niryuu/sni-each-converted huggingface dataset:
Suppose we are given a specific language model such as GPT-2 BIBREF6, GROVER BIBREF8, or CTRL BIBREF7, and it is characterized in terms of estimates of either cross-entropy $H(P,Q)$ or perplexity $\mathrm {PPL}(P,Q)$.

We can see directly that the Neyman-Pearson error of detection in the case of i.i.d. tokens is:

and similar results hold for ergodic observations.

Since we think of $H(P)$ as a constant, we observe that the error exponent for the decision problem is precisely an affine shift of the cross-entropy. Outputs from models that are better in the sense of cross-entropy or perplexity are harder to distinguish from authentic text.

Thus we see that intuitive measures of generative text quality match a formal operational measure of indistinguishability that comes from the hypothesis testing limit. 
 Question: Which language models generate text that can be easier to classify as genuine or generated?
Output: Outputs from models that are better in the sense of cross-entropy or perplexity are harder to distinguish from authentic text.
Our semi-supervised approach is quite straightforward: first a model is trained on the training set and then this model is used to predict the labels of the silver data. This silver data is then simply added to our training set, after which the model is retrained. However, an extra step is applied to ensure that the silver data is of reasonable quality. 
 Question: What semi-supervised learning is applied?
Output: first a model is trained on the training set and then this model is used to predict the labels of the silver data This silver data is then simply added to our training set, after which the model is retrained
RC-QED$^{\rm E}$ can be naturally solved by path ranking-based KGC (PRKGC), where the query triplet and the sampled paths correspond to a question and derivation steps, respectively. 
 Question: What is the baseline?
Output:  path ranking-based KGC (PRKGC)
Recently, Wen et al. wensclstm15 proposed an RNN-based approach, which outperformed previous methods on several metrics. However, the generated sentences often did not include all desired attributes. 
 Question: How is some information lost in the RNN-based generation models?
Output: the generated sentences often did not include all desired attributes.
The aim of this study is to provide empirical evidence indicating that leveraging open data sources of german texts, automated political bias prediction is possible with above chance accuracy. 
 Question: Which countries and languages do the political speeches and manifestos come from?
Output: german
To establish a baseline, we consider a random method that predicts the positive label with 0.15 probability (the base rate of positive instances). To examine the utility of our features in a neural framework, we further adapt our word-level task as a tagging task, and use LSTM as a baseline. Specifically, we concatenate an OP and PC with a special token as the separator so that an LSTM model can potentially distinguish the OP from PC, and then tag each word based on the label of its stemmed version. We use GloVe embeddings to initialize the word embeddings BIBREF40. We concatenate our proposed features of the corresponding stemmed word to the word embedding; the resulting difference in performance between a vanilla LSTM demonstrates the utility of our proposed features. 
 Question: What is the baseline?
Output: random method  LSTM
Each one of these tokenizers, we combine with some types of representation methods, including word to vector methods such as continuous bag of words BIBREF5, pre-trained embedding as fasttext (trained on Wiki Vietnamese language) BIBREF6 and sonvx (trained on Vietnamese newspaper) BIBREF7 The dataset in this HSD task is really imbalance. Clean class dominates with 91.5%, offensive class takes 5% and the rest belongs to hate class with 3.5%. 
 Question: What dataset do they use?
Output: They used Wiki Vietnamese language and Vietnamese newspapers to pretrain embeddings and dataset provided in HSD task to train model (details not mentioned in paper).
ACL Anthology Reference Corpus contains the canonical 10,921 computational linguistics papers, from which I have generated 622,144 sentences after filtering out sentences with lower quality. 
 Question: What dataset is used?
Output: ACL Anthology Reference Corpus
We can identify three main cases for which the model produces an error:

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

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

the correct class can be inferred from the text content if the text is interpreted correctly 
 Question: What type of errors do the classifiers use?
Output: correct class can be directly inferred from the text content easily, even without background knowledge correct class can be inferred from the text content, given that event-specific knowledge is provided orrect class can be inferred from the text content if the text is interpreted correctly
One would expect that training with more data would improve the quality of the embeddings, but we found out with the results obtained with the C3 dataset, that only high-quality data helps. 
 Question: What turn out to be more important high volume or high quality data?
Output: only high-quality data helps
Approach ::: Method: Training and Testing ::: Experiment 1: Representation
Some of the problems encountered by prior approaches seem to be attributable to the use of infix notation. In this experiment, we compare translation BLEU-2 scores to spot the differences in representation interpretability. Approach ::: Method: Training and Testing ::: Experiment 2: State-of-the-art
This experiment compares our networks to recent previous work. We count a given test score by a simple “correct versus incorrect" method. The answer to an expression directly ties to all of the translation terms being correct, which is why we do not consider partial precision. We compare average accuracies over 3 test trials on different randomly sampled test sets from each MWP dataset. 
 Question: How is this problem evaluated?
Output: BLEU-2 average accuracies over 3 test trials on different randomly sampled test sets
Controls are calculated heuristically. All words found in the control word lists are then removed from the reference sentence. The remaining words, which represent the content, are used as input into the model, along with their POS tags and lemmas.

