niryuu/sni-each-converted huggingface dataset:
Level B categorizes the type of offense and two labels are used: targeted (TIN) and untargeted (INT) insults and threats.

Targeted Insult (TIN): Posts which contain an insult/threat to an individual, group, or others (see next layer);

Untargeted (UNT): Posts containing non-targeted profanity and swearing. Posts with general profanity are not targeted, but they contain non-acceptable language. Level C categorizes the targets of insults and threats as individual (IND), group (GRP), and other (OTH).

Individual (IND): Posts targeting an individual. It can be a a famous person, a named individual or an unnamed participant in the conversation. Insults and threats targeted at individuals are often defined as cyberbulling.

Group (GRP): The target of these offensive posts is a group of people considered as a unity due to the same ethnicity, gender or sexual orientation, political affiliation, religious belief, or other common characteristic. Many of the insults and threats targeted at a group correspond to what is commonly understood as hate speech.

Other (OTH): The target of these offensive posts does not belong to any of the previous two categories (e.g. an organization, a situation, an event, or an issue). 
 Question: What kinds of offensive content are explored?
Output: non-targeted profanity and swearing, targeted insults such as cyberbullying, offensive content related to ethnicity, gender or sexual orientation, political affiliation, religious belief, and anything belonging to hate speech
We cluster the embeddings with INLINEFORM0 -Means.  
 Question: How were the cluster extracted? 
Output: Word clusters are extracted using k-means on word embeddings
As the gold standard sentiment lexica, we chose manually created lexicon in Czech BIBREF11 , German BIBREF12 , French BIBREF13 , Macedonian BIBREF14 , and Spanish BIBREF15 . 
 Question: what sentiment sources do they compare with?
Output: manually created lexicon in Czech BIBREF11 , German BIBREF12 , French BIBREF13 , Macedonian BIBREF14 , and Spanish BIBREF15
Among the GLUE tasks, these state-of-the-art systems make their greatest gains on the acceptability task with the Corpus of Linguistic Acceptability BIBREF0 . CoLA contains example sentences from linguistics publications labeled by experts for grammatical acceptability, and written to show subtle grammatical features. Because minimal syntactic differences can separate acceptable sentences from unacceptable ones (What did Bo write a book about? / *What was a book about written by Bo?), and acceptability classifiers are more reliable when trained on GPT and BERT than on recurrent models, it stands to reason that GPT and BERT have better implicit knowledge of syntactic features relevant to acceptability. 
 Question: How is the CoLA grammatically annotated?
Output: labeled by experts
In CBOW architecture the task is predicting the word given its context and in SG the task in predicting the context given the word. We built 16 models of word embeddings using the implementation of CBOW and Skip-gram methods in the FastText tool BIBREF9 . 
 Question: What is specific about the specific embeddings?
Output: predicting the word given its context
Based on the gained insights, we have improved the state-of-the-art in XNLI for the Translate-Test and Zero-Shot approaches by a substantial margin. 
 Question: Is the improvement over state-of-the-art statistically significant?
Output: Yes
Inspired by the line of work reported in these studies, we propose to use modified objective functions for a different purpose: learning more interpretable dense word embeddings. By doing this, we aim to incorporate semantic information from an external lexical resource into the word embedding so that the embedding dimensions are aligned along predefined concepts. This alignment is achieved by introducing a modification to the embedding learning process. In our proposed method, which is built on top of the GloVe algorithm BIBREF2 , the cost function for any one of the words of concept word-groups is modified by the introduction of an additive term to the cost function.  
 Question: What is the additive modification to the objective function?
Output: The cost function for any one of the words of concept word-groups is modified by the introduction of an additive term to the cost function. . Each embedding vector dimension is first associated with a concept. For a word belonging to any one of the word-groups representing these concepts, the modified cost term favors an increase for the value of this word's embedding vector dimension corresponding to the concept that the particular word belongs to,
Our end-to-end method reads text, and writes to both memory slots and edges between them. Intuitively, the memory slots correspond to entities and the edges correspond to relationships between entities, each represented as a vector. 
 Question: How is knowledge retrieved in the memory?
Output: the memory slots correspond to entities and the edges correspond to relationships between entities, each represented as a vector.
In this section, we first describe a baseline method inspired by the “align to segment” of BIBREF12, BIBREF13. We then propose two extensions providing the model with a signal relevant to the segmentation process, so as to move towards a joint learning of segmentation and alignment. 
 Question: Does the paper report any alignment-only baseline?
Output: Yes
In task 1, there are two top categories, namely, chit-chat and task-oriented dialogue. 
 Question: How many intents were classified?
Output: two
Generally, adversarial training improves the accuracy across all languages, and the improvement is sometimes dramatic versus the BERT non-adversarial baseline. When combined with adversarial learning, the BERT cross-lingual F1 scores increased for German over the non-adversarial baseline, and the scores remained largely the same for Spanish and Dutch. 
