niryuu/sni-each-converted huggingface dataset:
We use word alignments, similarly to other annotation projection work, to project the AMR alignments to the target languages. Our approach depends on an underlying assumption that we make: if a source word is word-aligned to a target word and it is AMR aligned with an AMR node, then the target word is also aligned to that AMR node. Word alignments were generated using fast_align BIBREF10 , while AMR alignments were generated with JAMR BIBREF11 . 
 Question: How is annotation projection done when languages have different word order?
Output: Word alignments are generated for parallel text, and aligned words are assumed to also share AMR node alignments.
To evaluate the performance of our approach, we used a subset of the SNIPS BIBREF12 dataset, which is readily available in RASA nlu format.  We use accuracy of intent and entity recognition as our task and metric.  
 Question: How are their changes evaluated?
Output: The changes are evaluated based on accuracy of intent and entity recognition on SNIPS dataset
To train our model, we generated a dataset of 20,000 demonstrated 7 DOF trajectories (6 robot joints and 1 gripper dimension) in our simulated environment together with a sentence generator capable of creating natural task descriptions for each scenario. In order to create the language generator, we conducted an human-subject study to collect sentence templates of a placement task as well as common words and synonyms for each of the used features. By utilising these data, we are able to generate over 180,000 unique sentences, depending on the generated scenario. To test our model, we generated 500 new scenario testing each of the three features to identify the correct target among other bowls.  
 Question: Does proposed end-to-end approach learn in reinforcement or supervised learning manner?
Output: supervised learning
We also include RNNLM BIBREF11 , a recurrent neural network based language model in the comparison. 
 Question: Which language models do they compare against?
Output: RNNLM BIBREF11
For this knowledge, we create a knowledge base by counting how many times a predicate-argument tuple appears in a corpus and use the resulted number to represent the preference strength. Specifically, we use the English Wikipedia as the base corpus for such counting. 
 Question: What is the source of external knowledge?
Output: counts of predicate-argument tuples from English Wikipedia
We end up with two salient roles called Anchors and Punctual speakers:

the Anchor speakers (A) are above the threshold of 1% for both criteria, meaning they are intervening often and for a long time thus holding an important place in interaction;

the Punctual speakers (PS) on the contrary are below the threshold of 1% for both the total number of turns and the total speech time. 
 Question: How many categories do authors define for speaker role?
Output:  two salient roles called Anchors and Punctual speakers
FastTextBIBREF4: It uses bag of words and bag of n-grams as features for text classification, capturing partial information about the local word order efficiently. BiLSTM: Unlike feedforward neural networks, recurrent neural networks like BiLSTMs use memory based on history information to learn long-distance features and then predict the output. We use a two-layer BiLSTM architecture with GloVe word embeddings as a strong RNN baseline. BERT BIBREF5: It is a contextualized word representation model that uses bidirectional transformers, pretrained on a large $3.3B$ word corpus. We use the $BERT_{large}$ model finetuned on the training dataset. 
 Question: What is the baseline for the experiments?
Output: FastText BiLSTM BERT
We work with a dataset composed of 3,206 news articles, each labeled real or fake, with a perfect 50/50 split between 1,603 real and fake articles, respectively. Fake articles were sourced from online sites that were tagged as fake news sites by the non-profit independent media fact-checking organization Verafiles and the National Union of Journalists in the Philippines (NUJP). Real articles were sourced from mainstream news websites in the Philippines, including Pilipino Star Ngayon, Abante, and Bandera. 
 Question: What is the source of the dataset?
Output: Online sites tagged as fake news site by Verafiles and NUJP and news website in the Philippines, including Pilipino Star Ngayon, Abante, and Bandera
Sentiment: For each cluster, its overall sentiment score is quantified by the mean of the sentiment scores among all tweets. 
 Question: How is sentiment polarity measured?
Output: For each cluster, its overall sentiment score is quantified by the mean of the sentiment scores among all tweets
We evaluate the quality of the document embeddings learned by the different variants of CAHAN and the HAN baseline on three of the large-scale document classification datasets introduced by BIBREF14 and used in the original HAN paper BIBREF5. 