In this way we encourage models to construct a sentence using content and style independently. 
 Question: How they know what are content words?
Output:  words found in the control word lists are then removed The remaining words, which represent the content
The Gallup survey asked for opinions on legality of “homosexual relations" until 2008, but then changed the wording to “gay and lesbian relations". This was likely because many people who identify as gay and lesbian find the word homosexual to be outdated and derogatory. 
 Question: Do they analyze specific derogatory words?
Output: Yes
Our training data consists of 2.09M sentence pairs extracted from LDC corpus. To test different approaches on Chinese-to-English translation task, we use NIST 2003(MT03) dataset as the validation set, and NIST 2004(MT04), NIST 2005(MT05), NIST 2006(MT06) datasets as our test sets. For English-to-Chinese translation task, we also use NIST 2003(MT03) dataset as the validation set, and NIST 2008(MT08) will be used as test set. 
 Question: What dataset did they use?
Output: LDC corpus NIST 2003(MT03) NIST 2004(MT04), NIST 2005(MT05), NIST 2006(MT06) NIST 2008(MT08)
Ternary and fine-grained sentiment classification were part of the SemEval-2016 “Sentiment Analysis in Twitter” task BIBREF16 . We use the high-quality datasets the challenge organizers released. 
 Question: What dataset did they use?
Output:  high-quality datasets  from SemEval-2016 “Sentiment Analysis in Twitter” task
Spoken-SQuAD is chosen as the target domain data for training and testing. Spoken-SQuAD BIBREF5 is an automatically generated corpus in which the document is in spoken form and the question is in text form. The reference transcriptions are from SQuAD BIBREF1 . There are 37,111 and 5,351 question answer pairs in the training and testing sets respectively, and the word error rate (WER) of both sets is around 22.7%.

The original SQuAD, Text-SQuAD, is chosen as the source domain data, where only question answering pairs appearing in Spoken-SQuAD are utilized. In our task setting, during training we train the proposed QA model on both Text-SQuAD and Spoken-SQuAD training sets. While in the testing stage, we evaluate the performance on Spoken-SQuAD testing set. 
 Question: Which datasets did they use for evaluation?
Output: Spoken-SQuAD testing set
We make copies of the monolingual model for each language and add additional crosslingual latent variables (CLVs) to couple the monolingual models, capturing crosslingual semantic role patterns. Concretely, when training on parallel sentences, whenever the head words of the arguments are aligned, we add a CLV as a parent of the two corresponding role variables.  
 Question: Which additional latent variables are used in the model?
Output: CLV as a parent of the two corresponding role variables
We have evaluated the output of Nefnir against a reference corpus of 21,093 tokens and their correct lemmas.

