niryuu/sni-each-converted huggingface dataset:
We created eight different classifiers, each of which used one of the following eight features available from a tweet as retrieved from a stream of the Twitter API:

User location (uloc): This is the location the user specifies in their profile. While this feature might seem a priori useful, it is somewhat limited as this is a free text field that users can leave empty, input a location name that is ambiguous or has typos, or a string that does not match with any specific locations (e.g., “at home”). Looking at users' self-reported locations, Hecht et al. BIBREF49 found that 66% report information that can be translated, accurately or inaccurately, to a geographic location, with the other 34% being either empty or not geolocalisable.

User language (ulang): This is the user's self-declared user interface language. The interface language might be indicative of the user's country of origin; however, they might also have set up the interface in a different language, such as English, because it was the default language when they signed up or because the language of their choice is not available.

Timezone (tz): This indicates the time zone that the user has specified in their settings, e.g., “Pacific Time (US & Canada)”. When the user has specified an accurate time zone in their settings, it can be indicative of their country of origin; however, some users may have the default time zone in their settings, or they may use an equivalent time zone belonging to a different location (e.g., “Europe/London” for a user in Portugal). Also, Twitter's list of time zones does not include all countries.

Tweet language (tlang): The language in which a tweet is believed to be written is automatically detected by Twitter. It has been found to be accurate for major languages, but it leaves much to be desired for less widely used languages. Twitter's language identifier has also been found to struggle with multilingual tweets, where parts of a tweet are written in different languages BIBREF50 .

Offset (offset): This is the offset, with respect to UTC/GMT, that the user has specified in their settings. It is similar to the time zone, albeit more limited as it is shared with a number of countries.

User name (name): This is the name that the user specifies in their settings, which can be their real name, or an alternative name they choose to use. The name of a user can reveal, in some cases, their country of origin.

User description (description): This is a free text where a user can describe themselves, their interests, etc.

Tweet content (content): The text that forms the actual content of the tweet. The use of content has a number of caveats. One is that content might change over time, and therefore new tweets might discuss new topics that the classifiers have not seen before. Another caveat is that the content of the tweet might not be location-specific; in a previous study, Rakesh et al. BIBREF51 found that the content of only 289 out of 10,000 tweets was location-specific.
Output: What are the eight features mentioned?
27,534 messages were left after filtering. This is the data set that is used for experimentation.
Output: What is the size of the labelled dataset?
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
Output: How they measure robustness in experiments?
We conducted our experiments on the CMU ARCTIC database BIBREF33, which contains parallel recordings of professional US English speakers sampled at 16 kHz. One female (slt) was chosen as the target speaker and one male (bdl) and one female (clb) were chosen as sources. We selected 100 utterances each for validation and evaluation, and the other 932 utterances were used as training data. For the TTS corpus, we chose a US female English speaker (judy bieber) from the M-AILABS speech dataset BIBREF34 to train a single-speaker Transformer-TTS model. With the sampling rate also at 16 kHz, the training set contained 15,200 utterances, which were roughly 32 hours long.
Output: What datasets are experimented with?
Past research took a reductionist approach, separately considering these two problems of “what” and “how” via content selection and question construction.  In contrast, neural models motivate an end-to-end architectures. Deep learned frameworks contrast with the reductionist approach, admitting approaches that jointly optimize for both the “what” and “how” in an unified framework. 
Output: What learning paradigms do they cover in this survey?
Work on VAE in BIBREF17 to learn acoustic embeddings conducted experiments using the TIMIT data set. The baseline performance for VAE based phone classification experiments in BIBREF17 report an accuracy of 72.2%. The re-implementation forming the basis for our work gave an accuracy of 72.0%, a result that was considered to provide a credible basis for further work.
Output: What classification baselines are used for comparison?
We follow a similar evaluation protocol to those presented in BIBREF6 , BIBREF8 , BIBREF9 which is to use our learned representations as features for a low complexity classifier (typically linear) on a novel supervised task/domain unseen during training without updating the parameters of our sentence representation model. We also consider such a transfer learning evaluation in an artificially constructed low-resource setting. In addition, we also evaluate the quality of our learned individual word representations using standard benchmarks BIBREF36 , BIBREF37 .
Output: How do they evaluate their sentence representations?
While the mixing strategy compensates for most of the gap between the Fr-De* and the Fr*-De (3.01 $\rightarrow $ 0.17) in the De $\rightarrow $ Fr case, the resulting PSEUDOmix still shows lower BLEU than the target-originated Fr-De* corpus. We thus enhance the quality of the synthetic examples of the source-originated Fr*-De data by further training its mother translation model (En $\rightarrow $ Fr). As illustrated in Figure 2 , with the target-originated Fr-De* corpus being fixed, the quality of the models trained with the source-originated Fr*-De data and PSEUDOmix increases in proportion to the quality of the mother model for the Fr*-De corpus. Eventually, PSEUDOmix shows the highest BLEU, outperforming both Fr*-De and Fr-De* data.  As presented in Table 6 , we observe that fine-tuning using ground truth parallel data brings substantial improvements in the translation qualities of all NMT models. Among all fine-tuned models, PSEUDOmix shows the best performance in all experiments. This is particularly encouraging for the case of De $\rightarrow $ Fr, where PSEUDOmix reported lower BLEU than the Fr-De* data before it was fine-tuned. Even in the case where PSEUDOmix shows comparable results with other synthetic corpora in the Pseudo Only scenario, it shows higher improvements in the translation quality when fine-tuned with the real parallel data. 
Output: How many improvements on the French-German translation benchmark?
We then perform the following analysis.