 Question: Do they compare to other models appart from HAN?
Output: No
Originally, the seed dictionaries typically spanned several thousand word pairs BIBREF15 , BIBREF18 , BIBREF19 , but more recent work has shown that CLWEs can be induced with even weaker supervision from small dictionaries spanning several hundred pairs BIBREF20 , identical strings BIBREF21 , or even only shared numerals BIBREF22 . 
 Question: How are seed dictionaries obtained by fully unsupervised methods?
Output: the latest CLWE developments almost exclusively focus on fully unsupervised approaches BIBREF23 , BIBREF24 , BIBREF25 , BIBREF26 , BIBREF27 , BIBREF28 , BIBREF29 , BIBREF30 : they fully abandon any source of (even weak) supervision and extract the initial seed dictionary by exploiting topological similarities between pre-trained monolingual embedding spaces
Through our experiments, we make subtle points related to: (a) the performance of our features, (b) how our approach compares against human ability to detect drunk-texting, (c) most discriminative stylistic features, and (d) an error analysis that points to future work. 
 Question: Do the authors mention any confounds to their study?
Output: No
As with INLINEFORM0 , Greens-EFA, S&D, and EPP exhibit the highest cohesion, even though their ranking is permuted when compared to the ranking obtained with INLINEFORM1 . At the other end of the scale, we observe the same situation as with INLINEFORM2 . The non-aligned members NI have the lowest cohesion, followed by EFDD and ENL. The only place where the two methods disagree is the level of cohesion of GUE-NGL. The Alpha attributes GUE-NGL a rather high level of cohesion, on a par with ALDE, whereas the ERGM attributes them a much lower cohesion. 
 Question: What insights does the analysis give about the cohesion of political groups in the European parliament?
Output: Greens-EFA, S&D, and EPP exhibit the highest cohesion non-aligned members NI have the lowest cohesion, followed by EFDD and ENL two methods disagree is the level of cohesion of GUE-NGL
In this experiment, we compute the correlation of the proposed NED measure with the patient-perceived emotional bond ratings. Since the proposed measure is asymmetric in nature, we compute the measures for both patient-to-therapist and therapist-to-patient entrainment. We report Pearson's correlation coefficients ( INLINEFORM0 ) for this experiment in Table TABREF26 along with their INLINEFORM1 -values.  
 Question: How do they correlate NED with emotional bond levels?
Output: They compute Pearson’s correlation between NED measure for patient-to-therapist and patient-perceived emotional bond rating and NED measure for therapist-to-patient and patient-perceived emotional bond rating
For the comparison we chose the best text model for each representation. As expected we obtain the largest improvement ($22-26\%$ E.R) when text-based unsupervised models are combined with image representations. 
 Question: How much better is inference that has addition of image representation compared to text-only representations? 
Output:  largest improvement ($22-26\%$ E.R) when text-based unsupervised models are combined with image representations
From the in-corpus experiments, we obtain good results on SarcasmCorpus, which is the only corpus containing Amazon reviews. Unfortunately, when we train our models in a cross-corpora or all-corpora setting, our results drop dramatically, especially in the cross-corpora case. These results mean that the sarcasm in SarcasmCorpus is conveyed through features that are not present in the other corpora. 
 Question: In which domains is sarcasm conveyed in different ways?
Output: Amazon reviews
We assess our dataset using traditional and deep learning methods. Our simplest model is a linear SVM trained on word unigrams. SVMs have produced state-of-the-art results for many text classification tasks BIBREF13 . We also train a bidirectional Long Short-Term-Memory (BiLSTM) model, which we adapted from the sentiment analysis system of sentimentSystem,rasooli2018cross and altered to predict offensive labels instead. It consists of (1) an input embedding layer, (2) a bidirectional LSTM layer, (3) an average pooling layer of input features. The concatenation of the LSTM's and average pool layer is passed through a dense layer and the output is passed through a softmax function. We set two input channels for the input embedding layers: pre-trained FastText embeddings BIBREF14 , as well as updatable embeddings learned by the model during training. Finally, we also apply a Convolutional Neural Network (CNN) model based on the architecture of BIBREF15 , using the same multi-channel inputs as the above BiLSTM. 
