niryuu/sni-each-converted huggingface dataset:
On inspection, the drop in performance between translated and original Japanese was often a result of translations that were reasonable but not consistent with the labels. For example, when translating the first example in Figure FIGREF2, both machine translations map “UTF8min風邪”, which means cold (the illness), into “UTF8min寒さ”, which means cold (low temperature). Another example is where the Japanese pseudo-tweet “UTF8min花粉症の時期はすごい疲れる。” was provided alongside an English pseudo-tweet “Allergy season is so exhausting.”. Here, the Japanese word for hay fever “UTF8min花粉症。” has been manually mapped to the less specific word “allergies” in English; the machine translation maps back to Japanese using the word for “allergies” i.e. “UTF8minアレルギー” in the katakana alphabet (katakana is used to express words derived from foreign languages), since there is no kanji character for the concept of allergies. 
 Question: Is there any explanation why some choice of language pair is better than the other?
Output: translations that were reasonable but not consistent with the labels
We extract data from the WMT'14 English-French (En-Fr) and English-German (En-De) datasets.  
 Question: Are this techniques used in training multilingual models, on what languages?
Output: English to French and English to German
Questions: We make use of the 7,787 science exam questions of the Aristo Reasoning Challenge (ARC) corpus BIBREF31 , which contains standardized 3rd to 9th grade science questions from 12 US states from the past decade.  
 Question: How was the dataset collected?
Output: from 3rd to 9th grade science questions collected from 12 US states
Since, we there was no publicly available standard Nepali NER dataset and did not receive any dataset from the previous researchers, we had to create our own dataset. This dataset contains the sentences collected from daily newspaper of the year 2015-2016.  
 Question: What is the source of their dataset?
Output: daily newspaper of the year 2015-2016
We used the annotated dataset reported by degen2015investigating, a dataset of the utterances from the Switchboard corpus of telephone dialogues BIBREF21 that contain the word some. The dataset consists of 1,362 unique utterances with a noun phrase containing some (some-NP). For each example with a some-NP, degen2015investigating collected inference strength ratings from at least 10 participants recruited on Amazon's Mechanical Turk. Participants saw both the target utterance and ten utterances from the preceding discourse context. They then rated the similarity between the original utterance like (UNKREF8) and an utterance in which some was replaced with some, but not all like (UNKREF9), on a 7-point Likert scale with endpoints labeled “very different meaning” (1) and “same meaning” (7). Low similarity ratings thus indicate low inference strength, and high similarity ratings indicate high inference strength. 
 Question: Which dataset do they use?
Output: the annotated dataset reported by degen2015investigating, a dataset of the utterances from the Switchboard corpus of telephone dialogues BIBREF21 that contain the word some
We explore how to utilize additional commonsense knowledge (i.e. rationales) as the input to the task. Like we mentioned in Section SECREF6, we search relevant sentences from the OMCS corpus as the additional distant rationales, and ground truth rationale sentences for dev/test data. The inputs are no longer the concept-sets themselves, but in a form of “[rationales$|$concept-set]” (i.e. concatenating the rationale sentences and original concept-set strings). 
 Question: Are the models required to also generate rationales?
Output: No
Overall, this confirms our initial intuition that combining the best performing popularity-based approach with the best similarity-based approach should result in the highest accuracy (i.e., INLINEFORM7 for INLINEFORM8 ). 
 Question: which algorithm was the highest performer?
Output: A hybrid model consisting of best performing popularity-based approach with the best similarity-based approach
In this regard, we observed that HDGAN produced relatively better visual results on the CUB and Oxford datasets while AttnGAN produced far more impressive results than the rest on the more complex COCO dataset. In terms of inception score (IS), which is the metric that was applied to majority models except DC-GAN, the results in Table TABREF48 show that StackGAN++ only showed slight improvement over its predecessor, StackGAN, for text-to-image synthesis. In addition, the results in Table TABREF48 also show that DM-GAN BIBREF53 has the best performance, followed by Obj-GAN BIBREF81. Notice that both DM-GAN and Obj-GAN are most recently developed methods in the field (both published in 2019), indicating that research in text to image synthesis is continuously improving the results for better visual perception and interception. 
 Question: What is the conclusion of comparison of proposed solution?
Output: HDGAN produced relatively better visual results on the CUB and Oxford datasets while AttnGAN produced far more impressive results than the rest on the more complex COCO dataset In terms of inception score (IS), which is the metric that was applied to majority models except DC-GAN, the results in Table TABREF48 show that StackGAN++ only showed slight improvement over its predecessor text to image synthesis is continuously improving the results for better visual perception and interception
The Word2Vec architecture has inspired a great deal of research in the bio/cheminformatics domains. The Word2Vec algorithm has been successfully applied for determining protein classes BIBREF44 and protein-protein interactions (PPI) BIBREF56. 
 Question: Is there any concrete example in the paper that shows that this approach had huge impact on drug discovery?