 Question: Does adversarial learning have stronger performance gains for text classification, or for NER?
Output: classification
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. They fall into two categories: topic classification (Yahoo) and fine-grained sentiment analysis (Amazon, Yelp). 
 Question: What are the datasets used
Output: large-scale document classification datasets introduced by BIBREF14
For the MCTest dataset, Fig. FIGREF30 compares our proposed models with the current state-of-art ensemble of hand-crafted syntactic and frame-semantic features BIBREF16 , as well as past neural models from the literature, all using attention mechanisms — the Attentive Reader of BIBREF26 , Neural Reasoner of BIBREF27 and the HABCNN model family of BIBREF17 .  
 Question: what is the state of the art for ranking mc test answers?
Output: ensemble of hand-crafted syntactic and frame-semantic features BIBREF16
We demonstrate our approach on the task of answering open-domain fill-in-the-blank natural language questions. 
 Question: What task do they evaluate on?
Output: Fill-in-the-blank natural language questions
In this section, we evaluate the proposed method intrinsically in terms of whether the co-occurrence matrix after the low-rank approximation is able to capture similar concepts on student response data sets, and also extrinsically in terms of the end task of summarization on all corpora. In the following experiments, summary length is set to be the average number of words in human summaries or less. An alternative way to evaluate the hypothesis is to let humans judge whether two bigrams are similar or not, which we leave for future work. 
 Question: Do they quantitavely or qualitatively evalute the output of their low-rank approximation to verify the grouping of lexical items?
Output: They evaluate quantitatively.
We used four classification algorithms: 1) Logistic regression, which is conventionally used in sentiment classification. Other three algorithms which are relatively new and has shown great results on sentiment classification types of problems are: 2) Naïve Bayes with SVM (NBSVM), 3) Extreme Gradient Boosting (XGBoost) and 4) FastText algorithm with Bidirectional LSTM (FastText-BiLSTM). 
 Question: What state of the art models are used in the experiments?
Output: 2) Naïve Bayes with SVM (NBSVM) 3) Extreme Gradient Boosting (XGBoost) 4) FastText algorithm with Bidirectional LSTM (FastText-BiLSTM)
Similarly the agents utterances can be clustered to identify system responses. However, we argue that rather than treating user utterances and agents responses in an isolated manner, there is merit in jointly clustering them. There is adjacency information of these utterances that can be utilized to identify better user intents and system responses. 
 Question: Do they study frequent user responses to help automate modelling of those?
Output: Yes
We collected Japanese-Vietnamese parallel data from TED talks extracted from WIT3's corpus BIBREF15 .  
 Question: what japanese-vietnamese dataset do they use?
Output: WIT3's corpus
. We could observe that compared with the base model, we have an improvement of 7.36% on accuracy and 9.69% on F1 score.  
 Question: How big are improvements of small-scale unbalanced datasets when sentence representation is enhanced with topic information?
Output: 7.36% on accuracy and 9.69% on F1 score
For evaluation metrics, we used BLEU BIBREF8 and RIBES BIBREF9 to measure translation accuracy, and token-level delay to measure latency. We used Kytea BIBREF10 as a tokenize method for evaluations of Japanese translation accuracy. 
 Question: Which metrics do they use to evaluate simultaneous translation?
Output: BLEU BIBREF8 RIBES BIBREF9 token-level delay
Task 1: Quora Duplicate Question Pair Detection Task 2: Ranking questions in Bing's People Also Ask 
 Question: On which tasks do they test their conflict method?
Output: Task 1: Quora Duplicate Question Pair Detection Task 2: Ranking questions
The first type of classifier we evaluate in this work are feedforward neural networks (DNNs) consisting of 3 hidden layers, each with 512 rectified linear units (ReLUs) with a softmax activation function. As a second classifier to evaluate, we use convolutional neural networks (CNNs) with 2 convolutional and max pooling layers, followed by 2 fully-connected ReLU layers with 512 nodes. 
 Question: What type of models are used for classification?
Output: feedforward neural networks (DNNs) convolutional neural networks (CNNs)
The DDI corpus contains thousands of XML files, each of which are constructed by several records. For a sentence containing INLINEFORM0 drugs, there are INLINEFORM1 drug pairs. 
 Question: How big is the evaluated dataset?
Output: contains thousands of XML files, each of which are constructed by several records
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". 
 Question: What is the source of the paraphrases of the questions?
Output: WikiTableQuestions
First, we compare its performance with other BERT-models and state-of-the-art systems in sentiment analysis, to show its performance for classification tasks.  Second, we compare its performance in a recent Dutch language task, namely the disambiguation of demonstrative pronouns, which allows us to additionally compare the zero-shot performance of our and other BERT models, i.e. using only the pre-trained model without any fine-tuning. 
 Question: What language tasks did they experiment on?
Output: sentiment analysis the disambiguation of demonstrative pronouns,
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.  