 Question: What models are used in the experiment?
Output: linear SVM bidirectional Long Short-Term-Memory (BiLSTM) Convolutional Neural Network (CNN)
Our target data set consists of a corpus of discharge summaries, admission notes, individual encounter notes, and other clinical notes from 220 patients in the OnTrackTM program at McLean Hospital. OnTrackTM is an outpatient program, focusing on treating adults ages 18 to 30 who are experiencing their first episodes of psychosis. The length of time in the program varies depending on patient improvement and insurance coverage, with an average of two to three years. The program focuses primarily on early intervention via individual therapy, group therapy, medication evaluation, and medication management. See Table TABREF2 for a demographic breakdown of the 220 patients, for which we have so far extracted approximately 240,000 total EHR paragraphs spanning from 2011 to 2014 using Meditech, the software employed by McLean for storing and organizing EHR data.

These patients are part of a larger research cohort of approximately 1,800 psychosis patients, which will allow us to connect the results of this EHR study with other ongoing research studies incorporating genetic, cognitive, neurobiological, and functional outcome data from this cohort.

We also use an additional data set for training our vector space model, comprised of EHR texts queried from the Research Patient Data Registry (RPDR), a centralized regional data repository of clinical data from all institutions in the Partners HealthCare network. These records are highly comparable in style and vocabulary to our target data set. The corpus consists of discharge summaries, encounter notes, and visit notes from approximately 30,000 patients admitted to the system's hospitals with psychiatric diagnoses and symptoms. This breadth of data captures a wide range of clinical narratives, creating a comprehensive foundation for topic extraction. 
 Question: What datasets did the authors use?
Output:  a corpus of discharge summaries, admission notes, individual encounter notes, and other clinical notes from 220 patients in the OnTrackTM program at McLean Hospital an additional data set for training our vector space model, comprised of EHR texts queried from the Research Patient Data Registry (RPDR)
Modern Standard Arabic (MSA) and Classical Arabic (CA) have two types of vowels, namely long vowels, which are explicitly written, and short vowels, aka diacritics, which are typically omitted in writing but are reintroduced by readers to properly pronounce words. We show that our model achieves a case ending error rate (CEER) of 3.7% for MSA and 2.5% for CA. For MSA, this CEER is more than 60% lower than other state-of-the-art systems such as Farasa and the RDI diacritizer, which are trained on the same dataset and achieve CEERs of 10.7% and 14.4% respectively.  
 Question: what are the previous state of the art?
Output: Farasa RDI
In Section SECREF5 , we discuss the resulting data collection of questions, texts and answers via crowdsourcing on Amazon Mechanical Turk (henceforth MTurk). 
 Question: what crowdsourcing platform was used?
Output: Amazon Mechanical Turk
We constructed the ensembled predictions by choosing the answer from the network that had the highest probability and choosing no answer if any of the networks predicted no answer. 
 Question: What ensemble methods are used for best model?
Output: choosing the answer from the network that had the highest probability and choosing no answer if any of the networks predicted no answer
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%. 
 Question: How did they do compared to other teams?
Output: second on Subtask A with an F1 score of 77.78% among 33 other team submissions performs well on Subtask B with an F1 score of 79.59%
Practical evaluation of GTD is currently only possible on synthetic data. We construct a range of datasets designed for image captioning evaluation. We call this diagnostic evaluation benchmark ShapeWorldICE (ShapeWorld for Image Captioning Evaluation). We illustrate the evaluation of specific image captioning models on ShapeWorldICE. 
 Question: Are the images from a specific domain?