Samples for the reference corpus were extracted from two larger corpora, in order to obtain a diverse vocabulary: 
 Question: Which dataset do they use?
Output: a reference corpus of 21,093 tokens and their correct lemmas
We evaluate our method in translating English into three morphologically-rich languages each with a distinct morphological typology: Arabic, Czech and Turkish, and show that our model is able to obtain better translation accuracy and generalization capacity than conventional approaches to open-vocabulary NMT. 
 Question: What are the three languages studied in the paper?
Output: Arabic, Czech and Turkish
We use the publicly available dataset KVRET BIBREF5 in our experiments. This dataset includes dialogues in 3 domains: calendar, weather, navigation (POI) which is suitable for our mix-domain dialogue experiments.  
 Question: What multi-domain dataset is used?
Output: KVRET
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. 
 Question: Which loss metrics do they try in their new training procedure evaluated on the output of beam search?
Output:  continuous relaxation to top-k-argmax
The average content score across the paintings is 3.7 which demonstrates that the prose generated is relevant to the painting. The average creativity score is 3.9 which demonstrates that the model captures more than basic objects in the painting successfully using poetic clues in the scene. The average style score is 3.9 which demonstrates that the prose generated is perceived to be in the style of Shakespeare. 
 Question: How does final model rate on Likert scale?
Output: average content score across the paintings is 3.7 average creativity score is 3.9 average style score is 3.9
In order to derive the discourse features, an entity grid is constructed by feeding the document through an NLP pipeline to identify salient entities. Two flavors of discourse features are created by populating the entity grid with either (i) grammatical relations (GR) or (ii) RST discourse relations (RST).  Denoting a feature as such with INLINEFORM4 , we construct the pooling vector INLINEFORM5 for the char-bigrams, and concatenate INLINEFORM6 to INLINEFORM7 before feeding the resulting vector to the softmax layer. 
 Question: How are discourse features incorporated into the model?
Output: They derive entity grid with grammatical relations and RST discourse relations and concatenate them with pooling vector for the char-bigrams before feeding to the resulting vector to the softmax layer.
String kernels represent a way of using information at the character level by measuring the similarity of strings through character n-grams. 
 Question: What is a string kernel?
Output: String kernel is a technique that uses character n-grams to measure the similarity of strings
Parallel Scan Inference
The commutative properties of semiring algorithms allow flexibility in the order in which we compute $A(\ell )$. Typical implementations of dynamic programming algorithms are serial in the length of the sequence. Vectorized Parsing
Computational complexity is even more of an issue for parsing algorithms, which cannot be as easily parallelized. Semiring Matrix Operations
The two previous optimizations reduce most of the cost to semiring matrix multiplication. In the specific case of the $(\sum , \times )$ semiring these can be computed very efficiently using matrix multiplication, which is highly-tuned on GPU hardware. Unfortunately for other semirings, such as log and max, these operations are either slow or very memory inefficient. For instance, for matrices $T$ and $U$ of sized $N \times M$ and $M \times O$, we can broadcast with $\otimes $ to a tensor of size $N \times M \times O$ and then reduce dim $M$ by $\bigoplus $ at a huge memory cost. 
 Question: What baselines are used in experiments?
Output: Typical implementations of dynamic programming algorithms are serial in the length of the sequence Computational complexity is even more of an issue for parsing algorithms, which cannot be as easily parallelized Unfortunately for other semirings, such as log and max, these operations are either slow or very memory inefficient
Another widely used method for classifying misleading content is to use distant annotations, for example to classify a tweet based on the domain of a URL that is shared by the tweet, or a hashtag that is contained in the tweet BIBREF6, BIBREF7, BIBREF8. In order to scale annotations that go beyond metadata to larger datasets, Natural Language Processing (NLP) models can be used to automatically label text content. 
 Question: What proxies for data annotation were used in previous datasets?
Output: widely used method for classifying misleading content is to use distant annotations, for example to classify a tweet based on the domain of a URL that is shared by the tweet, or a hashtag that is contained in the tweet Natural Language Processing (NLP) models can be used to automatically label text content
Three incremental levels of document preprocessing are experimented with: raw text, text cleaning through document logical structure detection, and removal of keyphrase sparse sections of the document. In doing so, we present the first consistent comparison of different keyphrase extraction models and study their robustness over noisy text. 
 Question: what levels of document preprocessing are looked at?
Output: raw text text cleaning through document logical structure detection removal of keyphrase sparse sections of the document
For all tasks, we use a TensorFlow implementation. AraNet predicts age, dialect, gender, emotion, irony, and sentiment from social media posts. It delivers state-of-the-art and competitive performance on these tasks and has the advantage of using a unified, simple framework based on the recently-developed BERT model.  
 Question: Did they experiment on all the tasks?
Output: Yes
We extract 5 Surface and Lexical features, namely sequence length in number of tokens, average word length, type-token ratio, and lexical to tokens ratio (ratio of adjectives, verbs, nouns, and adverbs to tokens). We extracted two features that use a learned representation: Firstly, we get a sentence embedding feature that is built by averaging the word embeddings of an input sentence. Secondly, we extract a fastText representation using the fastText library with the same parameters as reported in Joulin et al. joulin2016bag. 
 Question: Do they differentiate insights where they are dealing with learned or engineered representations?
Output: Yes
The systems were trained on all parallel data available for the WMT 2016. The news commentary corpus, the European parliament proceedings and the common crawl corpus sum up to 3.7M sentences and around 90M words. 
 Question: Which dataset do they use?
Output: parallel data available for the WMT 2016
The attention function BIBREF11 is used to compute the similarity score between passages and questions as: INLINEFORM2 
 Question: Do they use attention?
Output: Yes
Adding word alignments in parallel sentences results in small, non significant improvements, even if there is some labeled data available in the source language. This difficulty in showing the usefulness of parallel corpora for SRI may be due to the current assumptions about role alignments, which mean that only a small percentage of roles are aligned. Further analyses reveals that annotating small amounts of data can easily outperform the performance gains obtained by adding large unlabeled dataset as well as adding parallel corpora. 
 Question: Overall, does having parallel data improve semantic role induction across multiple languages?
Output: No
We performed the annotation with freely available tools for the Portuguese language. 
 Question: Are the annotations automatic or manually created?
Output: Automatic
The MCD splits achieve a significantly higher compound divergence at a similar atom divergence when compared to the other experiments.  
 Question: What are results of comparison between novel method to other approaches for creating compositional generalization benchmarks?
Output: The MCD splits achieve a significantly higher compound divergence at a similar atom divergence when compared to the other experiments
We train our models on Sentiment140 and Amazon product reviews. Both of these datasets concentrates on sentiment represented by a short text.  For training the softmax model, we divide the text sentiment to two kinds of emotion, positive and negative. And for training the tanh model, we convert the positive and negative emotion to [-1.0, 1.0] continuous sentiment score, while 1.0 means positive and vice versa.  
 Question: Was the introduced LSTM+CNN model trained on annotated data in a supervised fashion?
Output: Yes
We extracted Japanese word pairs from the Evaluation Dataset of Japanese Lexical Simplification kodaira.  
 Question: where does the data come from?
Output: Evaluation Dataset of Japanese Lexical Simplification kodaira
The main contribution of our work is thus a new parallel data-to-text NLG corpus that (1) is more conversational, rather than information seeking or question answering, and thus more suitable for an open-domain dialogue system, (2) represents a new, unexplored domain which, however, has excellent potential for application in conversational agents, and (3) has high-quality, manually cleaned human-produced utterances. 
 Question: How the authors made sure that corpus is clean despite being crowdsourced?
Output: manually cleaned human-produced utterances
They used 20-million-words data randomly sampled from the raw text released by the CoNLL 2017 Shared Task - Automatically Annotated Raw Texts and Word Embeddings BIBREF8, which is a combination of Wikipedia dump and common crawl.  For example, we compared the Latvian model by ELMoForManyLangs with a model we trained on a complete (wikidump + common crawl) Latvian corpus, which has about 280 million tokens. 
 Question: How larger are the training sets of these versions of ELMo compared to the previous ones?
Output: By 14 times.
We compare our approaches with related approaches of pivoting, multilingual NMT (MNMT) BIBREF19, and cross-lingual transfer without pretraining BIBREF16. Although it is challenging for one model to translate all zero-shot directions between multiple distant language pairs of MultiUN, MLM+BRLM-SA still achieves better performances on Es $\rightarrow $ Ar and Es $\rightarrow $ Ru than strong pivoting$_{\rm m}$, which uses MNMT to translate source to pivot then to target in two separate steps with each step receiving supervised signal of parallel corpora.  
 Question: what are the pivot-based baselines?
Output: pivoting pivoting$_{\rm m}$
The participants are asked to score each summary on three indicators: relevance, conciseness and readability on a 1 to 5 scale, and rank the summary pair (tie allowed). We observe that SPNet reaches the highest score in both ROUGE and CIC 
 Question: What automatic and human evaluation metrics are used to compare SPNet to its counterparts?
Output: ROUGE and CIC relevance, conciseness and readability on a 1 to 5 scale, and rank the summary pair
In this part, we discuss three significant limitations of BLEU and ROUGE. These metrics can assign: High scores to semantically opposite translations/summaries, Low scores to semantically related translations/summaries and High scores to unintelligible translations/summaries.