Speaker's Gender Effects: We search for first-person singular pronouns with subject case (ani, unmarked for gender, corresponding to the English I), and consider the gender of its governing verb (or adjectives in copular constructions such as `I am nice'). The possible genders are `masculine', `feminine' and `both', where the latter indicates a case where the none-diacriticized written form admits both a masculine and a feminine reading. We expect the gender to match the ones requested in the prefix.

Interlocutors' Gender and Number Effects: We search for second-person pronouns and consider their gender and number. For pronouns in subject position, we also consider the gender and number of their governing verbs (or adjectives in copular constructions). For a singular audience, we expect the gender and number to match the requested ones. For a plural audience, we expect the masculine-plural forms.
Output: What type of syntactic analysis is performed?
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 $x$ : 
Output: How does the model compute the likelihood of executing to the correction semantic denotation?
We compared our model with MLE, RL and GAN baselines. Since COCO and EMNLP2017 WMT don't have input while WeiboDial regards posts as input, we chose the following baselines respectively:

MLE: a RNN model trained with MLE objective BIBREF4 . Its extension, Seq2Seq, can work on the dialogue dataset BIBREF2 .

SeqGAN: The first text GAN model that updates the generator with policy gradient based on the rewards from the discriminator BIBREF7 .

LeakGAN: A variant of SeqGAN that provides rewards based on the leaked information of the discriminator for the generator BIBREF11 .

MaliGAN: A variant of SeqGAN that optimizes the generator with a normalized maximum likelihood objective BIBREF8 .

IRL: This inverse reinforcement learning method replaces the discriminator with a reward approximator to provide dense rewards BIBREF12 .

RAML: A RL approach to incorporate MLE objective into RL training framework, which regards BLEU as rewards BIBREF17 .

DialogGAN: An extension of SeqGAN tuned to dialogue generation task with MLE objective added to the adversarial objective BIBREF16 .

DPGAN: A variant of DialogGAN which uses a language model based discriminator and regards cross-entropy as rewards BIBREF13 .
Output: What GAN models were used as baselines to compare against?
Our architecture was designed to show that using a model that captures better information about the context and sequential nature of the text can outperform lexicon-based methods commonly used in the literature.
Output: What meaningful information does the GRU model capture, which traditional ML models do not?
For the image-aided model (W+C+V; upper row in Figure FIGREF19 ), we confirm that the modality attention successfully attenuates irrelevant signals (selfies, etc.) and amplifies relevant modality-based contexts in prediction of a given token.
Output: Do they inspect their model to see if their model learned to associate image parts with words related to entities?
A dialog turn from one speaker may not only be a direct response to the other speaker's query, but also likely to be a continuation of his own previous statement. Thus, when modeling turn $k$ in a dialog, we propose to connect the last RNN state of turn $k-2$ directly to the starting RNN state of turn $k$ , instead of letting it to propagate through the RNN for turn $k-1$ .
Output: How long of dialog history is captured?
Four factual attributes pertaining to the 200 colleges are extracted from the U.S. News Statistics, which consists of Undergraduate Enrollment, Male/Female Ratio, Private/Public, and Region (Northeast, South, West, and Midwest). 
Output: Which major geographical regions are studied?
Our model adopts the recently introduced biaffine attention BIBREF14 to enhance our role scorer. 
Output: What is the biaffine scorer?
We have used a dataset of 14 TDs to conduct our experiments.
Output: Which dataset(s) do they use?
Since the input of our method is textual data, we follow the approach of BIBREF15 and map the text into a fixed-size vector representation. To this end, we use word embeddings that were successfully applied in other domains. We follow BIBREF5 and use pre-trained GloVe word vectors BIBREF16 to initialize the embedding layer (also known as look-up table). Section SECREF18 discusses the embedding layer in more details.
Output: Which pretrained word vectors did they use?
We adopt the following two datasets:

Quora BIBREF1: The Quora Question Pairs dataset contains question pairs annotated with labels indicating whether the two questions are paraphrases. We use the same dataset partition as BIBREF5, with 384,348/10,000/10,000 pairs in the training/development/test set respectively.