Output: Yes
We represent each instance of image, question, and user choice as a triplet consisting of image feature, question feature, and the label vector for the user's answer. In addition, collecting multiple choices from identical users enables us to represent any two instances by the same user as a pair of triplets, assuming source-target relation. With these pairs of triplets, we can train the system to predict a user's choice on a new image and a new question, given the same user's choice on the previous image and its associated question. As discussed earlier, we attempt to reflect user's interest by asking questions that provide visual context. In other words, a question whose answer is so obvious that it can be answered in an identical way would not be valid as an interactive question.   If the most likely candidate $c_i=\max p_{ans}$ , where $c_i \in C$ , has a probability of being answer over a certain threshold $\alpha $ , then the question is considered to have a single obvious answer, and is thus considered ineligible.  In our experiments, we set $\alpha $ as 0.33. We also excluded the yes/no type of questions.  
 Question: What are the features of used to customize target user interaction? 
Output: image feature question feature label vector for the user's answer
Although the candidates agreed to the use of their interviews, the dataset will not be released to public outside of the scope of this study due to the videos being personal data subject to high privacy constraints. 
 Question: Have the candidates given their consent to have their videos used for the research?
Output: Yes
The key idea behind our proposed interactive MRC (iMRC) is to restrict the document context that a model observes at one time. Concretely, we split a supporting document into its component sentences and withhold these sentences from the model. Given a question, the model must issue commands to observe sentences in the withheld set; we equip models with actions such as Ctrl+F (search for token) and stop for searching through partially observed documents. A model searches iteratively, conditioning each command on the input question and the sentences it has observed previously. Thus, our task requires models to `feed themselves' rather than spoon-feeding them with information. This casts MRC as a sequential decision-making problem amenable to reinforcement learning (RL). 
 Question: Do they provide decision sequences as supervision while training models?
Output: No
To create our training dataset, we followed an approach similar to LASER. The dataset contains 6 languages: English, Spanish, German, Dutch, Korean and Chinese Mandarin. The dataset was created by using translations provided by Tatoeba and OpenSubtitles BIBREF16. 
 Question: Which corpus do they use?
Output: The dataset was created by using translations provided by Tatoeba and OpenSubtitles BIBREF16.
We use the dropout technique of Gal & Ghahramani gal as a baseline because it is the most similar dropout technique to our approach and denote it VBD (variational binary dropout). 
 Question: What is binary variational dropout?
Output: the dropout technique of Gal & Ghahramani gal
Experiments ::: Results ::: Natural Language Inference: XNLI
On the XNLI benchmark, CamemBERT obtains improved performance over multilingual language models on the TRANSLATE-TRAIN setting (81.2 vs. 80.2 for XLM) while using less than half the parameters (110M vs. 250M). However, its performance still lags behind models trained on the original English training set in the TRANSLATE-TEST setting, 81.2 vs. 82.91 for RoBERTa. It should be noted that CamemBERT uses far fewer parameters than RoBERTa (110M vs. 355M parameters). 
 Question: Which tasks does CamemBERT not improve on?
Output: its performance still lags behind models trained on the original English training set in the TRANSLATE-TEST setting, 81.2 vs. 82.91 for RoBERTa
We evaluate our proposed approach for joint sentiment and emotion analysis on the benchmark dataset of SemEval 2016 Task 6 BIBREF7 and Stance Sentiment Emotion Corpus (SSEC) BIBREF15. 
 Question: What are the datasets used for training?
Output: SemEval 2016 Task 6 BIBREF7 Stance Sentiment Emotion Corpus (SSEC) BIBREF15
In this study, we focused on the LDA approaches to recommendation systems and given the importance of research, we have studied recent impressive articles on this subject and presented a taxonomy of recommendation systems based on LDA of the recent research. we evaluated ISWC and WWW conferences articles from DBLP website and used the Gibbs sampling algorithm as an evaluation parameter. We succeeded in discovering the relationship between LDA topics and paper features and also obtained the researchers' interest in research field. To perform approximate inference and learning LDA, there are many inference methods for LDA topic model such as Gibbs sampling, collapsed Variational Bayes, Expectation Maximization. Gibbs sampling is a popular technique because of its simplicity and low latency. However, for large numbers of topics, Gibbs sampling can become unwieldy. In this paper, we use Gibbs Sampling in our experiment in section 5. 
 Question: How they utilize LDA and Gibbs sampling to evaluate ISWC and WWW publications?