Challenges with BLEU and ROUGE ::: High score, opposite meanings
Suppose that we have a reference summary s1. By adding a few negation terms to s1, one can create a summary s2 which is semantically opposite to s1 but yet has a high BLEU/ROUGE score.

Challenges with BLEU and ROUGE ::: Low score, similar meanings
In addition not to be sensitive to negation, BLEU and ROUGE score can give low scores to sentences with equivalent meaning. If s2 is a paraphrase of s1, the meaning will be the same ;however, the overlap between words in s1 and s2 will not necessarily be significant.

Challenges with BLEU and ROUGE ::: High score, unintelligible sentences
A third weakness of BLEU and ROUGE is that in their simplest implementations, they are insensitive to word permutation and can give very high scores to unintelligible sentences. Let s1 be "On a morning, I saw a man running in the street." and s2 be “On morning a, I saw the running a man street”. s2 is not an intelligible sentence. The unigram version of ROUGE and BLEU will give these 2 sentences a score of 1. 
 Question: What are the three limitations?
Output: High scores to semantically opposite translations/summaries, Low scores to semantically related translations/summaries and High scores to unintelligible translations/summaries.
Macaw also supports Wizard of Oz studies or intermediary-based information seeking studies. The architecture of Macaw for such setup is presented in FIGREF16. As shown in the figure, the seeker interacts with a real conversational interface that supports multi-modal and mixed-initiative interactions in multiple devices. The intermediary (or the wizard) receives the seeker's message and performs different information seeking actions with Macaw. All seeker-intermediary and intermediary-system interactions will be logged for further analysis. 
 Question: What is a wizard of oz setup?
Output: seeker interacts with a real conversational interface intermediary (or the wizard) receives the seeker's message and performs different information seeking actions
An emerging body of work in natural language processing and computational social science has investigated how NLP systems can detect moral sentiment in online text. For example, moral rhetoric in social media and political discourse BIBREF19, BIBREF20, BIBREF21, the relation between moralization in social media and violent protests BIBREF22, and bias toward refugees in talk radio shows BIBREF23 have been some of the topics explored in this line of inquiry. In contrast to this line of research, the development of a formal framework for moral sentiment change is still under-explored, with no existing systematic and formal treatment of this topic BIBREF16. 
 Question: Does the paper discuss previous models which have been applied to the same task?
Output: Yes
Following work by doukhan2018open, we wanted to explore the corpora looking at the number of speakers of each gender category as well as their speech duration, considering both variables as good features to account for gender representation.  
 Question: What representations are presented by this paper?
Output: the number of speakers of each gender category their speech duration
To evaluate model robustness, we devise a test set consisting of ‘adversarial’ examples, i.e, perturbed examples that can potentially change the base model's prediction. These could stem from paraphrasing a sentence, e.g., lexical and syntactical changes. We use two approaches described in literature: back-translation and noisy sequence autoencoder. Note that these examples resemble black-box attacks but are not intentionally designed to fool the system and hence, we use the term 'adversarial' broadly. We use these techniques to produce many paraphrases and find a subset of utterances that though very similar to the original test set, result in wrong predictions. We will measure the model robustness against such changes. 
 Question: How authors create adversarial test set to measure model robustness?
Output: we devise a test set consisting of ‘adversarial’ examples, i.e, perturbed examples that can potentially change the base model's prediction.  We use two approaches described in literature: back-translation and noisy sequence autoencoder.
Methods ::: Baselines ::: BiGRU s with attention:
This is very similar to Sum-QE but now $\mathcal {E}$ is a stack of BiGRU s with self-attention BIBREF21, instead of a BERT instance. The final summary representation ($h$) is the sum of the resulting context-aware token embeddings ($h = \sum _i a_i h_i$) weighted by their self-attention scores ($a_i$). We again have three flavors: one single-task (BiGRU-ATT-S-1) and two multi-task (BiGRU-ATT-M-1 and BiGRU-ATT-M-5).