MRPC BIBREF34: The Microsoft Research Paraphrase Corpus consists of sentence pairs collected from online news. Each pair is annotated with a label indicating whether the two sentences are semantically equivalent. There are 4,076/1,725 pairs in the training/test set respectively.
Output: What are benhmark datasets for paraphrase identification?
Tweets IDs 6, 8, and 10 are some samples containing offensive words and slurs which arenot hate or offensive in all cases and writers of them used this type of language in their daily communications. Given these pieces of evidence, by considering the content of tweets, we can see in tweets IDs 3, 4, and 9 that our BERT-based classifier can discriminate tweets in which neither and implicit hatred content exist.
Output: What evidence do the authors present that the model can capture some biases in data annotation and collection?
We use the following three evaluation metrics:

Phoneme Error Rate (PER) is the Levenshtein distance between the predicted phoneme sequences and the gold standard phoneme sequences, divided by the length of the gold standard phoneme sequences.

Word Error Rate (WER) is the percentage of words in which the predicted phoneme sequence does not exactly match the gold standard phoneme sequence.

Word Error Rate 100 (WER 100) is the percentage of words in the test set for which the correct guess is not in the first 100 guesses of the system.
Output: what evaluation metrics were used?
The end-to-end system (prefix E2E) uses the DNN topology depicted in Figure FIGREF8 . We present results with 3 distinct size configurations (infixes 700K, 318K, and 40K) each representing the number of approximate parameters, and 2 types of training recipes (suffixes 1stage and 2stage) corresponding to end-to-end and encoder+decoder respectively, as described in UID7 .
Output: How many parameters does the presented model have?
KGR10, also known as plWordNet Corpus 10.0 (PLWNC 10.0), is the result of the work on the toolchain to automatic acquisition and extraction of the website content, called CorpoGrabber BIBREF19 . It is a pipeline of tools to get the most relevant content of the website, including all subsites (up to the user-defined depth). The proposed toolchain can be used to build a big Web corpus of text documents. It requires the list of the root websites as the input. Tools composing CorpoGrabber are adapted to Polish, but most subtasks are language independent. The whole process can be run in parallel on a single machine and includes the following tasks: download of the HTML subpages of each input page URL with HTTrack, extraction of plain text from each subpage by removing boilerplate content (such as navigation links, headers, footers, advertisements from HTML pages) BIBREF20 , deduplication of plain text BIBREF20 , bad quality documents removal utilising Morphological Analysis Converter and Aggregator (MACA) BIBREF21 , documents tagging using Wrocław CRF Tagger (WCRFT) BIBREF22 . Last two steps are available only for Polish.
Output: How was the KGR10 corpus created?
BIBREF14 introduce EMPATHETICDIALOGUES dataset, a novel dataset containing 25k conversations include emotional contexts information to facilitate training and evaluating the textual conversational system. Then, work from BIBREF2 produce a dataset containing 1.5 million Twitter conversation, gathered by using Twitter API from customer care account of 62 brands across several industries. This dataset was used to build tone-aware customer care chatbot. Finally, BIBREF29 tried to enhance SEMAINE corpus BIBREF30 by using crowdsourcing scenario to obtain a human judgement for deciding which response that elicits positive emotion. 
Output: What are the currently available datasets for EAC?
We first pre-train our generator on the Gutenberg dataset BIBREF24 for 20 epochs and then fine-tune BIBREF19 them to our target datasets with a language modeling objective.
Output: What objective function is used in the GAN?
In this work, we build a novel regression model, based on linguistic, content, behavioral and topic features to detect Arabic Twitter bots to understand the impact of bots in spreading religious hatred in Arabic Twitter space. 
Output: Do they propose a new model to better detect Arabic bots specifically?
This process gives a total of INLINEFORM0 10700 training examples with roughly 2000 to 3000 examples per class, with each speaker having an average of 185 examples.
Output: How many instances does their dataset have?
convertible.pl: implementing DCG rules for 1st and 3rd steps in the three-steps conversion, as well as other rules including lexicon.
Output: What DCGs are used?
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.
Output: Does the DMN+ model establish state-of-the-art ?
