlabeled microposts are collected by using the keyword `hack' for CyberAttack, while for PoliticianDeath, we use a set of keywords related to `politician' and `death' (such as `bureaucrat', `dead' etc.)
Output: Do they report results only on English data?
We apply the same data split in BIBREF31, BIBREF30, BIBREF6, which uses news (the union of bn and nw) as the training set, a half of bc as the development set and the remaining data as the test set.

We learn the model parameters using Adam BIBREF32. We apply dropout BIBREF33 to the hidden layers to reduce overfitting. The development set is used for tuning the model hyperparameters and for early stopping. We train 5 Bi-LSTM English RE models initiated with different random seeds, apply the 5 models on the target languages, and combine the outputs by selecting the relation type labels with the highest probabilities among the 5 models.
Output: Do they train their own RE model?
We address this issue by We address this issue by examining and reviewing publicly available datasets for abusive content detection, which we provide access to on a new dedicated website, hatespeechdata.com. We address this issue by examining and reviewing publicly available datasets for abusive content detection, which we provide access to on a new dedicated website, hatespeechdata.com.
Output: What is open website for cataloguing abusive language data?
We empirically test our approach on a series of experiments on WikiTableQuestions, to our knowledge the only dataset designed for this task. In Algorithm 1 we describe how logical forms are transformed into interpretable textual representations called "paraphrases".
Output: What is the source of the paraphrases of the questions?
We experiment and compare with the following models.

Pointer-Gen is the baseline model trained by optimizing $L_\text{MLE}$ in Equation DISPLAY_FORM13.

Pointer-Gen+Pos is the baseline model by training Pointer-Gen only on positive examples whose sensationalism score is larger than 0.5

Pointer-Gen+Same-FT is the model which fine-tunes Pointer-Gen on the training samples whose sensationalism score is larger than 0.1

Pointer-Gen+Pos-FT is the model which fine-tunes Pointer-Gen on the training samples whose sensationalism score is larger than 0.5

Pointer-Gen+RL-ROUGE is the baseline model trained by optimizing $L_\text{RL-ROUGE}$ in Equation DISPLAY_FORM17, with ROUGE-L BIBREF9 as the reward.

Pointer-Gen+RL-SEN is the baseline model trained by optimizing $L_\text{RL-SEN}$ in Equation DISPLAY_FORM17, with $\alpha _\text{sen}$ as the reward.
Output: Which baselines are used for evaluation?
For example, it can be used to extract features for usage with other machine learning tools, or to evaluate given features with the existing classifiers or regressors. Figure FIGREF19 demonstrates a simple feature extractor that retrieves the sentence length.
Output: Do they show an example of usage for INFODENS?
Our experiments are run using the English$\rightarrow $Italian/German portions of the MuST-C corpus BIBREF25, which is extracted from TED talks, using the same train/validation/test split as provided with the corpus (see Table TABREF18). As additional data, we use a mix of public and proprietary data for about 16 million sentence pairs for English-Italian (En-It) and $4.4$ million WMT14 sentence pairs for the English-German (En-De).
Output: What dataset do they use?
Our experiments are run using the English$\rightarrow $Italian/German portions of the MuST-C corpus BIBREF25, which is extracted from TED talks, using the same train/validation/test split as provided with the corpus (see Table TABREF18). As additional data, we use a mix of public and proprietary data for about 16 million sentence pairs for English-Italian (En-It) and $4.4$ million WMT14 sentence pairs for the English-German (En-De).
Output: What dataset do they use for experiments?
Our main contributions are (1) the publication of Turkish corpus for coarse-grained and fine-grained NER, and TC research, (2) six different versions of corpus according to noise reduction methodology and entity types, (3) an analysis of the corpus and (4) benchmark comparisons for NER and TC tasks against human annotators. 
Output: Did they experiment with the dataset on some tasks?
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.
Output: How are resources adapted to properties of Chinese text?
Experimental Setup
Output: which chinese datasets were used?
Experimental Setup
Output: what english datasets were used?
Experimental Setup
Output: What domains are detected in this paper?
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.
Output: How big is the improvement over the state-of-the-art results?
We use the $\textsc {BERT}_{\textsc {BASE}}$ ensemble from BIBREF3 as the single-hop QA model. 
Output: What off-the-shelf QA model was used to answer sub-questions?
Baseline Experiments
In this section, we briefly describe a baseline and evaluation scripts that we release, with a detailed documentation, along with the corpus.
Output: What type of evaluation is proposed for this task?
Baseline Experiments
In this section, we briefly describe a baseline and evaluation scripts that we release, with a detailed documentation, along with the corpus.
Output: What baseline system is proposed?
An important feature when suggesting an article INLINEFORM0 to an entity INLINEFORM1 is the novelty of INLINEFORM2 w.r.t the already existing entity profile INLINEFORM3 Given an entity INLINEFORM0 and the already added news references INLINEFORM1 up to year INLINEFORM2 , the novelty of INLINEFORM3 at year INLINEFORM4 is measured by the KL divergence between the language model of INLINEFORM5 and articles in INLINEFORM6 . We combine this measure with the entity overlap of INLINEFORM7 and INLINEFORM8 . The novelty value of INLINEFORM9 is given by the minimal divergence value. Low scores indicate low novelty for the entity profile INLINEFORM10 .