Output: Yes
To verify the reliability of the transformed semantic space, we propose an evaluation benchmark on the basis of word similarity datasets. Given an enriched space INLINEFORM0 and a similarity dataset INLINEFORM1 , we compute the similarity of each word pair INLINEFORM2 as the cosine similarity of their corresponding transformed vectors INLINEFORM3 and INLINEFORM4 from the two spaces, where INLINEFORM5 and INLINEFORM6 for LS and INLINEFORM7 and INLINEFORM8 for CCA. A high performance on this benchmark shows that the mapping has been successful in placing semantically similar terms near to each other whereas dissimilar terms are relatively far apart in the space. We repeat the computation for each pair in the reverse direction. 
 Question: How is performance measured?
Output: To verify the reliability of the transformed semantic space, we propose an evaluation benchmark on the basis of word similarity datasets. Given an enriched space INLINEFORM0 and a similarity dataset INLINEFORM1 , we compute the similarity of each word pair INLINEFORM2 as the cosine similarity of their corresponding transformed vectors INLINEFORM3 and INLINEFORM4 from the two spaces, where INLINEFORM5 and INLINEFORM6 for LS and INLINEFORM7 and INLINEFORM8 for CCA.
The above pseudocode is agnostic with respect to the choice of fragmentation and environment functions; task-specific choices are described in more detail for each experiment below.

Discussion 
 Question: Which languages do they test on?
Output: Answer with content missing: (Applications section) We use Wikipedia articles
in five languages
(Kinyarwanda, Lao, Pashto, Tok Pisin, and a subset of English) as well as the Na dataset of Adams
et al. (2017).
Select:
Kinyarwanda, Lao, Pashto, Tok Pisin, and a subset of English
We collect three years of online news articles from June 2016 to June 2019. 
 Question: What unlabeled corpus did they use?
Output: three years of online news articles from June 2016 to June 2019
In this section, we compare our model with state-of-the-art systems, including those with different degrees of supervision. The baselines include: (1) Procrustes BIBREF11, which learns a linear mapping through Procrustes Analysis BIBREF36. (2) GPA BIBREF37, an extension of Procrustes Analysis. (3) GeoMM BIBREF38, a geometric approach which learn a Mahalanobis metric to refine the notion of similarity. (4) GeoMM$_{semi}$, iterative GeoMM with weak supervision. (5) Adv-C-Procrustes BIBREF11, which refines the mapping learned by Adv-C with iterative Procrustes, which learns the new mapping matrix by constructing a bilingual lexicon iteratively. (6) Unsup-SL BIBREF13, which integrates a weak unsupervised mapping with a robust self-learning. (7) Sinkhorn-BT BIBREF28, which combines sinkhorn distance BIBREF29 and back-translation. 
 Question: What are current state-of-the-art methods that consider the two tasks independently?
Output: Procrustes GPA GeoMM GeoMM$_{semi}$ Adv-C-Procrustes Unsup-SL Sinkhorn-BT
Data was collected from a 10% uniform sample of Twitter posts made during 2013, specifically the Gardenhose API. Control documents were also selected. These documents did not contain any of `caused', `causing', or `causes', nor any bidirectional words, and are further matched temporally to obtain the same number of control documents as causal documents in each fifteen-minute period during 2013. Control documents were otherwise selected randomly; causal synonyms may be present. 
 Question: how do they collect the comparable corpus?
Output: Randomly from a Twitter dump
The basic concept is to use a phone-discriminative model to produce frame-level phonetic features, and then use these features to enhance RNN LID systems that were originally built with raw acoustic features. The initial step is therefore feature combination, with the phonetic feature used as auxiliary information to assist acoustic RNN LID. This is improved further, as additional research identified that a simpler model using only the phonetic feature as the RNN LID input provides even better performance. We call this RNN model based on phonetic features the phonetic temporal neural LID approach, or PTN LID. As well as having a simplified model structure, the PTN offers deeper insight into the LID task by rediscovering the value of the phonetic temporal property in language discrimination.  
 Question: What is the main contribution of the paper? 
Output: Proposing an improved RNN model, the phonetic temporal neural LID approach, based on phonetic features that results in better performance
The embedding-wise convolution is to apply a convolution filter w $\in \mathbb {R}^{\mathcal {\\}k*d}$ to a window of $k$ word embeddings to generate a new feature, i.e., summarizing a local context of $k$ words. By applying the convolutional filter to all possible windows in the sentence, a feature map $c$ will be generated. 
 Question: What supports the claim that injected CNN into recurent units will enhance ability of the model to catch local context and reduce ambiguities?