Methods ::: Baselines ::: ROUGE:
This baseline is the ROUGE version that performs best on each dataset, among the versions considered by BIBREF13. Although ROUGE focuses on surface similarities between peer and reference summaries, we would expect properties like grammaticality, referential clarity and coherence to be captured to some extent by ROUGE versions based on long $n$-grams or longest common subsequences.

Methods ::: Baselines ::: Language model (LM):
For a peer summary, a reasonable estimate of $\mathcal {Q}1$ (Grammaticality) is the perplexity returned by a pre-trained language model. We experiment with the pre-trained GPT-2 model BIBREF22, and with the probability estimates that BERT can produce for each token when the token is treated as masked (BERT-FR-LM). Given that the grammaticality of a summary can be corrupted by just a few bad tokens, we compute the perplexity by considering only the $k$ worst (lowest LM probability) tokens of the peer summary, where $k$ is a tuned hyper-parameter.

Methods ::: Baselines ::: Next sentence prediction:
BERT training relies on two tasks: predicting masked tokens and next sentence prediction. The latter seems to be aligned with the definitions of $\mathcal {Q}3$ (Referential Clarity), $\mathcal {Q}4$ (Focus) and $\mathcal {Q}5$ (Structure & Coherence). Intuitively, when a sentence follows another with high probability, it should involve clear referential expressions and preserve the focus and local coherence of the text. We, therefore, use a pre-trained BERT model (BERT-FR-NS) to calculate the sentence-level perplexity of each summary:

where $p(s_i|s_{i-1})$ is the probability that BERT assigns to the sequence of sentences $\left< s_{i-1}, s \right>$, and $n$ is the number of sentences in the peer summary. 
 Question: What simpler models do they look at?
Output: BiGRU s with attention ROUGE Language model (LM) Next sentence prediction
For experiments that use parallel data to initialize foreign specific parameters, we use the same datasets in the work of BIBREF6. Specifically, we use United Nations Parallel Corpus BIBREF18 for en-ru, en-ar, en-zh, and en-fr. We collect en-hi parallel data from IIT Bombay corpus BIBREF19 and en-vi data from OpenSubtitles 2018. 
 Question: What datasets are used for evaluation?
Output: United Nations Parallel Corpus IIT Bombay corpus OpenSubtitles 2018
In the speaker-closed condition, two episodes were set aside from each speaker as development and test sets. Thereafter, the total sizes of the development and test sets turns out to be 1585 IPUs spanning 2 hours 23 minutes and 1841 IPUs spanning 2 hours and 48 minutes respectively. The ASR model is trained with the rest data. In the speaker-open condition, all the data except for the test speaker's were used for training As it would be difficult to train the model if all of the data of speaker KM or UT were removed, experiments using their speaker-open conditions were not conducted. 
 Question: What is the difference between speaker-open and speaker-closed setting?
Output: In the speaker-closed condition, two episodes were set aside from each speaker as development and test sets. In the speaker-open condition, all the data except for the test speaker's were used for training
Our model differs by learning the subword vectors and resulting representation jointly as weighted factorization of a word-context co-occurrence matrix is performed. 
 Question: Which matrix factorization methods do they use?
Output: weighted factorization of a word-context co-occurrence matrix
Figure FIGREF9 illustrates the overall architecture of the proposed Attentional Encoder Network (AEN), which mainly consists of an embedding layer, an attentional encoder layer, a target-specific attention layer, and an output layer. Embedding layer has two types: GloVe embedding and BERT embedding. Accordingly, the models are named AEN-GloVe and AEN-BERT. 
 Question: How is their model different from BERT?
Output: overall architecture of the proposed Attentional Encoder Network (AEN), which mainly consists of an embedding layer, an attentional encoder layer, a target-specific attention layer, and an output layer.
We draw on a recently released corpus of state speeches delivered during the annual UN General Debate that provides the first dataset of textual output from states that is recorded at regular time-series intervals and includes a sample of all countries that deliver speeches BIBREF11 .  
 Question: Which dataset do they use?
Output: corpus of state speeches delivered during the annual UN General Debate
All documents are segmented into paragraphs and processed at the paragraph level (both training and inference); this is acceptable because we observe that most paragraphs are less than 200 characters. The input sequences are segmented by the BERT tokenizer, with the special [CLS] token inserted at the beginning and the special [SEP] token added at the end. 
 Question: Was the structure of regulatory filings exploited when training the model? 
Output: No
Particularly, it is not clear whether BERT shed light on solving tasks such as the Pronoun Disambiguation Problem (PDP) and Winograd Schema Challenge (WSC). On a Pronoun Disambiguation dataset, PDP-60, our method achieves 68.3% accuracy, which is better than the state-of-art accuracy of 66.7%. On a WSC dataset, WSC-273, our method achieves 60.3%.  
 Question: Which datasets do they evaluate on?
Output: PDP-60 WSC-273
This step was followed by a pre-processing stage where the texts were normalized by replacing zero-width-non-joiner (ZWNJ) BIBREF2 and manually verifying the orthography based on the reference orthography of the Kurdistan Region of Iraq. 
 Question: How is the corpus normalized?
Output: by replacing zero-width-non-joiner (ZWNJ) BIBREF2 and manually verifying the orthography
We use the text corpora (S) from BIBREF6 aristo2016:combining to build our tuple KB.  Specifically, for each multiple-choice question $(q,A) \in Q_\mathit {tr}$ and each choice $a \in A$ , we use all non-stopword tokens in $q$ and $a$ as an ElasticSearch query against S. We take the top 200 hits, run Open IE v4, and aggregate the resulting tuples over all $a \in A$ and over all questions in $Q_\mathit {tr}$ to create the tuple KB (T). The Sentence corpus (S) consists of domain-targeted $~$ 80K sentences and 280 GB of plain text extracted from web pages used by BIBREF6 aristo2016:combining.  
 Question: What corpus was the source of the OpenIE extractions?
Output: domain-targeted $~$ 80K sentences and 280 GB of plain text extracted from web pages used by BIBREF6 aristo2016:combining
Each training sample is generated by three steps: align, mask and select, which we call as AMS method. Each sample in the dataset consists of a question and several candidate answers, which has the same form as the CommonsenseQA dataset. 
 Question: How do they select answer candidates for their QA task?
Output: AMS method.
The policies trained with the NUS achieved an average success rate (SR) of 94.0% and of 96.6% when tested on the ABUS and the NUS, respectively. 
 Question: by how much did nus outperform abus?
Output: Average success rate is higher by 2.6 percent points.
Deep learning has unquestionably advanced the state of the art in many natural language processing tasks, from syntactic dependency parsing BIBREF0 to named-entity recognition BIBREF1 to machine translation BIBREF2 . The same certainly applies to language modeling, where recent advances in neural language models (NLMs) have led to dramatically better approaches as measured using standard metrics such as perplexity BIBREF3 , BIBREF4 . 
 Question: What is a commonly used evaluation metric for language models?
Output: perplexity
CN-Celeb specially focuses on Chinese celebrities, and contains more than $130,000$ utterances from $1,000$ persons.