Output: What features are used to represent the novelty of news articles to entity pages?
Currently, the following WSD models induced from a text corpus are available: Word senses based on cluster word features. This model uses the cluster words from the induced word sense inventory as sparse features that represent the sense.

Word senses based on context word features. This representation is based on a sum of word vectors of all cluster words in the induced sense inventory weighted by distributional similarity scores.

Super senses based on cluster word features. To build this model, induced word senses are first globally clustered using the Chinese Whispers graph clustering algorithm BIBREF9 . The edges in this sense graph are established by disambiguation of the related words BIBREF11 , BIBREF12 . The resulting clusters represent semantic classes grouping words sharing a common hypernym, e.g. “animal”. This set of semantic classes is used as an automatically learned inventory of super senses: There is only one global sense inventory shared among all words in contrast to the two previous traditional “per word” models. Each semantic class is labeled with hypernyms. This model uses words belonging to the semantic class as features.

Super senses based on context word features. This model relies on the same semantic classes as the previous one but, instead, sense representations are obtained by averaging vectors of words sharing the same class.
Output: Do they use a neural model for their task?
However, all previous IE benchmarks BIBREF18 are too small to train neural network models typically used in QA, and thus we need to build a large benchmark. Therefore, we build a large scale benchmark named QA4IE benchmark which consists of 293K Wikipedia articles and 2M golden relation triples with 636 different relation types. We manually find 148 relations which can be projected to a WikiData relation out of 2064 DBpedia relations.
Output: Was this benchmark automatically created from an existing dataset?
We compare our model with other strong competitive methods on the SQuAD leaderboard and TriviaQA leaderboard.
Output: What other solutions do they compare to?
In the first one, shown in Figure FIGREF12, a special dedicated discriminator is added to the model to control that the latent representation does not contain stylistic information. The second extension of the baseline architecture does not use an adversarial component $D_z$ that is trying to eradicate information on $c$ from component $z$. Instead, the system, shown in Figure FIGREF16 feeds the "soft" generated sentence $\tilde{G}$ into encoder $E$ and checks how close is the representation $E(\tilde{G} )$ to the original representation $z = E(x)$ in terms of the cosine distance. We further refer to it as shifted autoencoder or SAE. We also study a combination of both approaches described above, shown on Figure FIGREF17.
Output: What are three new proposed architectures?
For the proposed model, we denote INLINEFORM0 parameterized by INLINEFORM1 as a neural-based feature encoder that maps documents from both domains to a shared feature space, and INLINEFORM2 parameterized by INLINEFORM3 as a fully connected layer with softmax activation serving as the sentiment classifier. We have left the feature encoder INLINEFORM0 unspecified, for which, a few options can be considered. In our implementation, we adopt a one-layer CNN structure from previous works BIBREF22 , BIBREF4 , as it has been demonstrated to work well for sentiment classification tasks.
Output: What is the architecture of the model?
Task 1: Quora Duplicate Question Pair Detection Task 2: Ranking questions in Bing's People Also Ask
Output: On which tasks do they test their conflict method?
To better analyze model generalization to an unseen, new domain as well as model leveraging the out-of-domain sources, we propose a new architecture which is an extension of the ARED model. In order to better select, aggregate and control the semantic information, a Refinement Adjustment LSTM-based component (RALSTM) is introduced to the decoder side. The proposed model can learn from unaligned data by jointly training the sentence planning and surface realization to produce natural language sentences. 
Output: What is the difference of the proposed model with a standard RNN encoder-decoder?
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.
Output: What were the baselines?
For the sentiment analysis, we look at our problem in a multi-label setting, our two labels being sentiment polarity and the candidate/category in consideration.
Output: How many label options are there in the multi-label task?
The method is evaluated on the BERTbase model, which has 12 layers, 12 self-attention heads with a hidden size of 768.
Output: What BERT model do they test?
Our experiments showed that using the objects color or shape to uniquely identify an object allows the robot successfully complete the binning task in 97.6% and 96.0% of the cases. However, using the shape alone as a unique identifier, the task could only be completed in 79.0% of the cases.
Output: What is task success rate achieved?
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
Output: What are the differences between FHIR and RDF?
Clean-labeled Datasets. We use three clean labeled datasets. The first one is the movie sentence polarity dataset from BIBREF19. The other two datasets are laptop and restaurant datasets collected from SemEval-2016 . Noisy-labeled Training Datasets. For the above three domains (movie, laptop, and restaurant), we collected 2,000 reviews for each domain from the same review source.