Output: word embeddings to generate a new feature, i.e., summarizing a local context
Curriculum Plausibility ::: Conversational Attributes ::: Specificity
A notorious problem for neural dialogue generation model is that the model is prone to generate generic responses. Curriculum Plausibility ::: Conversational Attributes ::: Repetitiveness
Repetitive responses are easy to generate in current auto-regressive response decoding, where response generation loops frequently, whereas diverse and informative responses are much more complicated for neural dialogue generation. Curriculum Plausibility ::: Conversational Attributes ::: Continuity
A coherent response not only responds to the given query, but also triggers the next utterance. Curriculum Plausibility ::: Conversational Attributes ::: Model Confidence
Despite the heuristic dialogue attributes, we further introduce the model confidence as an attribute, which distinguishes the easy-learnt samples from the under-learnt samples in terms of the model learning ability. Curriculum Plausibility ::: Conversational Attributes ::: Query-relatedness
A conversation is considered to be coherent if the response correlates well with the given query. 
 Question: What five dialogue attributes were analyzed?
Output: Model Confidence Continuity Query-relatedness Repetitiveness Specificity
To evaluate our model capability with processing the Chinese language, we also tested the performance of LCF-ATEPC on four Chinese comment datasets BIBREF35, BIBREF36, BIBREF29 (Car, Phone, Notebook, Camera). 
 Question: In what four Chinese review datasets does LCF-ATEPC achieves state of the art?
Output: Car, Phone, Notebook, Camera
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.  
 Question: What was their system's performance?
Output: accuracy and F1-score of 89.6% and 89.2%, respectively
The experiment settings from this paper and evaluation protocol for the Mboshi corpus (Boundary F-scores using the ZRC speech reference) are the same from BIBREF8. Table presents the results for bilingual UWS and multilingual leveraging.  
 Question: Does the paper report the accuracy of the model?
Output: No
n this paper we provide a novel real-time and adaptive cryptocurrency price prediction platform based on Twitter sentiments. The integrative and modular platform copes with the three aforementioned challenges in several ways. Firstly, it provides a Spark-based architecture which handles the large volume of incoming data in a persistent and fault tolerant way. Secondly, the proposed platform offers an approach that supports sentiment analysis based on VADER which can respond to large amounts of natural language processing queries in real time. Thirdly, the platform supports a predictive approach based on online learning in which a machine learning model adapts its weights to cope with new prices and sentiments. Finally, the platform is modular and integrative in the sense that it combines these different solutions to provide novel real-time tool support for bitcoin price prediction that is more scalable, data-rich, and proactive, and can help accelerate decision-making, uncover new opportunities and provide more timely insights based on the available and ever-larger financial data volume and variety. 
 Question: Which elements of the platform are modular?
Output: handling large volume incoming data, sentiment analysis on tweets and predictive online learning
As in the Sequicity framework, we report entity match rate, BLEU score and Success F1 score.  
 Question: What were the evaluation metrics used?
Output: entity match rate BLEU score Success F1 score
In ConMask, we use a similar idea to select the most related words given some relationship and mask irrelevant words by assigning a relationship-dependent similarity score to words in the given entity description. ConMask selects words that are related to the given relationship to mitigate the inclusion of irrelevant and noisy words. 
 Question: Can the model add new relations to the knowledge graph, or just new entities?
Output: The model does not add new relations to the knowledge graph.
We employ BIBREF73 's weighted lexicon of terms that uses the dataset of 75,394 Facebook users who shared their status, age and gender. Utilizing these two weighted lexicon of terms, we are predicting the demographic information (age or gender) of INLINEFORM0 (denoted by INLINEFORM1 ) using following equation: INLINEFORM2

where INLINEFORM0 is the lexicon weight of the term, and INLINEFORM1 represents the frequency of the term in the user generated INLINEFORM2 , and INLINEFORM3 measures total word count in INLINEFORM4 . 
 Question: How do this framework facilitate demographic inference from social media?
Output: Demographic information is predicted using weighted lexicon of terms.
Evaluation Metrics. To evaluate the strategy formulation ability, we introduce a measure called Coverage( INLINEFORM0 ), defined as the fraction of total query data instances, for which LiLi has successfully formulated strategies that lead to winning. If LiLi wins on all episodes for a given dataset, INLINEFORM1 is 1.0. To evaluate the predictive performance, we use Avg. MCC and avg. +ve F1 score. 
 Question: What metrics are used to establish that this makes chatbots more knowledgeable and better at learning and conversation? 
Output: Coverage Avg. MCC and avg. +ve F1 score
Following the previous works, we report the precision ($P.$), recall ($R.$) and $F_1$ scores for target recognition and targeted sentiment.  
 Question: How is the effectiveness of the model evaluated?