 Question: What is the difference in findings of Buck et al? It looks like the same conclusion was mentioned in Buck et al..
Output: AQA diverges from well structured language in favour of less fluent, but more effective, classic information retrieval (IR) query operations
Besides, we also use two of its adversarial sets, namely AddSent and AddOneSent BIBREF6 , to evaluate the robustness to noise of MRC models. The passages in the adversarial sets contain misleading sentences, which are aimed at distracting MRC models.  Specifically, we not only evaluate the performance of KAR on the development set and the test set, but also do this on the adversarial sets.  
 Question: How do the authors examine whether a model is robust to noise or not?
Output: By evaluating their model on adversarial sets containing misleading sentences
In test batch 4, our system (called FACTOIDS) achieved highest recall score of ‘0.7033’ but low precision of 0.1119, leaving open the question of how could we have better balanced the two measures. 
 Question: What was their highest recall score?
Output: 0.7033
Embedding: We developed different variations of our models with a simple lookup table embeddings learned from scratch and using high-performance contextual embeddings, which are ELMo BIBREF11, BERT BIBREF16 and ClinicalBERT BIBREF13 (trained and provided by the authors). 
 Question: What embeddings are used?
Output:  simple lookup table embeddings learned from scratch using high-performance contextual embeddings, which are ELMo BIBREF11, BERT BIBREF16 and ClinicalBERT BIBREF13
With this approach we improve the standard BERT models by up to four percentage points in accuracy. 
 Question: By how much do they outperform standard BERT?
Output: up to four percentage points in accuracy
Of the 144 schemas in the collection at WinogradSchemas there are 33 that can plausibly be translated this way. 
 Question: Did they collect their own datasets?
Output: No
DNN based models coupled with transfer learning beat the best-known results for all three datasets. Previous best F1 scores for Wikipedia BIBREF4 and Twitter BIBREF8 datasets were 0.68 and 0.93 respectively. We achieve F1 scores of 0.94 for both these datasets using BLSTM with attention and feature level transfer learning (Table TABREF25 ). For Formspring dataset, authors have not reported F1 score. Their method has accuracy score of 78.5% BIBREF2 . We achieve F1 score of 0.95 with accuracy score of 98% for the same dataset. 
 Question: What were their performance results?
Output: best model achieves 0.94 F1 score for Wikipedia and Twitter datasets and 0.95 F1 on Formspring dataset
Hate memes were retrieved from Google Images with a downloading tool. We used the following queries to collect a total of 1,695 hate memes: racist meme (643 memes), jew meme (551 memes), and muslim meme (501 Memes). Non-hate memes were obtained from the Reddit Memes Dataset . 
 Question: What is the source of memes?
Output: Google Images Reddit Memes Dataset
The baseline classifier uses a linear Support Vector Machine BIBREF7 , which is suited for a high number of features.  
 Question: What baseline is used?
Output: SVM
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 ($55.98\%$). 
 Question: How better are results compared to baseline models?
Output: F1 score of best authors' model is 55.98 compared to BiLSTM and FastText that have F1 score slighlty higher than 46.61.
This version highly surpasses the previous state of the art while still having fewer parameters than previous work. 
 Question: Do they compare results against state-of-the-art language models?
Output: Yes
Table TABREF16 shows the recognition performance of naive multilingual approach using BLSTMP and VGG model against a monolingual model trained with BLSTMP. The results clearly indicate that having a better architecture such as VGG-BLSTM helps in improving multilingual performance. We used a character-level RNNLM, which was trained with 2-layer LSTM on character sequences. 
 Question: What architectures are explored to improve the seq2seq model?
Output: VGG-BLSTM character-level RNNLM
In this method, we simply concatenate the corpora of multiple domains with two small modifications: a. Appending the domain tag “<2domain>" to the source sentences of the respective corpora.  
 Question: How did they use the domain tags?
Output: Appending the domain tag “<2domain>" to the source sentences of the respective corpora
To fine-tune BERT, we used a proprietary corpus that consists of hundreds of thousands of legal agreements. 
 Question: How big is dataset used for fine-tuning BERT?
Output: hundreds of thousands of legal agreements
However, as shown in Figure 1 , to assess the similarity between two research papers, a more effective strategy would compare and align (via local-weighting) individual important words (keywords) within a pair of abstracts, while information from other words (e.g., stop words) that tend to be less relevant can be effectively ignored (down-weighted).  We propose to learn a semantic-aware Network Embedding (NE) that incorporates word-level alignment features abstracted from text sequences associated with vertex pairs. Given a pair of sentences, our model first aligns each word within one sentence with keywords from the other sentence (adaptively up-weighted via an attention mechanism), producing a set of fine-grained matching vectors 
 Question: What text sequences are associated with each vertex?
