niryuu/sni-each-converted huggingface dataset:
To select the most appropriate sentences in a large number of unlabeled corpora, we propose a scoring model based on information entropy and neural network as the sampling strategy of active learning, which is inspired by Cai and Zhao BIBREF32 . The score of a segmented sentence is computed as follows. First, mapping the segmented sentence to a sequence of candidate word embeddings. Then, the scoring model takes the word embedding sequence as input, scoring over each individual candidate word from two perspectives: (1) the possibility that the candidate word itself can be regarded as a legal word; (2) the rationality of the link that the candidate word directly follows previous segmentation history. Fig. FIGREF10 illustrates the entire scoring model. A gated neural network is employed over character embeddings to generate distributed representations of candidate words, which are sent to a LSTM model. 
 Question: How does the scoring model work?
Output: First, mapping the segmented sentence to a sequence of candidate word embeddings. Then, the scoring model takes the word embedding sequence as input, scoring over each individual candidate word
The model is composed of an encoder (§SECREF5) and a decoder with the attention mechanism (§SECREF7) that are both implemented using recurrent neural networks (RNNs); the encoder converts source words into a sequence of vectors, and the decoder generates target language words one-by-one with the attention mechanism based on the conditional probability shown in the equation DISPLAY_FORM2 and DISPLAY_FORM3. 
 Question: Which model architecture do they use to build a model?
Output: model is composed of an encoder (§SECREF5) and a decoder with the attention mechanism (§SECREF7) that are both implemented using recurrent neural networks (RNNs)
We consider QA PGNet and Multi-decoder QA PGNet with lookup table embedding as baseline models and improve on the baselines with other variations described below. 
 Question: What is the baseline?
Output: QA PGNet Multi-decoder QA PGNet with lookup table embedding
We consider two different models for each language pair: the Baseline and the Document model. We evaluate them on 3 test sets and report BLEU and TER scores. All experiments are run 8 times with different seeds, we report averaged results and p-values for each experiment. 
 Question: What evaluation metrics did they use?
Output: BLEU and TER scores
We published a survey on Reddit asking Danish speaking users to suggest offensive, sexist, and racist terms for a lexicon. Language and user behaviour varies between platforms, so the goal is to capture platform-specific terms. This gave 113 offensive and hateful terms which were used to find offensive comments. The remainder of comments in the corpus were shuffled and a subset of this corpus was then used to fill the remainder of the final dataset. The resulting dataset contains 3600 user-generated comments, 800 from Ekstra Bladet on Facebook, 1400 from r/DANMAG and 1400 from r/Denmark. 
 Question: How large was the dataset of Danish comments?
Output: 3600 user-generated comments
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.  
 Question: Do they use large or small BERT?
Output: small BERT
Our experiments are run using the English$\rightarrow $Italian/German portions of the MuST-C corpus BIBREF25, which is extracted from TED talks, using the same train/validation/test split as provided with the corpus (see Table TABREF18). As additional data, we use a mix of public and proprietary data for about 16 million sentence pairs for English-Italian (En-It) and $4.4$ million WMT14 sentence pairs for the English-German (En-De). 
 Question: What dataset do they use for experiments?
Output: English$\rightarrow $Italian/German portions of the MuST-C corpus As additional data, we use a mix of public and proprietary data for about 16 million sentence pairs for English-Italian (En-It) and $4.4$ million WMT14 sentence pairs for the English-German (En-De)
The training dataset contains 2,815 examples, where 1,940 (i.e., 69%) are fake news and 1,968 (i.e., 70%) are click-baits; we further have 761 testing examples. 
 Question: what datasets were used?
Output:  training dataset contains 2,815 examples 761 testing examples
On the other hand, Go-Explore Seq2Seq shows promising results by solving almost half of the unseen games. Figure FIGREF62 (in Appendix SECREF60) shows that most of the lost games are in the hardest set, where a very long sequence of actions is required for winning the game. These results demonstrate both the relative effectiveness of training a Seq2Seq model on Go-Explore trajectories, but they also indicate that additional effort needed for designing reinforcement learning algorithms that effectively generalize to unseen games. 