Output: What is the dataset used to train the model?
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.
Output: What annotations are available in ICSI meeting corpus?
Table 1 shows the official leaderboard on SQuAD test set when we submitted our system. Our model achieves a 68.73% EM score and 77.39% F1 score, which is ranked among the state of the art single models (without model ensembling).
Output: What is the exact performance on SQUAD?
We see that A-gen performance improves significantly with the joint model: both F1 and EM increase by about 10 percentage points.
Output: How much improvement does jointly learning QA and QG give, compared to only training QA?
For the competition, we provided 1,000 labeled dialogues from each dataset for training, and 240 unlabeled dialogues from each dataset for evaluation. 
Output: How large is the first dataset?
For the competition, we provided 1,000 labeled dialogues from each dataset for training, and 240 unlabeled dialogues from each dataset for evaluation. 
Output: What is the size of the second dataset?
We compare the above model to a similar model, where rather than explicitly represent $K$ features as input, we have $K$ features in the form of a genre embedding, i.e. we learn a genre specific embedding for each of the gothic, scifi, and philosophy genres, as studied in BIBREF8 and BIBREF7.
Output: Is this style generator compared to some baseline?
Dataset Quality Analysis ::: Inter-Annotator Agreement (IAA)
To estimate dataset consistency across different annotations, we measure F1 using our UA metric with 5 generators per predicate. Dataset Quality Analysis ::: Dataset Assessment and Comparison
We assess both our gold standard set and the recent Dense set against an integrated expert annotated sample of 100 predicates.  Dataset Quality Analysis ::: Agreement with PropBank Data
It is illuminating to observe the agreement between QA-SRL and PropBank (CoNLL-2009) annotations BIBREF7. 
Output: How was quality measured?
Following the previous works, we perform 10-fold cross validation and report the average results. 
Output: How is the robustness of the model evaluated?
Using our annotation software, we automatically extracted landmarks of 2000 images from the UOttawa database BIBREF14 had been annotated for image segmentation tasks. 
Output: How big are datasets used in experiments?
The informal setting/environment of social media often encourage multilingual speakers to switch back and forth between languages when speaking or writing. These all resulted in code-mixing and code-switching. Code-mixing refers to the use of linguistic units from different languages in a single utterance or sentence, whereas code-switching refers to the co-occurrence of speech extracts belonging to two different grammatical systemsBIBREF3. This language interchange makes the grammar more complex and thus it becomes tough to handle it by traditional algorithms. Thus the presence of high percentage of code-mixed content in social media text has increased the complexity of the aggression detection task. For example, the dataset provided by the organizers of TRAC-2018 BIBREF0, BIBREF2 is actually a code-mixed dataset.
Output: What data/studies do the authors provide to support the assertion that the majority of aggressive conversations contain code-mixed languages?
The PolyResponse restaurant search is currently available in 8 languages and for 8 cities around the world: English (Edinburgh), German (Berlin), Spanish (Madrid), Mandarin (Taipei), Polish (Warsaw), Russian (Moscow), Korean (Seoul), and Serbian (Belgrade).
Output: In what 8 languages is PolyResponse engine used for restourant search and booking system?
We propose a simple and practical human evaluation for evaluating text summarization, where the summary is evaluated against the source content instead of the reference. It handles both of the problems of paraphrasing and lack of high-quality reference.  To avoid the deficiencies, we propose a simple human evaluation method to assess the semantic consistency. Each summary candidate is evaluated against the text rather than the reference. If the candidate is irrelevant or incorrect to the text, or the candidate is not understandable, the candidate is labeled bad. Otherwise, the candidate is labeled good. Then, we can get an accuracy of the good summaries. The proposed evaluation is very simple and straight-forward. It focuses on the relevance between the summary and the text. The semantic consistency should be the major consideration when putting the text summarization methods into practice, but the current automatic methods cannot judge properly. For detailed guidelines in human evaluation, please refer to Appendix SECREF6 . 
Output: What human evaluation method is proposed?
Then, we created test sets with varying levels of perturbation operations - $\lbrace 20\%,40\%,60\%\rbrace $.
Output: Are recurrent neural networks trained on perturbed data?
We analyze which discourse phenomena are hard to capture using monolingual data only. Among the four phenomena in the test sets we use (deixis, lexical cohesion, VP ellipsis and ellipsis which affects NP inflection) we find VP ellipsis to be the hardest phenomenon to be captured using round-trip translations.
Output: what phenomena do they mention is hard to capture?
Our model differs by learning the subword vectors and resulting representation jointly as weighted factorization of a word-context co-occurrence matrix is performed.
Output: Which matrix factorization methods do they use?
Fourteen such feature extractors have been implemented which can be clubbed into 3 major categories:

[noitemsep]

Lexicon Features

Word Vectors

Syntax Features
Output: how many total combined features were there?
We have training and testing sets in three different languages: English, Chinese and Korean. When fine-tuning, we simply adopted the official training script of BERT, with default hyperparameters, to fine-tune each model until training loss converged.
Output: what does the model learn in zero-shot setting?
We see that parent quality is a simple yet effective feature and SVM model with this feature can achieve significantly higher ($p<0.001$) F1 score ($46.61\%$) than distance from the thesis and linguistic features. Although the BiLSTM model with attention and FastText baselines performs better than the SVM with distance from the thesis and linguistic features, it has similar performance to the parent quality baseline. We find that the flat representation of the context achieves the highest F1 score. It may be more difficult for the models with a larger number of parameters to perform better than the flat representation since the dataset is small. We also observe that modeling 3 claims on the argument path before the target claim achieves the best F1 score ($55.98\%$).
Output: How better are results compared to baseline models?
In Figure FIGREF28, we show some examples of the annotation results in CORD-19-NER. We can see that our distantly- or weakly supervised methods achieve high quality recognizing the new entity types, requiring only several seed examples as the input. For example, we recognized “SARS-CoV-2" as the “CORONAVIRUS" type, “bat" and “pangolins" as the “WILDLIFE" type and “Van der Waals forces" as the “PHYSICAL_SCIENCE" type. This NER annotation results help downstream text mining tasks in discovering the origin and the physical nature of the virus. Our NER methods are domain-independent that can be applied to corpus in different domains. In addition, we show another example of NER annotation on New York Times with our system in Figure FIGREF29.