Output: abstracts sentences
RNN-based NMT model
We first briefly introduce the RNN based Neural Machine Translation (RNN-based NMT) model. Transformer-NMT
Recently, the Transformer model BIBREF4 has made remarkable progress in machine translation. This model contains a multi-head self-attention encoder and a multi-head self-attention decoder. 
 Question: what NMT models did they compare with?
Output: RNN-based NMT model Transformer-NMT
Given raw audio samples INLINEFORM0 , we apply the encoder network INLINEFORM1 which we parameterize as a five-layer convolutional network similar to BIBREF15 . Next, we apply the context network INLINEFORM0 to the output of the encoder network to mix multiple latent representations INLINEFORM1 into a single contextualized tensor INLINEFORM2 for a receptive field size INLINEFORM3 . The context network has seven layers and each layer has kernel size three and stride one.  
 Question: How many convolutional layers does their model have?
Output: wav2vec has 12 convolutional layers
image feature pre-selection part which models the tendency where people focus to ask questions We propose to perform saliency-like pre-selection operation to alleviate the problems and model the RoI patterns. The image is first divided into $g\times g$ grids as illustrated in Figure. 2 . Taking $m\times m$ grids as a region, with $s$ grids as the stride, we obtain $n\times n$ regions, where $n=\left\lfloor \frac{g-m}{s}\right\rfloor +1$ . We then feed the regions to a pre-trained ResNet BIBREF24 deep convolutional neural network to produce $n\times n\times d_I$ -dimensional region features, where $d_I$ is the dimension of feature from the layer before the last fully-connected layer. 
 Question: Does the new system utilize pre-extracted bounding boxes and/or features?
Output: Yes
We use three creative English datasets with distinct linguistic characteristics: (1) A corpus of 740 classical and contemporary English poems, (2) a corpus of 14950 metaphor sentences retrieved from a metaphor database website and (3) a corpus of 1500 song lyrics ranging across genres.  
 Question: Do they report results only on English data?
Output: Yes
We test our proposed approach for binary classification on either sarcasm or irony, on seven benchmark datasets retrieved from different media sources. Below we describe each dataset, please see Table TABREF1 below for a summary. Twitter: We use the Twitter dataset provided for the SemEval 2018 Task 3, Irony Detection in English Tweets BIBREF18  Finally, we use the dataset by BIBREF20 , who collected a user self-annotated corpus of tweets with the #sarcasm hashtag. Reddit: BIBREF21 collected SARC, a corpus comprising of 600.000 sarcastic comments on Reddit. We use main subset, SARC 2.0, and the political subset, SARC 2.0 pol. Online Dialogues: We utilize the Sarcasm Corpus V1 (SC-V1) and the Sarcasm Corpus V2 (SC-V2), which are subsets of the Internet Argument Corpus (IAC). Compared to other datasets in our selection, these differ mainly in text length and structure complexity BIBREF22 .  To augment each dataset with our external data, we first filter out tweets that are not in English using language guessing systems. 
 Question: Do they evaluate only on English?
Output: Yes
In this paper, we propose an improved RNN-T model with language bias to alleviate the problem. The model is trained to predict language IDs as well as the subwords. To ensure the model can learn CS information, we add language IDs in the CS point of transcription, as illustrated in Fig. 1. 
 Question: How do they obtain language identities?
Output: model is trained to predict language IDs as well as the subwords we add language IDs in the CS point of transcriptio
Our in-house dataset includes manually annotated RE data for 6 languages: English, German, Spanish, Italian, Japanese and Portuguese. It defines 56 entity types (e.g., Person, Organization, Geo-Political Entity, Location, Facility, Time, Event_Violence, etc.) and 53 relation types between the entities (e.g., AgentOf, LocatedAt, PartOf, TimeOf, AffectedBy, etc.). The ACE05 dataset includes manually annotated RE data for 3 languages: English, Arabic and Chinese. It defines 7 entity types (Person, Organization, Geo-Political Entity, Location, Facility, Weapon, Vehicle) and 6 relation types between the entities (Agent-Artifact, General-Affiliation, ORG-Affiliation, Part-Whole, Personal-Social, Physical). the ACE (Automatic Content Extraction) 2005 multilingual dataset BIBREF11. 
 Question: What datasets are used?
Output: in-house dataset ACE05 dataset
CNN can also be employed on the sarcasm datasets in order to identify sarcastic and non-sarcastic tweets. We term the features extracted from this network baseline features, the method as baseline method and the CNN architecture used in this baseline method as baseline CNN. Since the fully-connected layer has 100 neurons, we have 100 baseline features in our experiment.  
 Question: What are the network's baseline features?
Output:  The features extracted from CNN.
The reports are published by Météo France and the Met Office, its British counterpart. They are publicly available on the respective websites of the organizations. Both corpora span on the same period as the corresponding time series and given their daily nature, it yields a total of 4,261 and 4,748 documents respectively. 
 Question: How big is dataset used for training/testing?
Output: 4,261  days for France and 4,748 for the UK
For testing, we also applied the TongueNet on the UBC database BIBREF14 without any training to see the generalization ability of the model. 