CN-Celeb covers more genres of speech. We intentionally collected data from 11 genres, including entertainment, interview, singing, play, movie, vlog, live broadcast, speech, drama, recitation and advertisement. The speech of a particular speaker may be in more than 5 genres. As a comparison, most of the utterances in VoxCeleb were extracted from interview videos. The diversity in genres makes our database more representative for the true scenarios in unconstrained conditions, but also more challenging. 
 Question: What kind of settings do the utterances come from?
Output: entertainment, interview, singing, play, movie, vlog, live broadcast, speech, drama, recitation and advertisement
It begins as ordinary tournament selection evolutionary architecture search with early stopping, with each child model training for a relatively small $s_0$ number of steps before being evaluated for fitness. However, after a predetermined number of child models, $m$ , have been evaluated, a hurdle, $h_0$ , is created by calculating the the mean fitness of the current population. For the next $m$ child models produced, models that achieve a fitness greater than $h_0$ after $s_0$ train steps are granted an additional $s_1$ steps of training and then are evaluated again to determine their final fitness. Once another $m$ models have been considered this way, another hurdle, $h_1$ , is constructed by calculating the mean fitness of all members of the current population that were trained for the maximum number of steps. For the next $m$ child models, training and evaluation continues in the same fashion, except models with fitness greater than $m$0 after $m$1 steps of training are granted an additional $m$2 number of train steps, before being evaluated for their final fitness. This process is repeated until a satisfactory number of maximum training steps is reached. 
 Question: How does Progressive Dynamic Hurdles work?
Output: It begins as ordinary tournament selection evolutionary architecture search with early stopping, with each child model training for a relatively small $s_0$ number of steps before being evaluated for fitness. However, after a predetermined number of child models, $m$ , have been evaluated, a hurdle, $h_0$ , is created by calculating the the mean fitness of the current population. For the next $m$ child models produced, models that achieve a fitness greater than $h_0$ after $s_0$ train steps are granted an additional $s_1$ steps of training and then are evaluated again to determine their final fitness. Once another $m$ models have been considered this way, another hurdle, $h_1$ , is constructed by calculating the mean fitness of all members of the current population that were trained for the maximum number of steps. For the next $m$ child models, training and evaluation continues in the same fashion, except models with fitness greater than $m$0 after $m$1 steps of training are granted an additional $m$2 number of train steps, before being evaluated for their final fitness. This process is repeated until a satisfactory number of maximum training steps is reached.
We use two datasets in this work: the training is done on the Fisher Corpus English Part 1 (LDC2004S13) BIBREF15 and testing on the Suicide Risk Assessment corpus BIBREF16 , along with Fisher. 
 Question: Which dataset do they use to learn embeddings?
Output: Fisher Corpus English Part 1
Our VG training set consists of 85% of the data: 16k images and 740k corresponding region descriptions. 
 Question: How big is data provided by this research?
Output: 16k images and 740k corresponding region descriptions
The top performing approaches Caravel, COAV and NNCD deserve closer attention. 
 Question: Which is the best performing method?
Output: Caravel, COAV and NNCD
Despite this, all models in the literature rely on word-level representations, which keeps the models from being able to easily capture some of the lexical and morpho-syntactic cues known to denote irony, such as all caps, quotation marks and emoticons, and in Twitter, also emojis and hashtags. 
 Question: Which morphosyntactic features are thought to indicate irony or sarcasm?
Output: all caps quotation marks emoticons emojis hashtags
When predicting anger, joy, or valence, the number of systems consistently giving higher scores to sentences with female noun phrases (21–25) is markedly higher than the number of systems giving higher scores to sentences with male noun phrases (8–13). (Recall that higher valence means more positive sentiment.) In contrast, on the fear task, most submissions tended to assign higher scores to sentences with male noun phrases (23) as compared to the number of systems giving higher scores to sentences with female noun phrases (12). When predicting sadness, the number of submissions that mostly assigned higher scores to sentences with female noun phrases (18) is close to the number of submissions that mostly assigned higher scores to sentences with male noun phrases (16). These results are in line with some common stereotypes, such as females are more emotional, and situations involving male agents are more fearful BIBREF27  The majority of the systems assigned higher scores to sentences with African American names on the tasks of anger, fear, and sadness intensity prediction. On the joy and valence tasks, most submissions tended to assign higher scores to sentences with European American names. These tendencies reflect some common stereotypes that associate African Americans with more negative emotions BIBREF28 . 
 Question: Which race and gender are given higher sentiment intensity predictions?
Output: Females are given higher sentiment intensity when predicting anger, joy or valence, but males are given higher sentiment intensity when predicting  fear.
African American names are given higher score on the tasks of anger, fear, and sadness intensity prediction,  but European American names are given higher scores on joy and valence task.
Span detector. We adopt a multi-turn answer module for the span detector BIBREF1 . Formally, at time step INLINEFORM0 in the range of INLINEFORM1 , the state is defined by INLINEFORM2 . The initial state INLINEFORM3 is the summary of the INLINEFORM4 : INLINEFORM5 , where INLINEFORM6 . Here, INLINEFORM7 is computed from the previous state INLINEFORM8 and memory INLINEFORM9 : INLINEFORM10 and INLINEFORM11 . Finally, a bilinear function is used to find the begin and end point of answer spans at each reasoning step INLINEFORM12 : DISPLAYFORM0 DISPLAYFORM1