Output: precision ($P.$), recall ($R.$) and $F_1$ scores for target recognition and targeted sentiment
To compare the conversions between USVC and PitchNet, we employed an automatic evaluation score and a human evaluation score. The automatic score roughly followed the design in BIBREF13. The pitch tracker of librosa package BIBREF18 was employed to extract pitch information of the input and output audio. Then the output pitch was compared to the input pitch using the normalized cross correlation (NCC) which would give a score between 0 and 1. The higher the score is, the better the output pitch matches the input pitch. We conducted the evaluation on USVC (our) and PitchNet. The evaluated automatic scores on conversion and reconstruction tasks are shown in Tab. TABREF14. Our method performed better both on conversion and reconstruction. The scores of reconstruction are higher than conversion since both models were trained using a reconstruction loss. However, the score of our method on conversion is even higher than the score of USVC (Our) on reconstruction. Mean Opinion Score (MOS) was used as a subjective metric to evaluate the quality of the converted audio. Two questions were asked: (1) what is quality of the audio? (naturalness) (2) How well does the converted version match the original? (similarity) A score of 1-5 would be given to answer the questions. The evaluation was conducted on USVC (Our) and PitchNet. Besides, the converted samples provided by BIBREF0 was also included to give a more convincing evaluation. As shown by Tab. TABREF15, the naturalness and similarity of our method are both higher than the other two ones. Our implementation of USVC performed slightly lower than the original author's because we cannot fully reproduce the results of them. Next we qualitatively analyze the influence of input pitch in our method. We used different pitch as input to observe how the output pitch would change along with the input pitch. The input pitch was multiplied by 0.7, 1.0 and 1.3 respectively. And the output pitch was also extracted by the pitch tracker of the librosa package. Fig. FIGREF16 plots the pitch of input audio and output audio with different pitch as input while keeping the target singer the same. As shown by Fig. FIGREF16, the output pitch changes significantly along with the input pitch. The examples are also presented at our website. 
 Question: How is the quality of singing voice measured?
Output: To compare the conversions between USVC and PitchNet, we employed an automatic evaluation score and a human evaluation score.
Five attributes, specifying certain details of clinical significance, are defined to characterize the answer types of INLINEFORM4 : (1) the time the patient has been experiencing the symptom, (2) activities that trigger the symptom (to occur or worsen), (3) the extent of seriousness, (4) the frequency occurrence of the symptom, and (5) the location of symptom. For each symptom/attribute, it can take on different linguistic expressions, defined as entities. Note that if the queried symptom or attribute is not mentioned in the dialogue, the groundtruth output is “No Answer”, as in BIBREF6 . 
 Question: What labels do they create on their dataset?
Output: (1) the time the patient has been experiencing the symptom, (2) activities that trigger the symptom (to occur or worsen), (3) the extent of seriousness, (4) the frequency occurrence of the symptom, and (5) the location of symptom No Answer
In order to find the object type, SimplerVoice, first, builds an ontology-based knowledge tree. We propose to use 2 methods to generate the suitable verbs for the target object: heuristics-based, and n-grams model. 
 Question: Which model do they use to generate key messages?
Output: ontology-based knowledge tree heuristics-based n-grams model
Table TABREF15 shows a comparison of the results on SimCluster versus K-means algorithm. Here our SimCluster algorithm improves the F1-scores from 0.412 and 0.417 in the two domains to 0.442 and 0.441. The ARI scores also improve from 0.176 and 0.180 to 0.203 and 0.204. 
 Question: Do they use the same distance metric for both the SimCluster and K-means algorithm?
Output: Yes
Notably, this increase is observed after the conclusion of the US presidential primaries and during the period of the Democratic and Republican National Conventions and does not reduce even after the conclusion of the US presidential elections held on November 8. 
 Question: What other political events are included in the database?
Output: US presidential primaries Democratic and Republican National Conventions
Each annotator annotated 90 reference sentences (i.e. from the training corpus) with which style they thought the sentence was from. The accuracy on this baseline task for annotators A1, A2, and A3 was 80%, 88%, and 80% respectively, giving us an upper expected bound on the human evaluation. 
 Question: How they perform manual evaluation, what is criteria?
Output: accuracy
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.  
 Question: What is the difference of the proposed model with a standard RNN encoder-decoder?
Output: Introduce a "Refinement Adjustment LSTM-based component" to the decoder
Our resulting model obtains state of the art results on both the VQA dataset and the bAbI-10k text question-answering dataset, proving the framework can be generalized across input domains. 
 Question: Does the DMN+ model establish state-of-the-art ?
Output: Yes
Motivated by this, we introduce resolution mode variables $\Pi = \lbrace \pi _1, \ldots , \pi _n\rbrace $ , where for each mention $j$ the variable $\pi _j \in \lbrace str, prec, attr\rbrace $ indicates in which mode the mention should be resolved. In our model, we define three resolution modes — string-matching (str), precise-construct (prec), and attribute-matching (attr) — and $\Pi $ is deterministic when $D$ is given (i.e. $P(\Pi |D)$ is a point distribution). We determine $\pi _j$ for each mention $m_j$ in the following way:

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

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

$\pi _j = attr$ , if there is no mention $m_i, i < j$ satisfies the above two conditions. 
 Question: What are resolution model variables?
Output: Variables in the set {str, prec, attr} indicating in which mode the mention should be resolved.
RKS approach proposed in BIBREF21, BIBREF22, explicitly maps data vectors to a space where linear separation is possible. The RKS method provides an approximate kernel function via explicit mapping. 