 Question: What previously annotated databases are available?
Output: the UBC database BIBREF14
Strategy-based methods depart from the pre-training stage, seeking to take advantage of the pre-trained models during the process of target task learning. The approaches include fine-tuning schedules that elaborately design the control of learning rates for optimization, proxy tasks that leverage labeled data to help the pre-trained model better fit the target data distribution, and knowledge distillation approaches that ditch the paradigm of initialization with pre-trained parameters by adopting the pre-trained model as a teacher network. 
 Question: How strategy-based methods handle obstacles in NLG?
Output: fine-tuning schedules that elaborately design the control of learning rates for optimization proxy tasks that leverage labeled data to help the pre-trained model better fit the target data distribution knowledge distillation approaches that ditch the paradigm of initialization with pre-trained parameters by adopting the pre-trained model as a teacher network
We present in this section the baseline model from See et al. See2017 trained on the CNN/Daily Mail dataset. The baseline model is a deep sequence-to-sequence encoder/decoder model with attention. The encoder is a bidirectional Long-Short Term Memory(LSTM) cell BIBREF14 and the decoder a single LSTM cell with attention mechanism. The attention mechanism is computed as in BIBREF9 and we use a greedy search for decoding. We train end-to-end including the words embeddings. The embedding size used is of 128 and the hidden state size of the LSTM cells is of 254. 
 Question: Which baselines did they compare?
Output: The baseline model is a deep sequence-to-sequence encoder/decoder model with attention. The encoder is a bidirectional Long-Short Term Memory(LSTM) cell BIBREF14 and the decoder a single LSTM cell with attention mechanism. The attention mechanism is computed as in BIBREF9 and we use a greedy search for decoding. We train end-to-end including the words embeddings. The embedding size used is of 128 and the hidden state size of the LSTM cells is of 254.
With an intention to exploit the contribution of hashtag use to stance detection, we have also used the existence of hashtags in tweets as an additional feature to unigrams. 
 Question: What are hashtag features?
Output: hashtag features contain whether there is any hashtag in the tweet
Because our approach is specifically intended to yield sentences that are grammatical, we additionally consider the following log ratio (i.e., the grammatical phrase over the ungrammatical phrase): 
 Question: How do they measure grammaticality?
Output: by calculating log ratio of grammatical phrase over ungrammatical phrase
We first conduct experiments to compare the performance of FlowSeq with strong baseline models, including NAT w/ Fertility BIBREF6, NAT-IR BIBREF7, NAT-REG BIBREF25, LV NAR BIBREF26, CTC Loss BIBREF27, and CMLM BIBREF8. 
 Question: What non autoregressive NMT models are used for comparison?
Output: NAT w/ Fertility NAT-IR NAT-REG LV NAR CTC Loss CMLM
We evaluated the proposed model using the publicly available LibriSpeech ASR corpus BIBREF23. The LibriSpeech dataset consists of 970 hours of audio data with corresponding text transcripts (around 10M word tokens) and an additional 800M word token text only dataset. 
 Question: How big is LibriSpeech dataset?
Output: 970 hours of audio data with corresponding text transcripts (around 10M word tokens) and an additional 800M word token text only dataset
We used the Levenshtein distance metric BIBREF8 implemented in Apache Lucene library BIBREF9 . Another simple approach is the aforementioned diacritical swapping, which is a term that we introduce here for referring to a solution inspired by the work of BIBREF4 . A promising method, adapted from work on correcting texts by English language learners BIBREF11 , expands on the concept of selecting a correction nearest to the spelling error according to some notion of distance. Here, the Levenshtein distance is used in a weighted sum to cosine distance between word vectors. Our ELMo-augmented LSTM is bidirectional. 
 Question: What methods are tested in PIEWi?
Output: Levenshtein distance metric BIBREF8 diacritical swapping Levenshtein distance is used in a weighted sum to cosine distance between word vectors ELMo-augmented LSTM
Knowledge Base Question Answering (KBQA) systems answer questions by obtaining information from KB tuples BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 . 
 Question: What is te core component for KBQA?
Output: answer questions by obtaining information from KB tuples
Outlier Detection. The Outlier Detection task BIBREF0 is to determine which word in a list INLINEFORM0 of INLINEFORM1 words is unrelated to the other INLINEFORM2 which were chosen to be related. For each INLINEFORM3 , one can compute its compactness score INLINEFORM4 , which is the compactness of INLINEFORM5 . Sentiment analysis. We also consider sentiment analysis as described by BIBREF31 . 
 Question: Do they test their word embeddings on downstream tasks?