Output: the LDA approaches to recommendation systems and given the importance of research, we have studied recent impressive articles on this subject and presented a taxonomy of recommendation systems based on LDA of the recent research we evaluated ISWC and WWW conferences articles from DBLP website and used the Gibbs sampling algorithm as an evaluation parameter
The E2E NLG challenge dataset BIBREF21 is utilized in our experiments, which is a crowd-sourced dataset of 50k instances in the restaurant domain. Our models are trained on the official training set and verified on the official testing set.  
 Question: What datasets did they use?
Output: The E2E NLG challenge dataset BIBREF21
For instance, question and aux are correlated because main-clause questions in English require subject-aux inversion and in many cases the insertion of auxiliary do (Do lions meow?).  Expletives, or “dummy” arguments, are semantically inert arguments. The most common expletives in English are it and there, although not all occurrences of these items are expletives. 
 Question: Do they report results only on English data?
Output: Yes
BIBREF17: The Lemming model is a log-linear model that performs joint morphological tagging and lemmatization.  
 Question: What were the non-neural baselines used for the task?
Output: The Lemming model in BIBREF17
Our definition of bias is now: [Interpretive Bias] An interpretive bias in an epistemic ME game is the probability distribution over types given by the belief function of the conversationalists or players, or the Jury. Note that in an ME game there are typically several interpretive biases at work: each player has her own bias, as does the Jury. 
 Question: Which interpretative biases are analyzed in this paper?
Output: in an ME game there are typically several interpretive biases at work: each player has her own bias, as does the Jury
We feed these representations into a clustering algorithm that produces exactly eleven clusters of five cities each and evaluate them against both Calvino's original labels and crowdsourced human judgments. While the overall correlation with Calvino's labels is low, both computers and humans can reliably identify some thematic groups associated with concrete objects. 
 Question: How do they obtain human judgements?
Output: Using crowdsourcing
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.) 
 Question: Do they report results only on English data?
Output: Yes
We also applied our model to MCTest dataset which requires machines to answer multiple-choice reading comprehension questions about fictional stories.  
 Question: Do they experiment with their proposed model on any other dataset other than MovieQA?
Output: Yes
On another note, we apply our formalization to evaluate multilingual $\textsc {bert} $'s syntax knowledge on a set of six typologically diverse languages. Although it does encode a large amount of information about syntax (more than $81\%$ in all languages), it only encodes at most $5\%$ more information than some trivial baseline knowledge (a type-level representation). This indicates that the task of POS labeling (word-level POS tagging) is not an ideal task for contemplating the syntactic understanding of contextual word embeddings. We see that—in all analysed languages—type level embeddings can already capture most of the uncertainty in POS tagging. We also see that BERT only shares a small amount of extra information with the task, having small (or even negative) gains in all languages. Finally, when put into perspective, multilingual $\textsc {bert} $'s representations do not seem to encode much more information about syntax than a trivial baseline. $\textsc {bert} $ only improves upon fastText in three of the six analysed languages—and even in those, it encodes at most (in English) $5\%$ additional information. 
 Question: Was any variation in results observed based on language typology?
Output: It is observed some variability - but not significant. Bert does not seem to gain much more syntax information than with type level information.
We evaluate our model on a publicly available dataset, KARA ONE BIBREF17 , composed of multimodal data for stimulus-based, imagined and articulated speech state corresponding to 7 phonemic/syllabic ( /iy/, /piy/, /tiy/, /diy/, /uw/, /m/, /n/ ) as well as 4 words(pat, pot, knew and gnaw). The dataset consists of 14 participants, with each prompt presented 11 times to each individual.  
 Question: What data was presented to the subjects to elicit event-related responses?
Output: 7 phonemic/syllabic ( /iy/, /piy/, /tiy/, /diy/, /uw/, /m/, /n/ ) as well as 4 words(pat, pot, knew and gnaw)
We then proceed to connect these mentions i) if they co-occur within the same document (we will refer to this as DOC-BASED edges), ii) if the pair of named entity mentions is identical (MATCH edges—these may connect nodes across and within documents), or iii) if they are in the same coreference chain, as predicted by the external coreference system (COREF edges). 