 Question: What is the Random Kitchen Sink approach?
Output: Random Kitchen Sink method uses a kernel function to map data vectors to a space where linear separation is possible.
The results in Table TABREF7 show that SRCC performs much better in knowledge extraction. The two documents' contents contain the same idea expressed by terms in a different order that John had asked Mary to marry him before she left. It is obvious that cosine similarity cannot recognize this association, but SRCC has successfully recognized it and produced a similarity value of -0.285714. 
 Question: How do they evaluate knowledge extraction performance?
Output: SRCC
We introduce a new crowdsourced dataset of 23,700 queries, including 22,500 in-scope queries covering 150 intents, which can be grouped into 10 general domains.  
 Question: Where does the data come from?
Output: crowsourcing platform
To enable sentence-level classification we need to obtain a sentence representation from the word vectors INLINEFORM0 . We achieved this by using a BiLSTM with max pooling, which was shown to be a good universal sentence encoding mechanism BIBREF13 . 
 Question: Which model architecture do they use to obtain representations?
Output: BiLSTM with max pooling
The incomplete dataset used for training is composed of lower-cased incomplete data obtained by manipulating the original corpora. 
 Question: Do they test their approach on a dataset without incomplete data?
Output: No
Baselines. We compare our approach (FacTweet) to the following set of baselines: LR + Bag-of-words: We aggregate the tweets of a feed and we use a bag-of-words representation with a logistic regression (LR) classifier.

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

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

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

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

Top-$k$ replies, likes, or re-tweets: Some approaches in rumors detection use the number of replies, likes, and re-tweets to detect rumors BIBREF21. Thus, we extract top $k$ replied, liked or re-tweeted tweets from each account to assess the accounts factuality. We tested different $k$ values between 10 tweets to the max number of tweets from each account. Figure FIGREF24 shows the macro-F1 values for different $k$ values. It seems that $k=500$ for the top replied tweets achieves the highest result. Therefore, we consider this as a baseline. 
 Question: What baselines were used in this work?
Output: LR + Bag-of-words Tweet2vec LR + All Features (tweet-level) LR + All Features (chunk-level) FacTweet (tweet-level) Top-$k$ replies, likes, or re-tweets
To build the large ancient-modern Chinese dataset, we collected 1.7K bilingual ancient-modern Chinese articles from the internet. More specifically, a large part of the ancient Chinese data we used come from ancient Chinese history records in several dynasties (about 1000BC-200BC) and articles written by celebrities of that era. 
 Question: Where does the ancient Chinese dataset come from?
Output: ancient Chinese history records in several dynasties (about 1000BC-200BC) and articles written by celebrities of that era
It is a well known fact that NLP applications in industrial settings often have to deal with the noisy data. There are different kinds of possible noise namely non-canonical text such as spelling mistakes, typographic errors, colloquialisms, abbreviations, slang, internet jargon, emojis, embedded metadata (such as hashtags, URLs, mentions), non standard syntactic constructions and spelling variations, grammatically incorrect text, mixture of two or more languages to name a few. Such noisy data is a hallmark of user generated text content and commonly found on social media, chats, online reviews, web forums to name a few. 
 Question: What kind is noise is present in typical industrial data?
Output:  non-canonical text such as spelling mistakes, typographic errors, colloquialisms, abbreviations, slang, internet jargon, emojis, embedded metadata (such as hashtags, URLs, mentions), non standard syntactic constructions and spelling variations, grammatically incorrect text, mixture of two or more languages
Notably, disabling the first layer in the RTE task gives a significant boost, resulting in an absolute performance gain of 3.2%. However, effects of this operation vary across tasks, and for QNLI and MNLI, it produces a performance drop of up to -0.2%. 
 Question: How much is performance improved by disabling attention in certain heads?
Output: disabling the first layer in the RTE task gives a significant boost, resulting in an absolute performance gain of 3.2%  this operation vary across tasks
We use 300 Dimensional Glove Common Crawl Embeddings (840B Token) BIBREF11 and fine tune them for the task 
 Question: What embedding algorithm and dimension size are used?
Output: 300 Dimensional Glove
For experiments, we train and evaluate our models in the civil law system of mainland China. We collect and construct a large-scale real-world data set of INLINEFORM0 case documents that the Supreme People's Court of People's Republic of China has made publicly available 
 Question: what is the size of the real-world civil case dataset?
Output: 100 000 documents
In Section SECREF16, we first provide more details about the experimental setting that we followed.  As explained in Section SECREF3, we used BabelNet BIBREF29 as our reference taxonomy. BabelNet is a large-scale full-fledged taxonomy consisting of heterogeneous sources such as WordNet BIBREF36, Wikidata BIBREF37 and WiBi BIBREF38, making it suitable to test our hypothesis in a general setting.  To test our proposed category induction model, we consider all BabelNet categories with fewer than 50 known instances. This is motivated by the view that conceptual neighborhood is mostly useful in cases where the number of known instances is small. For each of these categories, we split the set of known instances into 90% for training and 10% for testing. To tune the prior probability $\lambda _A$ for these categories, we hold out 10% from the training set as a validation set. 