Our system was ranked second in the competition only 0.3 BLEU points behind the winning team UPC-TALP. The relative low BLEU and high TER scores obtained by all teams are due to out-of-domain data provided in the competition which made the task equally challenging to all participants. The fact that out-of-domain data was provided by the organizers resulted in a challenging but interesting scenario for all participants.
Output: Does the use of out-of-domain data improve the performance of the method?
In this paper, I introduce an interpretable word embedding model, and an associated topic model, which are designed to work well when trained on a small to medium-sized corpus of interest. I tested the performance of the representations as features for document categorization and regression tasks. The results are given in Table TABREF26 . For document categorization, I used three standard benchmark datasets: 20 Newsgroups (19,997 newsgroup posts), Reuters-150 newswire articles (15,500 articles and 150 classes), and Ohsumed medical abstracts on 23 cardiovascular diseases (20,000 articles). I I also analyzed the regression task of predicting the year of a state of the Union address based on its text information. 
Output: What supervised learning tasks are attempted with these representations?
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.
Output: Which metrics do they use to evaluate simultaneous translation?
Although our approach strongly outperforms random baselines, the relatively low F1 scores indicate that predicting which word is echoed in explanations is a very challenging task. It follows that we are only able to derive a limited understanding of how people choose to echo words in explanations. The extent to which explanation construction is fundamentally random BIBREF47, or whether there exist other unidentified patterns, is of course an open question. We hope that our study and the resources that we release encourage further work in understanding the pragmatics of explanations.
Output: Do authors provide any explanation for intriguing patterns of word being echoed?
We extract unique users and tweets from the combined result set to generate a dataset of about 60,000 unique tweets, pertaining to 51,104 unique users.
Output: How many followers did they analyze?
We extract unique users and tweets from the combined result set to generate a dataset of about 60,000 unique tweets, pertaining to 51,104 unique users.
Output: How many tweets are explored in this paper?
We propose a new unsupervised model, Sent2Vec, for learning universal sentence embeddings Formally, we learn a source (or context) embedding INLINEFORM0 and target embedding INLINEFORM1 for each word INLINEFORM2 in the vocabulary, with embedding dimension INLINEFORM3 and INLINEFORM4 as in ( EQREF6 ). The sentence embedding is defined as the average of the source word embeddings of its constituent words, as in ( EQREF8 ). We augment this model furthermore by also learning source embeddings for not only unigrams but also n-grams present in each sentence, and averaging the n-gram embeddings along with the words, i.e., the sentence embedding INLINEFORM5 for INLINEFORM6 is modeled as DISPLAYFORM0
Output: How do the n-gram features incorporate compositionality?
We therefore propose attention models to infer the latent context, i.e., the series of posts that trigger an intervention.
Output: What type of latent context is used to predict instructor intervention?
Arab-Tweet. For modeling age and gender, we use Arap-Tweet BIBREF19 , which we will refer to as Arab-Tweet. UBC Twitter Gender Dataset. We also develop an in-house Twitter dataset for gender. We manually labeled 1,989 users from each of the 21 Arab countries. The dialect identification model in AraNet is based on our winning system in the MADAR shared task 2 BIBREF20 as described in BIBREF12. The corpus is divided into train, dev and test, and the organizers masked test set labels. We make use of two datasets, the LAMA-DINA dataset from BIBREF22, a Twitter dataset with a combination of gold labels from BIBREF23 and distant supervision labels. The rest of the dataset is only labeled using distant supervision (LAMA-DIST) ($182,605$ tweets) . For more information about the dataset, readers are referred to BIBREF22.  We use the dataset for irony identification on Arabic tweets released by IDAT@FIRE2019 shared-task BIBREF24. We collect 15 datasets related to sentiment analysis of Arabic, including MSA and dialects BIBREF25, BIBREF26, BIBREF27, BIBREF1, BIBREF28, BIBREF29, BIBREF30, BIBREF31, BIBREF32, BIBREF33, BIBREF34. Table TABREF28 shows all the corpora we use. 
Output: What datasets are used in training?
We use the pre-trained uncased BERT$_\mathrm {BASE}$ model for fine-tuning, because we find that BERT$_\mathrm {LARGE}$ model performs slightly worse than BERT$_\mathrm {BASE}$ in this task. 