 Question: Did they use a relation extraction method to construct the edges in the graph?
Output: No
For evaluation of clustering performance, we use Calinski-Harabasz score BIBREF42 , also known as the variance ratio criterion. CH score is defined as the ratio between the within-cluster dispersion and the between-cluster dispersion. CH score has a range of $[0, +\infty ]$ and a higher CH score corresponds to a better clustering.  For visual validation, we plot and inspect the t-Distributed Stochastic Neighbor Embedding (t-SNE) BIBREF52 and Uniform Manifold Approximation and Projection (UMAP) BIBREF53 mappings of the learned representations as well. 
 Question: How do they evaluate their method?
Output: Calinski-Harabasz score t-SNE UMAP
For instance, BIBREF16 and BIBREF17 showed that a hypothesis-only baseline performs better than chance due to cues on their lexical choice and sentence length. Similarly, BIBREF18 showed that NLI models tend to predict entailment for sentence pairs with a high lexical overlap. 
 Question: What are examples of these artifacts?
Output: hypothesis-only baseline performs better than chance due to cues on their lexical choice and sentence length NLI models tend to predict entailment for sentence pairs with a high lexical overlap
In the second round, we collected 293 annotations from 12 annotators. After Korektor, there are 4262 unique sentences (including 150 seed sentences) that form the COSTRA 1.0 dataset. 
 Question: How are possible sentence transformations represented in dataset, as new sentences?
Output: Yes, as new sentences.
Following these methods, we evaluate NCEL on the following five datasets: (1) CoNLL-YAGO BIBREF22 : the CoNLL 2003 shared task including testa of 4791 mentions in 216 documents, and testb of 4485 mentions in 213 documents. (2) TAC2010 BIBREF39 : constructed for the Text Analysis Conference that comprises 676 mentions in 352 documents for testing. (3) ACE2004 BIBREF23 : a subset of ACE2004 co-reference documents including 248 mentions in 35 documents, which is annotated by Amazon Mechanical Turk. (4) AQUAINT BIBREF40 : 50 news articles including 699 mentions from three different news agencies. (5) WW BIBREF19 : a new benchmark with balanced prior distributions of mentions, leading to a hard case of disambiguation. It has 6374 mentions in 310 documents automatically extracted from Wikipedia. 
 Question: Which datasets do they use?
Output: CoNLL-YAGO TAC2010 ACE2004 AQUAINT WW
For automatically evaluating our methods, we propose to use widely used metric for image/video captioning. This is because the proposed CommonGen task can be regarded as also a caption task where the context are incomplete scenes with given concept-sets. Therefore, we choose BLEU-3/4 BIBREF28, ROUGE-2/L BIBREF29, CIDEr BIBREF30, and SPICE BIBREF31 as the main metrics. Apart from these classic metrics, we also include a novel embedding-based metric named BERTScore BIBREF32. To make the comparisons more clear, we show the delta of BERTScore results by subtracting the score of merely using input concept-sets as target sentences, named $\triangle $BERTS. 
 Question: What automatic metrics are used for this task?
Output: BLEU-3/4 ROUGE-2/L CIDEr SPICE BERTScore
In both cases, we denote with the word model one of the possible combinations of classic/statistical LSA and a classifier. The used classifiers are Support Vector Machine (SVM), Logistic regression (Log.Reg), Random Forest (RF) and gradient boosting (XGB). Where applicable, we compare our results with existing results in the literature. 
 Question: What are the different methods used for different corpora?
Output: Support Vector Machine (SVM), Logistic regression (Log.Reg), Random Forest (RF) and gradient boosting (XGB)
SMT techniques require a parallel corpus in thr source and thr target language. A text-code parallel corpus similar to Fig. FIGREF12 is used in training. This parallel corpus has 18805 aligned data in it . In source data, the expression of each line code is written in the English language. In target data, the code is written in Python programming language. 
 Question: What dataset do they use?
Output: A parallel corpus where the source is an English expression of code and the target is Python code.
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 . 
 Question: Which GAN do they use?