 Question: What experiments they perform to demonstrate that their approach leads more accurate region based representations?
Output:  To test our proposed category induction model, we consider all BabelNet categories with fewer than 50 known instances. This is motivated by the view that conceptual neighborhood is mostly useful in cases where the number of known instances is small. For each of these categories, we split the set of known instances into 90% for training and 10% for testing.
Our approach improves LR by 5.17% (Accuracy) and 18.38% (AUC), and MLP by 10.71% (Accuracy) and 30.27% (AUC) on average. Such significant improvements clearly demonstrate that our approach is effective at improving model performance. 
 Question: How are the accuracy merits of the approach demonstrated?
Output: significant improvements clearly demonstrate that our approach is effective at improving model performance
Causal documents were chosen to contain one occurrence only of the exact unigrams: `caused', `causing', or `causes'. 
 Question: What are the selection criteria for "causal statements"?
Output: Presence of only the exact unigrams 'caused', 'causing', or 'causes'
First, in order to deal with the unseen relations, we propose to break the relation names into word sequences for question-relation matching. Second, noticing that original relation names can sometimes help to match longer question contexts, we propose to build both relation-level and word-level relation representations. Third, we use deep bidirectional LSTMs (BiLSTMs) to learn different levels of question representations in order to match the different levels of relation information. Finally, we propose a residual learning method for sequence matching, which makes the model training easier and results in more abstract (deeper) question representations, thus improves hierarchical matching. 
 Question: What they use in their propsoed framework?
Output: break the relation names into word sequences  relation-level and word-level relation representations bidirectional LSTMs (BiLSTMs)  residual learning method
In this part, we show the performance of our proposed models—HAKE and ModE—against existing state-of-the-art methods, including TransE BIBREF8, DistMult BIBREF9, ComplEx BIBREF17, ConvE BIBREF18, and RotatE BIBREF7. 
 Question: What are state-of-the art models for this task?
Output: TransE DistMult ComplEx ConvE RotatE
In this work, we consider words that can indicate the characteristics of the neighbor words as contextual keywords and develop an approach to generate features from the automatically extracted contextual keywords. 
 Question: What contextual features are used?
Output: The words that can indicate the characteristics of the neighbor words as contextual keywords and generate it from the automatically extracted contextual keywords.
Thus we propose two kinds of relation-specific meta information: relation meta and gradient meta corresponding to afore mentioned two perspectives respectively. In our proposed framework MetaR, relation meta is the high-order representation of a relation connecting head and tail entities. Gradient meta is the loss gradient of relation meta which will be used to make a rapid update before transferring relation meta to incomplete triples during prediction. 
 Question: What meta-information is being transferred?
Output: high-order representation of a relation, loss gradient of relation meta
The tweetLID workshop shared task requires systems to identify the language of tweets written in Spanish (es), Portuguese (pt), Catalan (ca), English (en), Galician (gl) and Basque (eu).  
 Question: What shared task does this system achieve SOTA in?
Output: tweetLID workshop shared task
Sememes are minimum semantic units of word meanings, and the meaning of each word sense is typically composed of several sememes, as is illustrated in Figure 1 . 
 Question: What is a sememe?
Output: Sememes are minimum semantic units of word meanings, and the meaning of each word sense is typically composed of several sememes
The first one is a Turkish news-web corpus containing 423M words and 491M tokens, namely the BOUN Web Corpus BIBREF9 , BIBREF10 . The second one is composed of 21M Turkish tweets with 241M words and 293M tokens, where we combined 1M tweets from TS TweetS by Sezer-2013 and 20M Turkish Tweets by Bolat and Amasyalı. 
 Question: What data was used to build the word embeddings?
Output: Turkish news-web corpus  TS TweetS by Sezer-2013 and 20M Turkish Tweets by Bolat and Amasyalı
Even among researchers familiar with the definitions outlined above, there was still a low level of agreement (Krippendorff's INLINEFORM0 ). 
 Question: How was reliability measured?
Output: level of agreement (Krippendorff's INLINEFORM0 )
We identify many specific syntactic features that make sentences harder to classify, and many that have little effect. For instance, sentences involving unusual or marked argument structures are no harder than the average sentence, while sentences with long distance dependencies are hard to learn. We also find features of sentences that accentuate or minimize the differences between models. Specifically, the transformer models seem to learn long-distance dependencies much better than the recurrent model, yet have no advantage on sentences with morphological violations. We identify many specific syntactic features that make sentences harder to classify, and many that have little effect. For instance, sentences involving unusual or marked argument structures are no harder than the average sentence, while sentences with long distance dependencies are hard to learn. We also find features of sentences that accentuate or minimize the differences between models. Specifically, the transformer models seem to learn long-distance dependencies much better than the recurrent model, yet have no advantage on sentences with morphological violations. 