Output: Do they use large or small BERT?
DeepMine is publicly available for everybody with a variety of licenses for different users. It was collected using crowdsourcing BIBREF4. The data collection was done using an Android application. Each respondent installed the application on his/her personal device and recorded several phrases in different sessions. 
Output: how was the speech collected?
TF-IDF: We applied TF-IDF features as inputs to four classifiers: support vector machine classifier (SVM), logistic regression classifier (LR), Naive Bayes classifier (NB) and random forest (RF); CNN: We followed the same architecture as BIBREF9; LSTM: We applied an LSTM model BIBREF20 to classify the sentences with pre-trained word embeddings;LSTM-soft: We then added a soft-attention BIBREF21 layer on top of the LSTM model where we computed soft alignment scores over each of the hidden states; LSTM-self: We applied a self-attention layer BIBREF22 to LSTM model. 
Output: To what baseline models is proposed model compared?
Davidson et al. BIBREF2 showed that there were systematic and substantial racial biases in five benchmark Twitter datasets annotated for offensive language detection. By examining more samples and with respect to recently studies BIBREF2, BIBREF24, BIBREF19, it is clear that many errors are due to biases from data collection BIBREF19 and rules of annotation BIBREF24 and not the classifier itself. 
Output: What are the existing biases?
We first use state-of-the-art PDTB taggers for our baseline BIBREF13 , BIBREF12 for the evaluation of the causality prediction of our models ( BIBREF12 requires sentences extracted from the text as its input, so we used our parser to extract sentences from the message).
Output: What baselines did they consider?
Detection: In hypernymy detection, the task is to classify whether pairs of words are in a hypernymy relation. Direction: In direction prediction, the task is to identify which term is broader in a given pair of words. Graded Entailment: In graded entailment, the task is to quantify the degree to which a hypernymy relation holds.
Output: What hypernymy tasks do they study?
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.
Output: Which datasets do they use?
The results in Table TABREF38 confirm the results of BIBREF13 and suggest that we successfully replicated a large proportion of their features. The results for all three prediction settings (one outgoing edge: INLINEFORM0 , support/attack: INLINEFORM1 and support/attack/neither: INLINEFORM2 ) across all type variables ( INLINEFORM3 , INLINEFORM4 and INLINEFORM5 ) are displayed in Table TABREF39 . All models significantly outperform the majority baseline with respect to macro F1.
Output: What baseline and classification systems are used in experiments?
To do so, we constructed a new dataset with 23,700 queries that are short and unstructured, in the same style made by real users of task-oriented systems. 
Output: What is the size of this dataset?
Hence, the continuous relaxation to top-k-argmax operation can be simply implemented by iteratively using the max operation which is continuous and allows for gradient flow during backpropagation.
Output: Which loss metrics do they try in their new training procedure evaluated on the output of beam search?
Even though this corpus has incorrect sentences and their emotional labels, they lack their respective corrected sentences, necessary for the training of our model. In order to obtain this missing information, we outsource native English speakers from an unbiased and anonymous platform, called Amazon Mechanical Turk (MTurk) BIBREF19, which is a paid marketplace for Human Intelligence Tasks (HITs). We use this platform to create tasks for native English speakers to format the original incorrect tweets into correct sentences. Some examples are shown in Table TABREF12. The dataset used to evaluate the models' performance is the Chatbot Natural Language Unerstanding (NLU) Evaluation Corpus, introduced by Braun et al. BIBREF20 to test NLU services. It is a publicly available benchmark and is composed of sentences obtained from a German Telegram chatbot used to answer questions about public transport connections. The dataset has two intents, namely Departure Time and Find Connection with 100 train and 106 test samples, shown in Table TABREF18. Even though English is the main language of the benchmark, this dataset contains a few German station and street names.
Output: Do they report results only on English datasets?
TOEFL listening comprehension test is for human English learners whose native language is not English. This paper reports how today's machine can perform with such a test.