The final prediction is the average of each time step: INLINEFORM0 . We randomly apply dropout on the step level in each time step during training, as done in BIBREF1 . 
 Question: What is the architecture of the span detector?
Output: adopt a multi-turn answer module for the span detector BIBREF1
The CreateDebate dataset was collected from an English online debate forum discussing four topics: abortion (ABO), gay rights (GAY), Obama (OBA), and marijuana (MAR).  
 Question: What topics are included in the debate data?
Output: abortion gay rights Obama marijuana
We build on the state-of-the-art publicly available question answering system by docqa. The system extends BiDAF BIBREF4 with self-attention and performs well on document-level QA.  
 Question: What is the underlying question answering algorithm?
Output: The system extends BiDAF BIBREF4 with self-attention
A simple choice of non-sparse gating function BIBREF17 is to multiply the input by a trainable weight matrix INLINEFORM0 and then apply the INLINEFORM1 function. DISPLAYFORM0

We add two components to the Softmax gating network: sparsity and noise. Before taking the softmax function, we add tunable Gaussian noise, then keep only the top k values, setting the rest to INLINEFORM0 (which causes the corresponding gate values to equal 0). The sparsity serves to save computation, as described above. While this form of sparsity creates some theoretically scary discontinuities in the output of gating function, we have not yet observed this to be a problem in practice. The noise term helps with load balancing, as will be discussed in Appendix SECREF51 . The amount of noise per component is controlled by a second trainable weight matrix INLINEFORM1 . DISPLAYFORM0 DISPLAYFORM1 A simple choice of non-sparse gating function BIBREF17 is to multiply the input by a trainable weight matrix INLINEFORM0 and then apply the INLINEFORM1 function. DISPLAYFORM0