Output: Yes
Other Timex3 mentions are more ambiguous so we use a distant supervision approach. Phrases like “currently" and “today's" tend to occur near Events that overlap the current document creation time, while “ago" or “ INLINEFORM0 -years" indicate past events. These dominant temporal associations can be learned from training data and then used to label Timex3s. Finally, we define a logistic regression rule to predict entity DocRelTime values as well as specify a linear skip-chain factor over Event mentions and their nearest Timex3 neighbor, encoding the baseline system heuristic directly as an inference rule. 
 Question: How do they obtain distant supervision rules for predicting relations?
Output: dominant temporal associations can be learned from training data
We would expect that explanation performance should correlate with prediction performance. Since Possible-answer knowledge is primarily needed to decide if the net has enough information to answer the challenge question without guessing and relevant-variable knowledge is needed for the net to know what to query, we analyzed the network's performance on querying and answering separately. The memory network has particular difficulty learning to query relevant variables, reaching only about .5 accuracy when querying. At the same time, it learns to answer very well, reaching over .9 accuracy there. Since these two parts of the interaction are what we ask it to explain in the two modes, we find that the quality of the explanations strongly correlates with the quality of the algorithm executed by the network. 
 Question: How do they measure correlation between the prediction and explanation quality?
Output: They look at the performance accuracy of explanation and the prediction performance
We address the robustness problem on top of GE-FL BIBREF0 , a GE method which leverages labeled features as prior knowledge. 
 Question: What background knowledge do they leverage?
Output: labeled features
The greatest challenges with the competition arise directly from the potential for ongoing mixed-initiative multi-turn dialogues, which do not follow a particular plan or pursue a particular fixed information need.  This paper describes some of the lessons we learned building SlugBot for the 2017 Alexa Prize, particularly focusing on the challenges of integrating content found via search with content from structured data in order to carry on an ongoing, coherent, open-domain, mixed-initiative conversation More challenging is that at each system turn, there are a large number of conversational moves that are possible.  Finally, most other domains do not have such high quality structured data available, leaving us to develop or try to rely on more general models of discourse coherence.  Search cannot be used effectively here without constructing an appropriate query, or knowing in advance where plot information might be available. In a real-time system, live search may not be able to achieve the required speed and efficiency, so preprocessing or caching of relevant information may be necessary.  
 Question: Why mixed initiative multi-turn dialogs are the greatest challenge in building open-domain conversational agents?
Output: do not follow a particular plan or pursue a particular fixed information need  integrating content found via search with content from structured data at each system turn, there are a large number of conversational moves that are possible most other domains do not have such high quality structured data available live search may not be able to achieve the required speed and efficiency
The result on original WikiQA indicates that all three transfer learning methods not only do not improve the results but also hurt the F1-score. These are because other datasets could not add new information to the original dataset or they apparently add some redundant information which are dissimilar to the target dataset. 
 Question: Do transferring hurt the performance is the corpora are not related?
Output: Yes
We consider 5 major profile attributes for further analysis. These attributes are username, display name, profile image, location and description respectively. 
 Question: What profile metadata is used for this analysis?
Output: username display name profile image location description
In order to evaluate the models trained on generated data, we manually annotated a named entities dataset comprising 53453 tokens and 2566 sentences selected from over 250 news texts from ilur.am. During annotation, we generally relied on categories and guidelines assembled by BBN Technologies for TREC 2002 question answering track. 
 Question: did they use a crowdsourcing platform for manual annotations?
Output: No
Furthermore, the basic seq2seq assumes a strict order between generated tokens, but in reality, we should not severely punish the model when it predicts the correct tokens in the wrong order. 
 Question: Do they impose any grammatical constraints over the generated output?
Output: No
While the dataset was collected with simulated environments, no structure was imposed on the navigation instructions while crowd-sourcing data. Thus, many instructions in our dataset are ambiguous. Moreover, the order of the behaviors in the instructions is not always the same. For instance, a person said “turn right and advance” to describe part of a route, while another person said “go straight after turning right” in a similar situation. The high variability present in the natural language descriptions of our dataset makes the problem of decoding instructions into behaviors not trivial. See Appendix A of the supplementary material for additional details on our data collection effort. 
 Question: What language is the experiment done in?
Output: english language
The crowdworkers were located in the US and hired on the BIBREF22 platform. 
 Question: Who are the crowdworkers?
Output: people in the US that use Amazon Mechanical Turk
TF-IDF (Lexical, Non-Neural) is an important unsupervised baseline. NVDM (Lexical, Neural) is a VAE-based approach for document modeling BIBREF10 . 
 Question: Which lexicon-based models did they compare with?
Output: TF-IDF NVDM
In this paper, we use three encoders (NBOW, LSTM and attentive LSTM) to model the text descriptions. 
 Question: What neural models are used to encode the text?
Output: NBOW, LSTM, attentive LSTM
Ethnicity/race
One interesting pattern is that the ethnicity/race of babies doesn't seem to be mentioned unless the baby is black or asian. In other words: white seems to be the default, and others seem to be marked. How can we tell whether or not the data is actually biased?