 Question: Which models are best for learning long-distance movement?
Output: the transformer models
We crowdsource sentence specificity for evaluation for three target domains: Twitter, Yelp reviews and movie reviews. 
 Question: What domains do they experiment with?
Output: Twitter, Yelp reviews and movie reviews
Figure FIGREF18 shows the confusion matrices of labels between annotators as heatmaps. 
 Question: How is the annotation experiment evaluated?
Output: confusion matrices of labels between annotators
The final annotated dataset consists of 1000 conversations composed of 6833 sentences and 88047 tokens.  
 Question: What is the size of the dataset?
Output: 1000 conversations composed of 6833 sentences and 88047 tokens
Chatino is a group of languages spoken in Oaxaca, Mexico. Together with the Zapotec language group, the Chatino languages form the Zapotecan branch of the Otomanguean language family.  
 Question: Which language family does Chatino belong to?
Output: the Otomanguean language family
We adopt the annotation machinery of BIBREF5 implemented using Amazon's Mechanical Turk, and annotate each predicate by 2 trained workers independently, while a third consolidates their annotations into a final set of roles and arguments.  
 Question: How was the previous dataset annotated?
Output: the annotation machinery of BIBREF5
We collected a simplified dataset from Simple English Wikipedia that are freely available, which has been previously used for many text simplification methods BIBREF0 , BIBREF10 , BIBREF3 . 
 Question: what language does this paper focus on?
Output: English
In BIBREF14 , several word embedding models trained in a large Portuguese corpus are evaluated.  Using the word2vec model available in a public repository BIBREF14 , the proposal involves the analysis of the most similar analogies generated before and after the application of the BIBREF3 .  
 Question: What were the word embeddings trained on?
Output: large Portuguese corpus
Finally, we performed hyperparameter tuning by adjusting the number of coattention blocks, the batch size, and the number of epochs trained and ensembled our three best networks. 
 Question: What hyperparameters have been tuned?
Output: number of coattention blocks, the batch size, and the number of epochs trained and ensembled our three best networks
Dialogue Collection: before the formal data collection starts, we required the workers to make a small number of dialogues and gave them feedback about the dialogue quality. Then, well-trained workers were paired to converse according to the given goals. The workers were also asked to annotate both user states and system states.

Dialogue Annotation: we used some rules to automatically annotate dialogue acts according to user states, system states, and dialogue histories. To evaluate the quality of the annotation of dialogue acts and states, three experts were employed to manually annotate dialogue acts and states for 50 dialogues. The results show that our annotations are of high quality. Finally, each dialogue contains a structured goal, a task description, user states, system states, dialogue acts, and utterances. 
 Question: How was the corpus annotated?
Output: The workers were also asked to annotate both user states and system states we used some rules to automatically annotate dialogue acts according to user states, system states, and dialogue histories
A simple, yet recurrent text model is the well-known word co-occurrence network. After optional textual pre-processing steps, in a co-occurrence network each different word becomes a node and edges are established via co-occurrence in a desired window. A common strategy connects only adjacent words in the so called word adjacency networks.

While the co-occurrence representation yields good results in classification scenarios, some important features are not considered in the model. For example, long-range syntactical links, though less frequent than adjacent syntactical relationships, might be disregarded from a simple word adjacency approach BIBREF12. 
 Question: What is the reason that traditional co-occurrence networks fail in establishing links between similar words whenever they appear distant in the text?
Output: long-range syntactical links, though less frequent than adjacent syntactical relationships, might be disregarded from a simple word adjacency approach
The choice of two methods for our empirical study is motivated by the best performance achieved by Logistic Regression in question-question similarity at SemEval 2017 (best system BIBREF37 and second best system BIBREF38 ), and the high performance achieved by neural networks on larger datasets such as SNLI BIBREF13 , BIBREF39 , BIBREF40 , BIBREF41  
 Question: What machine learning and deep learning methods are used for RQE?
Output: Logistic Regression neural networks
The resulting dataset consists of 22,880 users, 41,094 blogs, and 561,003 posts. Table TABREF2 presents additional statistics of our dataset. 
 Question: How many users do they look at?
Output: 22,880 users
Architecture-based methods either try to induce task-specific architecture during pre-training (task-specific methods), or aim at building a general pre-training architecture to fit all downstream tasks (task-agnostic methods) 
 Question: How architecture-based method handle obstacles in NLG?
Output: task-specific architecture during pre-training (task-specific methods) aim at building a general pre-training architecture to fit all downstream tasks (task-agnostic methods)
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). 
 Question: What dataset do they use?