 Question: How do the authors show that their learned policy generalize better than existing solutions to unseen games?
Output: promising results by solving almost half of the unseen games most of the lost games are in the hardest set, where a very long sequence of actions is required for winning the game
In order to understand what may be happening in the model, we used the body and punchline only datasets to see what part of the joke was most important for humor. We found that all of the models, including humans, relied more on the punchline of the joke in their predictions (Table 2). Thus, it seems that although both parts of the joke are needed for it to be humorous, the punchline carries higher weight than the body. We hypothesize that this is due to the variations found in the different joke bodies: some take paragraphs to set up the joke, while others are less than a sentence. 
 Question: Which part of the joke is more important in humor?
Output: the punchline of the joke
Comparing these topics with those that appeared on a Catholic women forum, it seems that both ISIS and non-violent groups use topics about motherhood, spousal relationship, and marriage/divorce when they address women. Moreover, we used Depechemood methods to analyze the emotions that these materials are likely to elicit in readers. The result of our emotion analysis suggests that both corpuses used words that aim to inspire readers while avoiding fear. However, the actual words that lead to these effects are very different in the two contexts. Overall, our findings indicate that, using proper methods, automated analysis of large bodies of textual data can provide novel insight insight into extremist propaganda that can assist the counterterrorism community. 
 Question: What conclusions do the authors draw from their finding that the emotional appeal of ISIS and Catholic materials are similar?
Output: both corpuses used words that aim to inspire readers while avoiding fear actual words that lead to these effects are very different in the two contexts our findings indicate that, using proper methods, automated analysis of large bodies of textual data can provide novel insight insight into extremist propaganda
The UCL team had access to micro-narratives, as well as context specific meta-data such as demographic information and project details. For a cross-national comparison for policy-makers, the team translated the responses in multiple languages into English using machine translation, in this case Translate API (Yandex Technologies). As a pre-processing step, words without functional meaning (e.g. `I'), rare words that occurred in only one narrative, numbers, and punctuation were all removed. The remaining words were stemmed to remove plural forms of nouns or conjugations of verbs. 
 Question: What elements of natural language processing are proposed to analyze qualitative data?
Output: translated the responses in multiple languages into English using machine translation words without functional meaning (e.g. `I'), rare words that occurred in only one narrative, numbers, and punctuation were all removed remaining words were stemmed to remove plural forms of nouns or conjugations of verbs
We achieved first place for 8 directions: German$\leftrightarrow $English, German$\leftrightarrow $French, Chinese$\leftrightarrow $English, English$\rightarrow $Lithuanian, English$\rightarrow $Finnish, and Russian$\rightarrow $English, and three other directions were placed second (ranked by teams), which included Lithuanian$\rightarrow $English, Finnish$\rightarrow $English, and English$\rightarrow $Kazakh. 
 Question: Which language directions are machine translation systems of WMT evaluated on?
Output: German$\leftrightarrow $English, German$\leftrightarrow $French, Chinese$\leftrightarrow $English, English$\rightarrow $Lithuanian, English$\rightarrow $Finnish, and Russian$\rightarrow $English Lithuanian$\rightarrow $English, Finnish$\rightarrow $English, and English$\rightarrow $Kazakh
Deeply moved readers shed tears or get chills and goosebumps even in lab settings BIBREF4. In cases like these, the emotional response actually implies an aesthetic evaluation: narratives that have the capacity to move readers are evaluated as good and powerful texts for this very reason. Similarly, feelings of suspense experienced in narratives not only respond to the trajectory of the plot's content, but are also directly predictive of aesthetic liking (or disliking). Emotions that exhibit this dual capacity have been defined as “aesthetic emotions” BIBREF2. 
 Question: What are the aesthetic emotions formalized?