 The numbers in Table TABREF22 are striking: there seems to be a real, systematic difference in ethnicity marking between the groups. We can take one step further and look at all the 697 pictures with the word `baby' in it. If there turn out to be disproportionately many white babies, this strengthens the conclusion that the dataset is biased. 
 Question: What biases are found in the dataset?
Output: Ethnic bias
We use MS COCO, Bing and Flickr datasets from BIBREF26 to train the model that generates questions. These datasets contain natural questions about images with the purpose of knowing more about the picture. As can be seen in the Figure FIGREF8, questions cannot be answered by only looking at the image. Each source contains 5,000 images with 5 questions per image, adding a total of 15,000 images with 75,000 questions. 
 Question: How many questions per image on average are available in dataset?
Output: 5 questions per image
The performance on discriminating between offensive (OFF) and non-offensive (NOT) posts is reported in Table TABREF18 . We can see that all systems perform significantly better than chance, with the neural models being substantially better than the SVM. The CNN outperforms the RNN model, achieving a macro-F1 score of 0.80. The CNN system achieved higher performance in this experiment compared to the BiLSTM, with a macro-F1 score of 0.69. All systems performed better at identifying target and threats (TIN) than untargeted offenses (UNT). 
 Question: What is the best performing model?
Output: CNN
For DQA four participants answered each question, therefore we took the average INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 values over the four evaluators as the result per question. The detailed answers by the participants and available online. To assess the correctness of the answers given both by participants in the DQA experiments, and by the QALD system, we use the classic information retrieval metrics of precision (P), recall (R), and F1. INLINEFORM0 measures the fraction of relevant (correct) answer (items) given versus all answers (answer items) given. 
 Question: Do they test performance of their approaches using human judgements?
Output: Yes
To further improve the performance of our models, we introduce in-house labeled data that we use to fine-tune BERT. For the gender classification task, we manually label an in-house dataset of 1,100 users with gender tags, including 550 female users, 550 male users. We obtain 162,829 tweets by crawling the 1,100 users' timelines. For the dialect identification task, we randomly sample 20,000 tweets for each class from an in-house dataset gold labeled with the same 15 classes as the shared task. 
 Question: What are the in-house data employed?
Output: we manually label an in-house dataset of 1,100 users with gender tags we randomly sample 20,000 tweets for each class from an in-house dataset gold labeled with the same 15 classes as the shared task
Since the training data consists only of utterance-denotation pairs, the ranker is trained to maximize the log-likelihood of the correct answer $z$ by treating logical forms as a latent variable The role of the ranker is to score the candidate logical forms generated by the parser; at test time, the logical form receiving the highest score will be used for execution. The ranker is a discriminative log-linear model over logical form $y$ given utterance $x$ :  
 Question: How does the model compute the likelihood of executing to the correction semantic denotation?
Output: By treating logical forms as a latent variable and training a discriminative log-linear model over logical form y given x.
We validate the performance of the proposed s2sL by providing the preliminary results obtained on two different tasks namely, Speech/Music discrimination and emotion classification. We considered the GTZAN Music-Speech dataset [17], consisting of 120 audio files (60 speech and 60 music), for task of classifying speech and music. Each audio file (of 2 seconds duration) is represented using a 13-dimensional mel-frequency cepstral coefficient (MFCC) vector, where each MFCC vector is the average of all the frame level (frame size of 30 msec and an overlap of 10 msec) MFCC vectors. It is to be noted that our main intention for this task is not better feature selection, but to demonstrate the effectiveness of our approach, in particular for low data scenarios. The standard Berlin speech emotion database (EMO-DB) [18] consisting of 535 utterances corresponding to 7 different emotions is considered for the task of emotion classification.  
 Question: Up to how many samples do they experiment with?
Output: 535
The task, as framed above, requires to detect the semantic change between two corpora. The two corpora used in the shared task correspond to the diachronic corpus pair from BIBREF0: DTA18 and DTA19. They consist of subparts of DTA corpus BIBREF11 which is a freely available lemmatized, POS-tagged and spelling-normalized diachronic corpus of German containing texts from the 16th to the 20th century. DTA18 contains 26 million sentences published between 1750-1799 and DTA19 40 million between 1850-1899. 
 Question: What is the corpus used for the task?
Output: DTA18 DTA19
We compared several summarization methods which can be categorized into three groups: unsupervised, non-neural supervised, and neural supervised methods.  As a baseline, we used Lead-N which selects INLINEFORM0 leading sentences as the summary. For all methods, we extracted 3 sentences as the summary since it is the median number of sentences in the gold summaries that we found in our exploratory analysis. Lead-3 baseline performs really well and outperforms almost all the other models, which is not surprising and even consistent with other work that for news summarization, Lead-N baseline is surprisingly hard to beat. 
 Question: What was the best performing baseline?