The listening comprehension task considered here is highly related to Spoken Question Answering (SQA) BIBREF0 , BIBREF1 . 
Output: What is the new task proposed in this work?
For sentence encoding models, we chose a simple one-layer bidirectional LSTM with max pooling (BiLSTM-max) with the hidden size of 600D per direction, used e.g. in InferSent BIBREF17 , and HBMP BIBREF18 . For the other models, we have chosen ESIM BIBREF19 , which includes cross-sentence attention, and KIM BIBREF2 , which has cross-sentence attention and utilizes external knowledge. We also selected two model involving a pre-trained language model, namely ESIM + ELMo BIBREF20 and BERT BIBREF0 .
Output: Which models were compared?
Above all, we introduce two common baselines. The first one just selects the leading sentences to form a summary. It is often used as an official baseline of DUC, and we name it “LEAD”. The other system is called “QUERY_SIM”, which directly ranks sentences according to its TF-IDF cosine similarity to the query. Above all, we introduce two common baselines. The first one just selects the leading sentences to form a summary. It is often used as an official baseline of DUC, and we name it “LEAD”. The other system is called “QUERY_SIM”, which directly ranks sentences according to its TF-IDF cosine similarity to the query. In addition, we implement two popular extractive query-focused summarization methods, called MultiMR BIBREF2 and SVR BIBREF20 . Since our model is totally data-driven, we introduce a recent summarization system DocEmb BIBREF9 that also just use deep neural network features to rank sentences. To verify the effectiveness of the joint model, we design a baseline called ISOLATION, which performs saliency ranking and relevance ranking in isolation.
Output: What models do they compare to?
For the NMT experiment, we used the Torch implementation to train a 2-layer LSTM model with 500 hidden units in both encoder and decoder, with 12 epochs.
Output: Which NMT models did they experiment with?
Throughout this article, we use two different embedding spaces. The first is the widely used representation built on GoogleNews BIBREF8 . The second is taken from BIBREF2 , and was trained on a Reddit dataset BIBREF9 .
Output: Which embeddings do they detect biases in?
However, the coherence of the topics produced by LDA is poorer than expected.