Output: feelings of suspense experienced in narratives not only respond to the trajectory of the plot's content, but are also directly predictive of aesthetic liking (or disliking) Emotions that exhibit this dual capacity have been defined as “aesthetic emotions”
In this paper, we propose three different types of CRU models: shallow fusion, deep fusion and deep-enhanced fusion, from the most fundamental one to the most expressive one. The most simple one is to directly apply a CNN layer after the embedding layer to obtain blended contextual representations. Then a GRU layer is applied afterward. 
 Question: How is CNN injected into recurent units?
Output: The most simple one is to directly apply a CNN layer after the embedding layer to obtain blended contextual representations. Then a GRU layer is applied afterward.
Our experiments are performed on an actual endangered language, Mboshi (Bantu C25), a language spoken in Congo-Brazzaville, using the bilingual French-Mboshi 5K corpus of BIBREF17. 
 Question: Which language family does Mboshi belong to?
Output: Bantu
We manually reviewed 1,177 pairs of entities and referring expressions generated by the system. Overall, 73.3% of the non-first mentions of entities were replaced with suitable shorter and more fluent expressions. 
 Question: How is fluency of generated text evaluated?
Output: manually reviewed
Recently bhat-EtAl:2017:EACLshort provided a CS dataset for the evaluation of their parsing models which they trained on the Hindi and English Universal Dependency (UD) treebanks. We extend this dataset by annotating 1,448 more sentences. 
 Question: How big is the provided treebank?
Output: 1448 sentences more than the dataset from Bhat et al., 2017
To find a large amount of hate speech on the refugee crisis, we used 10 hashtags that can be used in an insulting or offensive way. 
 Question: How were potentially hateful messages identified?
Output: 10 hashtags that can be used in an insulting or offensive way
The ratio of correct `translations' (matches) was used as an evaluation measure. 
 Question: What evaluation metric do they use?
Output: Accuracy
We conducted our experiments on the CSJ BIBREF25, which is one of the most widely used evaluation sets for Japanese speech recognition. The CSJ consists of more than 600 hrs of Japanese recordings.

While most of the content is lecture recordings by a single speaker, CSJ also contains 11.5 hrs of 54 dialogue recordings (average 12.8 min per recording) with two speakers, which were the main target of ASR and speaker diarization in this study. 
 Question: How long are dialogue recordings used for evaluation?
Output: average 12.8 min per recording
We evalute Bertram on the WNLaMPro dataset of BIBREF0. 
 Question: What is dataset for word probing task?
Output: WNLaMPro dataset
n this work, we apply an automated neural ensemble annotation process for dialogue act labeling. Several neural models are trained with the Switchboard Dialogue Act (SwDA) Corpus BIBREF9, BIBREF10 and used for inferring dialogue acts on the emotion datasets. We ensemble five model output labels by checking majority occurrences (most of the model labels are the same) and ranking confidence values of the models. We adopted the neural architectures based on Bothe et al. bothe2018discourse where two variants are: non-context model (classifying at utterance level) and context model (recognizing the dialogue act of the current utterance given a few preceding utterances). 
 Question: How many models were used?
Output: five
To the best of our knowledge, the only scientific summarization benchmark is from TAC 2014 summarization track. For evaluating the effectiveness of Rouge variants and our metric (Sera), we use this benchmark, which consists of 20 topics each with a biomedical journal article and 4 gold human written summaries. Consider the following example:

Endogeneous small RNAs (miRNA) were genetically screened and studied to find the miRNAs which are related to tumorigenesis. 
 Question: Do the authors report results only on English data?
Output: Yes
We train language models on two languages: One model is estimated on the German newscrawl distributed by WMT'18 comprising 260M sentences or 6B tokens. Another model is trained on the English newscrawl data comprising 193M sentences or 5B tokens. We learn a joint Byte-Pair-Encoding (BPE; Sennrich et al., 2016) vocabulary of 37K types on the German and English newscrawl and train the language models with this vocabulary. We consider two benchmarks: Most experiments are run on the WMT'18 English-German (en-de) news translation task and we validate our findings on the WMT'18 English-Turkish (en-tr) news task. For WMT'18 English-German, the training corpus consists of all available bitext excluding the ParaCrawl corpus and we remove sentences longer than 250 tokens as well as sentence-pairs with a source/target length ratio exceeding 1.5. This results in 5.18M sentence pairs. We tokenize all data with the Moses tokenizer BIBREF8 and apply the BPE vocabulary learned on the monolingual corpora. 