Output: Lead-3
The log-likelihood of a Plackett-Luce model is not a strict upper bound of the BLEU score, however, it correlates with BLEU well in the case of rich features. The concept of “rich” is actually qualitative, and obscure to define in different applications. We empirically provide a formula to measure the richness in the scenario of machine translation. The greater, the richer. In practice, we find a rough threshold of r is 5 
 Question: How they measure robustness in experiments?
Output: We empirically provide a formula to measure the richness in the scenario of machine translation.
Table TABREF14 shows the results of our main experiments on the 2016 and 2018 test sets for French and German. We use Meteor BIBREF31 as the main metric, as in the WMT tasks BIBREF25 . We compare our transformer baseline to transformer models enriched with image information, as well as to the deliberation models, with or without image information.

We first note that our multimodal models achieve the state of the art performance for transformer networks (constrained models) on the English-German dataset, as compared to BIBREF30 . Second, our deliberation models lead to significant improvements over this baseline across test sets (average INLINEFORM0 , INLINEFORM1 ). 
 Question: What dataset does this approach achieve state of the art results on?
Output: the English-German dataset
To counter that, we use a left-to-right attention mask, similar to the one employed in the original Transformer decoder BIBREF1. For the input tokens in $X$, we apply such mask to all the target tokens $Y$ that were concatenated to $X$, so that input tokens can only attend to the other input tokens. Conversely, for target tokens $y_t$, we put an attention mask on all tokens $y_{>t}$, allowing target tokens $y_t$ to attend only to the input tokens and the already generated target tokens. 
 Question: What is different in BERT-gen from standard BERT?
Output: They use a left-to-right attention mask so that the input tokens can only attend to other input tokens, and the target tokens can only attend to the input tokens and already generated target tokens.
We recruited 100 crowdworkers on Amazon Mechanical Turk (AMT) and measured completion times and accuracies for typing randomly sampled sentences from the Yelp corpus.  
 Question: What user variations have been tested?
Output: completion times and accuracies
With this challenge in mind, we introduce Torch-Struct with three specific contributions:

Modularity: models are represented as distributions with a standard flexible API integrated into a deep learning framework.

Completeness: a broad array of classical algorithms are implemented and new models can easily be added in Python.

Efficiency: implementations target computational/memory efficiency for GPUs and the backend includes extensions for optimization. 
 Question: Is this library implemented into Torch or is framework agnostic?
Output: It uses deep learning framework (pytorch)
Using our annotation software, we automatically extracted landmarks of 2000 images from the UOttawa database BIBREF14 had been annotated for image segmentation tasks.  
 Question: How big are datasets used in experiments?
Output: 2000 images
We compare the predictions of logistic regression models based on unigram bag-of-words features (BOW), sentiment signals (SENT), the linguistic features from our earlier analyses (LING), and combinations of these features. 
 Question: What predictive model do they build?
Output: logistic regression models
In this work, we evaluate three publicly-available off-the-shelf coreference resolution systems, representing three different machine learning paradigms: rule-based systems, feature-driven statistical systems, and neural systems. We evaluate examples of each of the three coreference system architectures described in "Coreference Systems" : the BIBREF5 sieve system from the rule-based paradigm (referred to as RULE), BIBREF6 from the statistical paradigm (STAT), and the BIBREF11 deep reinforcement system from the neural paradigm (NEURAL). 
 Question: Which coreference resolution systems are tested?
Output: the BIBREF5 sieve system from the rule-based paradigm (referred to as RULE), BIBREF6 from the statistical paradigm (STAT), and the BIBREF11 deep reinforcement system from the neural paradigm (NEURAL).
Finally, our combined model which uses both Latent and Explicit structure performs the best with a strong improvement of 1.08 points in ROUGE-L over our base pointer-generator model and 0.6 points in ROUGE-1.  
 Question: By how much they improve over the previous state-of-the-art?
Output: 1.08 points in ROUGE-L over our base pointer-generator model  0.6 points in ROUGE-1
For our single-label topic classification experiments, we use the Switchboard Telephone Speech Corpus BIBREF21 , a collection of two-sided telephone conversations. We further evaluate our topic ID performance on the speech corpora of three languages released by the DARPA LORELEI (Low Resource Languages for Emergent Incidents) Program. 
 Question: What datasets are used to assess the performance of the system?
Output: Switchboard Telephone Speech Corpus BIBREF21 LORELEI (Low Resource Languages for Emergent Incidents) Program
In addition, we evaluate the effectiveness of pre-training by comparing it with a jointly-trained model of forward translation and back-translation. Experimental results show that the encoder-decoder-reconstructor offers significant improvement in BLEU scores and alleviates the problem of repeating and missing words in the translation on English-Japanese translation task, and the encoder-decoder-reconstructor can not be trained well without pre-training, so it proves that we have to train the forward translation model in a manner similar to the conventional attention-based NMT as pre-training. 
 Question: Is pre-training effective in their evaluation?
Output: Yes
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. 
 Question: Do they train their own RE model?
Output: Yes