To address this lack of coherence, we applied non-negative matrix factorization (NMF).
Output: How are prominent topics idenified in Dabiq and Rumiyah?
The baseline is a hierarchical phrase-based system BIBREF29 with a 4-gram language model, with feature weights tuned using MIRA BIBREF30 . Therefore, an appropriate additional baseline would be to mark translation rules with these indicator functions but without the scores, akin to identifying rules with phrases in them (Baseline + SegOn).
Output: Which translation systems do they compare against?
Our systems are attentional encoder-decoder networks BIBREF0 . We base our implementation on the dl4mt-tutorial, which we enhanced with new features such as ensemble decoding and pervasive dropout.
Output: what are the baseline systems?
We employ a huge dataset of 45,000 self-reported depressed users introduced in BIBREF46 where a lexicon of depression symptoms consisting of 1500 depression-indicative terms was created with the help of psychologist clinician and employed for collecting self-declared depressed individual's profiles. A subset of 8,770 users (24 million time-stamped tweets) containing 3981 depressed and 4789 control users (that do not show any depressive behavior) were verified by two human judges BIBREF46 .
Output: How is the data annotated?
We evaluate the adversarially trained models, as shown in Table TABREF18.

After adversarial training, the performance of all the target models raises significantly, while that on the original examples remain comparable.
Output: How much in experiments is performance improved for models trained with generated adversarial examples?
$Coherence@k$ 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.
Output: How do they evaluate interpretability?
For instance, a message can be regarded as harmless on its own, but when taking previous threads into account it may be seen as abusive, and vice versa. This aspect makes detecting abusive language extremely laborious even for human annotators; therefore it is difficult to build a large and reliable dataset BIBREF10 .
Output: What examples of the difficulties presented by the context-dependent nature of online aggression do they authors give?
However, probably the most surprising result in the field is Zipf's law, which states that if one ranks words by their frequency in a large text, the resulting rank frequency distribution is approximately a power law, for all languages BIBREF0, BIBREF11. Another interesting characterization of texts is the Heaps-Herdan law, which describes how the vocabulary -that is, the set of different words- grows with the size of a text, the number of which, empirically, has been found to grow as a power of the text size BIBREF18, BIBREF19. It is worth noting that it has been argued that this law is a consequence Zipf's law. BIBREF20, BIBREF21
Output: How do Zipf and Herdan-Heap's laws differ?
We describe our rules for WikiSQL here. Our rule for KBQA is simple without using a curated mapping dictionary. The pipeline of rules in SequentialQA is similar to that of WikiSQL.
Output: Are the rules dataset specific?
For comparison purpose, we build two baseline systems for each direction: one is use the traditional phrase-based statistical machine translation (SMT), the other one is the NMT system. 
Output: what was the baseline?
The number of fake news that have a duplicate in the training dataset are 1018 whereas, the number of articles with genuine content that have a duplicate article in the training set is 322.
Output: what is the size of the dataset?
As well as refining the annotation guidelines, the development process trained annotators who were not security experts. The development and test sets for Darkode and Hack Forums were annotated by additional team members (five for Darkode, one for Hack Forums), and then every disagreement was discussed and resolved to produce a final annotation. The authors, who are researchers in either NLP or computer security, did all of the annotation.
Output: Who annotated the data?
As illustrated in Figure FIGREF1, our key idea is that we can exploit discourse relations BIBREF4 to efficiently propagate polarity from seed predicates that directly report one's emotions (e.g., “to be glad” is positive). Suppose that events $x_1$ are $x_2$ are in the discourse relation of Cause (i.e., $x_1$ causes $x_2$). If the seed lexicon suggests $x_2$ is positive, $x_1$ is also likely to be positive because it triggers the positive emotion. The fact that $x_2$ is known to be negative indicates the negative polarity of $x_1$. Similarly, if $x_1$ and $x_2$ are in the discourse relation of Concession (i.e., $x_2$ in spite of $x_1$), the reverse of $x_2$'s polarity can be propagated to $x_1$. Even if $x_2$'s polarity is not known in advance, we can exploit the tendency of $x_1$ and $x_2$ to be of the same polarity (for Cause) or of the reverse polarity (for Concession) although the heuristic is not exempt from counterexamples. We transform this idea into objective functions and train neural network models that predict the polarity of a given event.
Output: How are relations used to propagate polarity?
The proposed Multimodal Differential Network (MDN) consists of a representation module and a joint mixture module. We use a triplet network BIBREF41 , BIBREF42 in our representation module. The triplet network consists of three sub-parts: target, supporting, and contrasting networks. All three networks share the same parameters. The Mixture module brings the image and caption embeddings to a joint feature embedding space.
Output: How do the authors define a differential network?
The dataset (composed of 2.6 GB of raw text) includes $421\,829\,960$ words divided into $17\,305\,401$ sentences.
Output: How big is dataset used to train Word2Vec for the Italian Language?
SVMRank is a modification to SVM that assigns scores to each data point and allows the results to be ranked ( BIBREF26 ). We use SVMRank in the experiments below. 
Output: what is the supervised model they developed?
The stance towards vaccination was categorized into `Negative’, `Neutral’, `Positive’ and `Not clear’.
Output: Do they allow for messages with vaccination-related key terms to be of neutral stance?
Data-driven models for morphological analysis are constructed using training data INLINEFORM0 consisting of INLINEFORM1 training examples. The baseline model BIBREF5 we compare with regards the output space of the model as a subset INLINEFORM2 where INLINEFORM3 is the set of all tag sets seen in this training data. Specifically, they solve the task as a multi-class classification problem where the classes are individual tag sets. In low-resource scenarios, this indicates that INLINEFORM4 and even for those tag sets existing in INLINEFORM5 we may have seen very few training examples. The conditional probability of a sequence of tag sets given the sentence is formulated as a 0th order CRF. DISPLAYFORM0
Output: What other cross-lingual approaches is the model compared to?
After using Kytea, Japanese texts are applied LSW algorithm to replace OOV words by their synonyms. 
Output: Are synonymous relation taken into account in the Japanese-Vietnamese task?
Input of the model is the concatenation of word embedding and another embedding indicating whether this word is predicate: $ \mathbf {x}_t = [\mathbf {W}_{\text{emb}}(w_t), \mathbf {W}_{\text{mask}}(w_t = v)]. $
Output: What's the input representation of OpenIE tuples into the model?
In this paper, we work on recognizing the polarity of an affective event that is represented by a score ranging from $-1$ (negative) to 1 (positive).
Output: What are labels available in dataset for supervision?
In order to fully leverage gloss information, we propose GlossBERT to construct context-gloss pairs from all possible senses of the target word in WordNet, thus treating WSD task as a sentence-pair classification problem.
Output: Do they incoprorate WordNet into the model?
In this case, the decoding function is a linear projection, which is $= f_{\text{de}}()=+ $ , where $\in ^{d_\times d_}$ is a trainable weight matrix and $\in ^{d_\times 1}$ is the bias term. A family of bijective transformation was designed in NICE BIBREF17 , and the simplest continuous bijective function $f:^D\rightarrow ^D$ and its inverse $f^{-1}$ is defined as:

$$h: \hspace{14.22636pt} _1 &= _1, & _2 &= _2+m(_1) \nonumber \\ h^{-1}: \hspace{14.22636pt} _1 &= _1, & _2 &= _2-m(_1) \nonumber $$ (Eq. 15)

where $_1$ is a $d$ -dimensional partition of the input $\in ^D$ , and $m:^d\rightarrow ^{D-d}$ is an arbitrary continuous function, which could be a trainable multi-layer feedforward neural network with non-linear activation functions. It is named as an `additive coupling layer' BIBREF17 , which has unit Jacobian determinant. To allow the learning system to explore more powerful transformation, we follow the design of the `affine coupling layer' BIBREF24 :

$$h: \hspace{5.69046pt} _1 &= _1, & _2 &= _2 \odot \text{exp}(s(_1)) + t(_1) \nonumber \\ h^{-1}: \hspace{5.69046pt} _1 &= _1, & _2 &= (_2-t(_1)) \odot \text{exp}(-s(_1)) \nonumber $$ (Eq. 16)

where $s:^d\rightarrow ^{D-d}$ and $t:^d\rightarrow ^{D-d}$ are both neural networks with linear output units.

The requirement of the continuous bijective transformation is that, the dimensionality of the input $$ and the output $$ need to match exactly.
Output: What are the two decoding functions?
We observe interesting hidden correlation in data. Fig. FIGREF24 has Topic 2 as selected topic. Topic 2 contains top-4 co-occurring keywords "vegan", "yoga", "job", "every_woman" having the highest term frequency. We can infer different things from the topic that "women usually practice yoga more than men", "women teach yoga and take it as a job", "Yogi follow vegan diet". We would say there are noticeable correlation in data i.e. `Yoga-Veganism', `Women-Yoga'. Women-Yoga
Output: What other interesting correlations are observed?
The corpus includes 2000 sentences. 
Output: How long is the dataset?
To understand the reason behind the drop in performance, first we need to understand how BERT processes input text data. BERT uses WordPiece tokenizer to tokenize the text. WordPiece tokenizer utterances based on the longest prefix matching algorithm to generate tokens . The tokens thus obtained are fed as input of the BERT model. When it comes to tokenizing noisy data, we see a very interesting behaviour from WordPiece tokenizer. Owing to the spelling mistakes, these words are not directly found in BERT’s dictionary. Hence WordPiece tokenizer tokenizes noisy words into subwords. However, it ends up breaking them into subwords whose meaning can be very different from the meaning of the original word. Often, this changes the meaning of the sentence completely, therefore leading to substantial dip in the performance.
Output: What is the reason behind the drop in performance using BERT for some popular task?
On the same architecture, our RCRN outperforms ablative baselines BiLSTM by INLINEFORM2 and 3L-BiLSTM by INLINEFORM3 on average across 16 datasets.
Output: By how much do they outperform BiLSTMs in Sentiment Analysis?
In order to verify the effectiveness of our method (i.e., Multi-linear attention) replacing multi-head attention in Transformer, we carry out two NLP tasks named language modeling (LM) and neural machine translation (NMT). Then, we test different model configurations on the PTB BIBREF25 , WikiText-103 BIBREF26 and One-Billion Word benchmark BIBREF27 datasets and report the results in Table 1 and Table 2 . In this task, we have trained the Transformer model BIBREF2 on WMT 2016 English-German dataset BIBREF36 .
Output: What datasets or tasks do they conduct experiments on?
We use the Dialogue State Tracking Competition 2 (DSTC2) dataset BIBREF27 which is the most widely used dataset for research on task-oriented chatbots.  We also used two other datasets recently open-sourced by Google Research BIBREF28 which are M2M-sim-M (dataset in movie domain) and M2M-sim-R (dataset in restaurant domain). 
Output: What are the three datasets used?
In the first scenario we equip the decoder with an additional morphology table including target-side affixes. We place an attention module on top of the table which is controlled by the decoder. At each step, as the decoder samples a character, it searches the table to find the most relevant information which can enrich its state. Signals sent from the table can be interpreted as additional constraints. In the second scenario we share the decoder between two output channels. The first one samples the target character and the other one predicts the morphological annotation of the character. This multi-tasking approach forces the decoder to send morphology-aware information to the final layer which results in better predictions. In the third scenario we combine these two models. 
Output: How are the auxiliary signals from the morphology table incorporated in the decoder?
Last but not least, ethics and fairness are important considerations, that deserve to be studied. In that sense, detection of individual and global bias should be prioritized in order to give useful feedbacks to practitioners. Furthermore we are considering using adversarial learning as in BIBREF33 in order to ensure fairness during the training process.
Output: Is there any ethical consideration in the research?
Last but not least, ethics and fairness are important considerations, that deserve to be studied. In that sense, detection of individual and global bias should be prioritized in order to give useful feedbacks to practitioners. Furthermore we are considering using adversarial learning as in BIBREF33 in order to ensure fairness during the training process.
Output: Do they analyze if their system has any bias?
Three different datasets have been used to train our models: the Toronto book corpus, Wikipedia sentences and tweets.  Our Sent2Vec models also on average outperform or are at par with the C-PHRASE model, despite significantly lagging behind on the STS 2014 WordNet and News subtasks. This observation can be attributed to the fact that a big chunk of the data that the C-PHRASE model is trained on comes from English Wikipedia, helping it to perform well on datasets involving definition and news items. 
Output: Do they report results only on English data?
We showed in BIBREF22 that Pearson correlation is badly suited for STS. Instead, we compute the Spearman's rank correlation between the cosine-similarity of the sentence embeddings and the gold labels.
Output: What metrics are used for the STS tasks?
We evaluate our newly proposed models and related baselines in several low-resource settings across different languages with real, distantly supervised data with non-synthetic noise. The advanced modeling of the noisy labels substantially improves the performance up to 36% over methods without noise-handling and up to 9% over all other noise-handling baselines.
Output: Did they evaluate against baseline?
First preference is given to the labels that are perfectly matching in all the neural annotators. In Table TABREF11, we can see that both datasets have about 40% of exactly matching labels over all models (AM). Then priority is given to the context-based models to check if the label in all context models is matching perfectly. In case two out of three context models are correct, then it is being checked if that label is also produced by at least one of the non-context models. Then, we allow labels to rely on these at least two context models. As a result, about 47% of the labels are taken based on the context models (CM). When we see that none of the context models is producing the same results, then we rank the labels with their respective confidence values produced as a probability distribution using the $softmax$ function. The labels are sorted in descending order according to confidence values. Then we check if the first three (case when one context model and both non-context models produce the same label) or at least two labels are matching, then we allow to pick that one. There are about 3% in IEMOCAP and 5% in MELD (BM).

Finally, when none the above conditions are fulfilled, we leave out the label with an unknown category. This unknown category of the dialogue act is labeled with `xx' in the final annotations, and they are about 7% in IEMOCAP and 11% in MELD (NM).
Output: How does the ensemble annotator extract the final label?
Building Extractive CNN/Daily Mail
Output: What is the problem with existing metrics that they are trying to address?