We add two components to the Softmax gating network: sparsity and noise. Before taking the softmax function, we add tunable Gaussian noise, then keep only the top k values, setting the rest to INLINEFORM0 (which causes the corresponding gate values to equal 0). The sparsity serves to save computation, as described above. While this form of sparsity creates some theoretically scary discontinuities in the output of gating function, we have not yet observed this to be a problem in practice. The noise term helps with load balancing, as will be discussed in Appendix SECREF51 . The amount of noise per component is controlled by a second trainable weight matrix INLINEFORM1 . DISPLAYFORM0 DISPLAYFORM1 
 Question: What equations are used for the trainable gating network?
Output: DISPLAYFORM0 DISPLAYFORM0 DISPLAYFORM1
We conduct experiments on the SQuAD dataset BIBREF3. 
 Question: On what datasets are experiments performed?
Output: SQuAD
In addition, we have another independent human evaluation task about the style strength – we present the generated headlines from TitleStylist and baselines to the human judges and let them choose the one that most conforms to the target style such as humor. Then we define the style strength score as the proportion of choices. 
 Question: How is presence of three target styles detected?
Output: human evaluation task about the style strength
These unstructured features include: 1) the relative number of sentences in the admission notes that involve each risk factor domain (out of total number of sentences within the admission) and 2) clinical sentiment scores for each of these risk factor domains, i.e. sentiment scores that evaluate the patient’s psychosocial functioning level (positive, negative, or neutral) with respect to each of these risk factor domain.

These sentiment scores were automatically obtained through the topic extraction and sentiment analysis pipeline introduced in our prior work BIBREF15 and pretrained on in-house psychiatric EHR text. 45 clinically interpretable features per admission were extracted as inputs to the readmission risk classifier. 
 Question: How do they incorporate sentiment analysis?
Output: features per admission were extracted as inputs to the readmission risk classifier
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). 
 Question: How are prominent topics idenified in Dabiq and Rumiyah?
Output: LDA non-negative matrix factorization (NMF)
Maximum Entropy theory is applied to solve Vietnamese word segmentation BIBREF15 , BIBREF22 , BIBREF23 . There are several studies about the Vietnamese word segmentation task over the last decade. Dinh et al. started this task with Weighted Finite State Transducer (WFST) approach and Neural Network approach BIBREF9 . In addition, machine learning approaches are studied and widely applied to natural language processing and word segmentation as well. In fact, several studies used support vector machines (SVM) and conditional random fields (CRF) for the word segmentation task BIBREF7 , BIBREF8 . 
 Question: Which approaches have been applied to solve word segmentation in Vietnamese?
Output: Maximum Entropy Weighted Finite State Transducer (WFST)  support vector machines (SVM) conditional random fields (CRF)
Excluding the embedding weights, our model requires 100k parameters , while BIBREF8 requires 250k parameters. 
 Question: how many parameters did their model use?
Output: Excluding the embedding weights, our model requires 100k parameters
We extract user's age by applying regular expression patterns to profile descriptions (such as "17 years old, self-harm, anxiety, depression") BIBREF41 . We compile "age prefixes" and "age suffixes", and use three age-extraction rules: 1. I am X years old 2. Born in X 3. X years old, where X is a "date" or age (e.g., 1994). We selected a subset of 1464 users INLINEFORM0 from INLINEFORM1 who disclose their gender in their profile description. 
 Question: Where does the information on individual-level demographics come from?
Output: From Twitter profile descriptions of the users.
We use the MedWeb (“Medical Natural Language Processing for Web Document”) dataset BIBREF4 that was provided as part of a subtask at the NTCIR-13 Conference BIBREF5. The data is summarised in Table TABREF1. There are a total of 2,560 pseudo-tweets in three different languages: Japanese (ja), English (en) and Chinese (zh). 
 Question: How big is dataset used for fine-tuning model for detection of red flag medical symptoms in individual statements?
Output: a total of 2,560 pseudo-tweets in three different languages: Japanese (ja), English (en) and Chinese (zh)
The corpus consists of a collection of 2,346 clinical notes (admission notes, progress notes, and discharge summaries), which amounts to 2,372,323 tokens in total (an average of 1,011 tokens per note). All the notes were written in English and extracted from the EHRs of 183 psychosis patients from McLean Psychiatric Hospital in Belmont, MA, all of whom had in their history at least one instance of 30-day readmission. 
 Question: What is the dataset used?
Output: EHRs of 183 psychosis patients from McLean Psychiatric Hospital in Belmont, MA
Consequently, our architecture is based on Embeddings from Language Model or ELMo BIBREF10 Concretely, we use a pre-trained ELMo model, obtained using the 1 Billion Word Benchmark which contains about 800M tokens of news crawl data from WMT 2011 BIBREF24 . Subsequently, the contextualized embeddings are passed on to a BiLSTM with 2,048 hidden units. We aggregate the LSTM hidden states using max-pooling, which in our preliminary experiments offered us better results, and feed the resulting vector to a 2-layer feed-forward network, where each layer has 512 units. The output of this is then fed to the final layer of the model, which performs the binary classification. 
 Question: What type of model are the ELMo representations used in?
Output: A bi-LSTM with max-pooling on top of it
In the VQABQ model, there are two main modules, the basic question generation module (Module 1) and co-attention visual question answering module (Module 2).  
 Question: What two main modules their approach consists of?
Output: the basic question generation module (Module 1) and co-attention visual question answering module (Module 2)
Most lexical resources for sentiment analysis are in English. To still be able to benefit from these sources, the lexicons in the AffectiveTweets package were translated to Spanish, using the machine translation platform Apertium BIBREF5  
 Question: How was the training data translated?
Output: using the machine translation platform Apertium