Output: We construct a GAN model which combines different sets of word embeddings INLINEFORM4 , INLINEFORM5 , into a single set of word embeddings INLINEFORM6 .
We collected tweets related to five different DDoS attacks on three different American banks. For each attack, all the tweets containing the bank's name posted from one week before the attack until the attack day were collected. There are in total 35214 tweets in the dataset. Only the tweets from the Bank of America attack on 09/19/2012 were used in this experiment. In this subsection we evaluate how good the model generalizes. To achieve that, the dataset is divided into two groups, one is about the attacks on Bank of America and the other group is about PNC and Wells Fargo. The only difference between this experiment and the experiment in section 4.4 is the dataset. In this experiment setting $D_a$ contains only the tweets collected on the days of attack on PNC and Wells Fargo. $D_b$ only contains the tweets collected before the Bank of America attack. 
 Question: What is the training and test data used?
Output: Tweets related to a Bank of America DDos attack were used as training data. The test datasets contain tweets related to attacks to Bank of America, PNC and Wells Fargo.
The task of attribute embedding lays is embedding every value in attribute triples into a continuous vector space while preserving the semantic information. To capture both high-order structural information of KGs, we used an attention-based embedding propagation method. The final embedding of entities, relations and values are feed into two different deep neural network for two different tasks including link predication and entity classification. 
 Question: How does KANE capture both high-order structural and attribute information of KGs in an efficient, explicit and unified manner?
Output: To capture both high-order structural information of KGs, we used an attention-based embedding propagation method.
As shown in Figure FIGREF3 , our model contains two key components, namely Truncated History-Attention (THA) and Selective Transformation Network (STN), for capturing aspect detection history and opinion summary respectively. THA and STN are built on two LSTMs that generate the initial word representations for the primary ATE task and the auxiliary opinion detection task respectively. THA is designed to integrate the information of aspect detection history into the current aspect feature to generate a new history-aware aspect representation. STN first calculates a new opinion representation conditioned on the current aspect candidate. Then, we employ a bi-linear attention network to calculate the opinion summary as the weighted sum of the new opinion representations, according to their associations with the current aspect representation. Finally, the history-aware aspect representation and the opinion summary are concatenated as features for aspect prediction of the current time step. 
 Question: How do they determine the opinion summary?
Output: the weighted sum of the new opinion representations, according to their associations with the current aspect representation
Inspired by BIBREF12, we integrate in this paper a boundary assembling step into the state-of-the-art LSTM model for Chinese word segmentation, and feed the output into a CRF model for NER, resulting in a 2% absolute improvement on the overall F1 score over current state-of-the-art methods. 
 Question: What state-of-the-art deep neural network is used?
Output: LSTM model
The median age is 17 for depressed class versus 19 for control class suggesting either likely depressed-user population is younger, or depressed youngsters are more likely to disclose their age for connecting to their peers (social homophily.) Our findings are consistent with the medical literature BIBREF10 as according to BIBREF52 more women than men were given a diagnosis of depression. 
 Question: What insights into the relationship between demographics and mental health are provided?
Output: either likely depressed-user population is younger, or depressed youngsters are more likely to disclose their age more women than men were given a diagnosis of depression
In this paper we propose two neural embedding models in order to learn continuous concept vectors based on the skip-gram model BIBREF11 . Our first model is the Concept Raw Context model (CRC) which utilizes concept mentions in a large scale KB to jointly learn embeddings of both words and concepts. Our second model is the Concept-Concept Context model (3C) which learns the embeddings of concepts from their conceptual contexts (i.e., contexts containing surrounding concepts only).  
 Question: What are the two neural embedding models?