 Question: What dataset do they use?
Output: German newscrawl distributed by WMT'18  English newscrawl data WMT'18 English-German (en-de) news translation task  WMT'18 English-Turkish (en-tr) news task
In a nutshell, B4MSA starts by applying text-transformations to the messages, then transformed text is represented in a vector space model (see Subsection SECREF13 ), and finally, a Support Vector Machine (with linear kernel) is used as the classifier. 
 Question: What are the components of the multilingual framework?
Output: text-transformations to the messages vector space model Support Vector Machine
To increase the variety of corpus, we selected 4 English corpora and 4 Mandarin corpora in addition to the low resource language corpora. 
 Question: Do they test their approach on large-resource tasks?
Output: Yes
The ensembles were formed by simply averaging the predictions from the constituent single models. 
 Question: How does their ensemble method work?
Output: simply averaging the predictions from the constituent single models
The BLEU metric is adopted to evaluate the model performance during evaluation. 
 Question: What evaluation metric is used?
Output: The BLEU metric
If we re-train the skip-gram model and update the emoji map periodically on new escort ads, when traffickers switch to new emojis, the map can link the new emojis to the old ones, assisting anti-trafficking experts in expanding the lexicon of trafficking flags. This approach also works for acronyms and deliberate typos. 
 Question: How is the lexicon of trafficking flags expanded?
Output: re-train the skip-gram model and update the emoji map periodically on new escort ads, when traffickers switch to new emojis, the map can link the new emojis to the old ones
The neural machine translation was trained using Nematus. For the NMT system as well as for the PreMT system, we used the default configuration. 
 Question: Which NMT architecture do they use?
Output: trained using Nematus default configuration
We compare the performance of our model (Table 2 ) with traditional Bag of Words (BoW), TF-IDF, and n-grams features based classifiers. We also compare against averaged Skip-Gram BIBREF29 , Doc2Vec BIBREF30 , CNN BIBREF23 , Hierarchical Attention (HN-ATT) BIBREF24 and hierarchical network (HN) models. HN it is similar to our model HN-SA but without any self attention. We further investigated the performance on SWBD2 by examining the confusion matrix of the model. Figure 2 shows the heatmap of the normalized confusion matrix of the model on SWBD2. 
 Question: Do the authors do manual evaluation?
Output: No
In order to validate the proposed classification model in real crowdsourcing settings, Amazon Mechanical Turk (AMT) was used to obtain labels from multiple annotators for two popular datasets: Reuters-21578 BIBREF30 and LabelMe BIBREF31 . In order to first validate the proposed model for classification problems in a slightly more controlled environment, the well-known 20-Newsgroups benchmark corpus BIBREF29 was used by simulating multiple annotators with different levels of expertise.  
 Question: what datasets were used?
Output: Reuters-21578 BIBREF30  LabelMe BIBREF31 20-Newsgroups benchmark corpus BIBREF29
We have downloaded 1,873 Twitter conversation threads, roughly 14k tweets, from a publicly available resource that were previously pre-processed and have conversation threads extracted. 
 Question: How large is the Twitter dataset?
Output: 1,873 Twitter conversation threads, roughly 14k tweets
As a raw corpus, we used a Japanese web corpus that was compiled through the procedures proposed by BIBREF13.  From the corpus of about 100 million sentences, we obtained 1.4 millions event pairs for AL, 41 millions for CA, and 6 millions for CO. 
 Question: How large is raw corpus used for training?
Output: 100 million sentences
For the cross-domain polarity classification experiments, we use the second version of Multi-Domain Sentiment Dataset BIBREF0 . The data set contains Amazon product reviews of four different domains: Books (B), DVDs (D), Electronics (E) and Kitchen appliances (K).  
 Question: What domains are contained in the polarity classification dataset?