In Figure FIGREF30, we show the comparison of our annotation results with existing NER/BioNER systems. In Figure FIGREF30, we can see that only our method can identify “SARS-CoV-2" as a coronavirus. In Figure FIGREF30, we can see that our method can identify many more entities such as “pylogenetic" as a evolution term and “bat" as a wildlife. In Figure FIGREF30, we can also see that our method can identify many more entities such as “racism" as a social behavior. In summary, our distantly- and weakly-supervised NER methods are reliable for high-quality entity recognition without requiring human effort for training data annotation.
Output: Did they experiment with the dataset?
To further study the information encoded in the discourse embeddings, we perform t-SNE clustering BIBREF20 on them, using the best performing model CNN2-DE (global).
Output: How are discourse embeddings analyzed?
Our attempt at language pre-training fell short of our expectations in all but one tested dataset. Our pre-training was unsuccessful in improving accuracy, even when applied to networks larger than those reported.
Output: Does pre-training on general text corpus improve performance?
We find that while optimizing its reformulations to adapt to the language of the QA system, AQA diverges from well structured language in favour of less fluent, but more effective, classic information retrieval (IR) query operations. 
Output: What is the difference in findings of Buck et al? It looks like the same conclusion was mentioned in Buck et al..
Active learning sharply increases the performance of iteratively trained machine learning models by selectively determining which unlabeled samples should be annotated. 
Output: What is active learning?
SCA BIBREF5 softly augments a randomly chosen word in a sentence by its contextual mixture of multiple related words, i.e., replacing the one-hot representation of a word by a distribution provided by a language model over the vocabulary.
Output: How does soft contextual data augmentation work?
The base sentence embedding model is a BiLSTM over all words in the respective sequences with randomly initialised word embeddings, following BIBREF30 . We opt for this strong baseline sentence encoding model, as opposed to engineering sentence embeddings that work particularly well for this dataset, to showcase the dataset. We would expect pre-trained contextual encoding models, e.g. ELMO BIBREF33 , ULMFit BIBREF34 , BERT BIBREF35 , to offer complementary performance gains, as has been shown for a few recent papers BIBREF36 , BIBREF37 .
Output: What were the baselines?
We use the Universal Dependencies' Hindi-English codemixed data set BIBREF9 to test the model's ability to label code-mixed data. This dataset is based on code-switching tweets of Hindi and English multilingual speakers. We use the Devanagari script provided by the data set as input tokens.
Output: What codemixed language pairs are evaluated?
We pair 11'248 standard German written words with their phonetical representations in six different Swiss dialects: Zürich, St. Gallen, Basel, Bern, Visp, and Stans (Figure FIGREF1).
Output: How many words are coded in the dictionary?
As of June 2019, AA had $\sim $50K entries, however, this includes some number of entries that are not truly research publications (for example, forewords, prefaces, table of contents, programs, schedules, indexes, calls for papers/participation, lists of reviewers, lists of tutorial abstracts, invited talks, appendices, session information, obituaries, book reviews, newsletters, lists of proceedings, lifetime achievement awards, erratum, and notes). We discard them for the analyses here. (Note: CL journal includes position papers like squibs, letter to editor, opinion, etc. We do not discard them.) We are then left with 44,896 articles.
Output: How many papers are used in experiment?
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. 
Output: what language is the data in?
While in this paper, we focus on the contexts and names of entities, there is a textual source of information about entities in KBs which we can also make use of: descriptions of entities. We extract Wikipedia descriptions of FIGMENT entities filtering out the entities ( $\sim $ 40,000 out of $\sim $ 200,000) without description.
Output: How do you find the entity descriptions?
When we annotate dialogues, we should read dialogues from begin to the end. For each utterance, we should find its one parent node at least from all its previous utterances. We assume that the discourse structure is a connected graph and no utterance is isolated. We propose three questions for eache dialogue and annotate the span of answers in the input dialogue. As we know, our dataset is the first corpus for multi-party dialogues reading comprehension.

We construct following questions and answers for the dialogue in Example 1:

Q1: When does Bdale leave?

A1: Fri morning

Q2: How to get people love Mark in Mjg59's opinion.

A2: Hire people to work on reverse-engineering closed drivers.

On the other hand, to improve the difficulty of the task, we propose $ \frac{1}{6}$ to $ \frac{1}{3}$ unanswerable questions in our dataset. We annotate unanswerable questions and their plausible answers (PA). Each plausible answer comes from the input dialogue, but is not the answer for the plausible question.

Q1: Whis is the email of daniels?

PA: +61 403 505 896
Output: Is annotation done manually?