Output: Our first model is the Concept Raw Context model (CRC) which utilizes concept mentions in a large scale KB to jointly learn embeddings of both words and concepts. Our second model is the Concept-Concept Context model (3C) which learns the embeddings of concepts from their conceptual contexts (i.e., contexts containing surrounding concepts only)
In this paper, we introduce a novel policy model to output multiple actions per turn (called multi-act), generating a sequence of tuples and expanding agents' expressive power. Each tuple is defined as $(\textit {continue}, \textit {act}, \textit {slots})$, where continue indicates whether to continue or stop producing new acts, act is an act type (e.g., inform or request), and slots is a set of slots (names) associated with the current act type. Correspondingly, a novel decoder (Figure FIGREF5) is proposed to produce such sequences. Each tuple is generated by a cell called gated Continue Act Slots (gCAS, as in Figure FIGREF7), which is composed of three sequentially connected gated units handling the three components of the tuple. This decoder can generate multi-acts in a double recurrent manner BIBREF18.  
 Question: What is specific to gCAS cell?
Output: It has three sequentially connected units to output continue, act and slots generating multi-acts in a doble recurrent manner.
The UNGA speeches dataset, compiled by Baturo et al. UNGAspeeches, contains the text from 7,507 speeches given between 1970-2015 inclusive. 
 Question: how many speeches are in the dataset?
Output: 7,507
For the low resource translation task, we used the BTEC corpus as the training data, which consists of 30k sentence pairs with 0.27M Chinese words and 0.33M English words. As development and test sets, we used the CSTAR03 and IWSLT04 held out sets, respectively.  We used the data from the NIST2008 Open Machine Translation Campaign. The training data consisted of 1.8M sentence pairs, the development set was nist02 (878 sentences), and the test sets are were nist05 (1082 sentences), nist06 (1664 sentences) and nist08 (1357 sentences). 
 Question: Which dataset do they use?
Output: BTEC corpus the CSTAR03 and IWSLT04 held out sets the NIST2008 Open Machine Translation Campaign
We conduct experiments on WikiSQL BIBREF8 , which provides 87,726 annotated question-SQL pairs over 26,375 web tables. We do our study on SimpleQuestions BIBREF10 , which includes 108,442 simple questions, each of which is accompanied by a subject-relation-object triple. We conduct experiments on SequentialQA BIBREF9 which is derived from the WikiTableQuestions dataset BIBREF19 . 
 Question: What datasets are used in this paper?
Output: WikiSQL SimpleQuestions SequentialQA
LiLi should have the following capabilities: 
 Question: What are the components of the general knowledge learning engine?
Output: Answer with content missing: (list)
LiLi should have the following capabilities:
1. to formulate an inference strategy for a given query that embeds processing and interactive actions.
2. to learn interaction behaviors (deciding what to ask and when to ask the user).
3. to leverage the acquired knowledge in the current and future inference process.
4. to perform 1, 2 and 3 in a lifelong manner for continuous knowledge learning.
We carried out our experiments on the Universal Dependencies v1.2 treebanks BIBREF21 , hereafter UD1.2, from which morphosyntactically annotated corpora can be trivially extracted.  We considered UD1.2 corpora for the following 16 languages: Bulgarian, Croatian, Czech, Danish, English, French, German, Indonesian, Italian, Norwegian, Persian, Polish, Portuguese, Slovenian, Spanish and Swedish.  
 Question: which datasets did they experiment with?
Output: Universal Dependencies v1.2 treebanks for the following 16 languages: Bulgarian, Croatian, Czech, Danish, English, French, German,
Indonesian, Italian, Norwegian, Persian, Polish, Portuguese, Slovenian, Spanish, and Swedish
Moreover, because English does not mark grammatical gender, approaches developed for English are not transferable to morphologically rich languages that exhibit gender agreement BIBREF8 . 
 Question: Why does not the approach from English work on other languages?
Output: Because, unlike other languages, English does not mark grammatical genders
In this subsection, we see the influence of each component of a model on performance by removing or replacing its components. the SNLI dataset is used for experiments, and the best performing configuration is used as a baseline for modifications. We consider the following variants: (i) models that use plain stacked LSTMs, (ii) models with different INLINEFORM0 , (iii) models without INLINEFORM1 , and (iv) models that integrate lower contexts via peephole connections. 
 Question: What were the baselines?
Output: (i) models that use plain stacked LSTMs (ii) models with different INLINEFORM0 (iii) models without INLINEFORM1 (iv) models that integrate lower contexts via peephole connections