Output: English$\rightarrow $Italian/German portions of the MuST-C corpus 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)
The training objective is to minimize the negative likelihood of the aligned unanswerable question $\tilde{q}$ given the answerable question $q$ and its corresponding paragraph $p$ that contains the answer $a$ : L=-(q,q,p,a)DP(q|q,p,a;) where $\mathcal {D}$ is the training corpus and $\theta $ denotes all the parameters. Sequence-to-sequence and pair-to-sequence models are trained with the same objective. 
 Question: What is the training objective of their pair-to-sequence model?
Output: is to minimize the negative likelihood of the aligned unanswerable question $\tilde{q}$ given the answerable question $q$ and its corresponding paragraph $p$ that contains the answer
To model an answer which is a collection of spans, the multi-span head uses the $\mathtt {BIO}$ tagging format BIBREF8: $\mathtt {B}$ is used to mark the beginning of a span, $\mathtt {I}$ is used to mark the inside of a span and $\mathtt {O}$ is used to mark tokens not included in a span. In this way, we get a sequence of chunks that can be decoded to a final answer - a collection of spans. 
 Question: How they use sequence tagging to answer multi-span questions?
Output: To model an answer which is a collection of spans, the multi-span head uses the $\mathtt {BIO}$ tagging format BIBREF8: $\mathtt {B}$ is used to mark the beginning of a span, $\mathtt {I}$ is used to mark the inside of a span and $\mathtt {O}$ is used to mark tokens not included in a span
$Coherence@k$ has been shown to have high correlation with human interpretability of topics learned via various topic modeling methods BIBREF7 . Hence, we can expect interpretable embeddings by maximizing it. For evaluating the interpretability, we use $Coherence@k$ (Equation 6 ) , automated and manual word intrusion tests. 
 Question: How do they evaluate interpretability?
Output: For evaluating the interpretability, we use $Coherence@k$ (Equation 6 ) , automated and manual word intrusion tests.
Our RoBERTa baseline performs quite well on HotpotQA (77.0 F1), despite processing each paragraph separately, which prohibits inter-paragraph reasoning. We fine-tune a pre-trained model to take a question and several paragraphs and predicts the answer, similar to the single-hop QA model from BIBREF21.  We use $\textsc {RoBERTa}_{\textsc {LARGE}}$ BIBREF24 as our pre-trained initialization.  
 Question: What is the strong baseline that this work outperforms?
Output: RoBERTa baseline
By analyzing large numbers of dialogue act sequences correlated with specific outcomes, various rules can be derived, i.e. "Continuing to request information late in a conversation often leads to customer dissatisfaction." This can then be codified into a best practice pattern rules for automated systems, such as "A request for information act should be issued early in a conversation, followed by an answer, informative statement, or apology towards the end of the conversation." Our analysis helps zone in on how the use of certain dialogue acts may be likely to result in different outcomes. The weights we observe vary in the amount of insight provided: for example, offering extra help at the end of a conversation, or thanking the customer yields more satisfied customers, and more resolved problems (with ratios of above 6:1). However, some outcomes are much more subtle: for example, asking yes-no questions early-on in a conversation is highly associated with problem resolution (ratio 3:1), but asking them at the end of a conversation has as similarly strong association with unsatisfied customers. Giving elaborate answers that are not a simple affirmative, negative, or response acknowledgement (i.e. Answer (Other)) towards the middle of a conversation leads to satisfied customers that are not frustrated. Likewise, requesting information towards the end of a conversation (implying that more information is still necessary at the termination of the dialogue) leads to unsatisfied and unresolved customers, with ratios of at least 4:1. 
 Question: Which patterns and rules are derived?
Output: A request for information act should be issued early in a conversation, followed by an answer, informative statement, or apology towards the end of the conversation  offering extra help at the end of a conversation, or thanking the customer yields more satisfied customers, and more resolved problems  asking yes-no questions early-on in a conversation is highly associated with problem resolution (ratio 3:1), but asking them at the end of a conversation has as similarly strong association with unsatisfied customers Giving elaborate answers that are not a simple affirmative, negative, or response acknowledgement (i.e. Answer (Other)) towards the middle of a conversation leads to satisfied customers that are not frustrated requesting information towards the end of a conversation (implying that more information is still necessary at the termination of the dialogue) leads to unsatisfied and unresolved customers
The method is evaluated on the BERTbase model, which has 12 layers, 12 self-attention heads with a hidden size of 768.

To accelerate the training speed, two-phase training BIBREF1 is adopted. The first phase uses a maximal sentence length of 128, and 512 for the second phase. The numbers of training steps of two phases are 50K and 40K for the BERTBase model. We used AdamW BIBREF13 optimizer with a learning rate of 1e-4, a $\beta _1$ of 0.9, a $\beta _2$ of 0.999 and a L2 weight decay rate of $0.01$. The first 10% of the total steps are used for learning rate warming up, followed by the linear decay schema. We used a dropout probability of 0.1 on all layers. The data used for pre-training is the same as BERT, i.e., English Wikipedia (2500M words) and BookCorpus (800M words) BIBREF14. 
 Question: Do they train their model starting from a checkpoint?
Output: No