Output: Books DVDs Electronics Kitchen appliances
We use word2vec BIBREF0 to train the word embeddings. 
 Question: What kind of model do they build to expand abbreviations?
Output: word2vec BIBREF0
For the 2019 Workshop on Neural Generation of Text (WNGT) Efficiency shared task BIBREF0, the Notre Dame Natural Language Processing (NDNLP) group looked at a method of inducing sparsity in parameters called auto-sizing in order to reduce the number of parameters in the Transformer at the cost of a relatively minimal drop in performance. 
 Question: What is WNGT 2019 shared task?
Output: efficiency task aimed  at reducing the number of parameters while minimizing drop in performance
The stance towards vaccination was categorized into `Negative’, `Neutral’, `Positive’ and `Not clear’. 
 Question: Do they allow for messages with vaccination-related key terms to be of neutral stance?
Output: Yes
BERT: We implement two BERT-based baselines BIBREF51 for evidence identification. First, we train BERT on each query-policy sentence pair as a binary classification task to identify if the sentence is evidence for the question or not (Bert). We also experiment with a two-stage classifier, where we separately train the model on questions only to predict answerability. At inference time, if the answerable classifier predicts the question is answerable, the evidence identification classifier produces a set of candidate sentences (Bert + Unanswerable).

 
 Question: What type of neural model was used?
Output: Bert + Unanswerable
Although FAR is supposed to be favored as FAMs are already manually labeled and tell exactly if one sentence should be extracted (assuming our annotations are in agreement), to further verify that FAR correlates with human preference, we rank UnifiedSum(E), NeuSum, and Lead-3 in Table TABREF15. The order of the 1st rank in the human evaluation coincides with FAR. FAR also has higher Spearman's coefficient $\rho $ than ROUGE (0.457 vs. 0.44, n=30, threshold=0.362 at 95% significance). 
 Question: How do they evaluate their proposed metric?
Output: manually labeled and tell exactly if one sentence should be extracted (assuming our annotations are in agreement), to further verify that FAR correlates with human preference,
The respondents in our MTurk survey had most difficulties recognizing reviews of category $(b=0.3, \lambda=-5)$, where true positive rate was $40.4\%$, while the true negative rate of the real class was $62.7\%$. The precision were $16\%$ and $86\%$, respectively. The class-averaged F-score is $47.6\%$, which is close to random. Detailed classification reports are shown in Table~\ref{table:MTurk_sub} in Appendix. Our MTurk-study shows that \emph{our NMT-Fake reviews pose a significant threat to review systems}, since \emph{ordinary native English-speakers have very big difficulties in separating real reviews from fake reviews}. We use the review category $(b=0.3, \lambda=-5)$ for future user tests in this paper, since MTurk participants had most difficulties detecting these reviews. We refer to this category as NMT-Fake* in this paper. The classifier is very effective in detecting reviews that humans have difficulties detecting. For example, the fake reviews MTurk users had most difficulty detecting ($b=0.3, \lambda=-5$) are detected with an excellent 97\% F-score. 
 Question: Does their detection tool work better than human detection?
Output: Yes
As a framework, we use the sentence classifier configuration of FLAIR BIBREF46 with a biLSTM encoder/classifier architecture fed by character and word level representations composed of a concatenation of fixed 300 dimensional GloVe embeddings BIBREF47, pre-trained contextualized FLAIR word embeddings, and pre-trained contextualized character embeddings from AllenNLP BIBREF48 with FLAIR's default hyperparameters. 
 Question: Are some models evaluated using this metric, what are the findings?
Output: Yes
Conditional Random Fields (CRF) BIBREF15 have been extensively used for tasks of sequential nature. In this paper, we propose as one of the competitive baselines a CRF classifier trained with sklearn-crfsuite for Python 3.5 and the following configuration: algorithm = lbfgs; maximum iterations = 100; c1 = c2 = 0.1; all transitions = true; optimise = false. spaCy is a widely used NLP library that implements state-of-the-art text processing pipelines, including a sequence-labelling pipeline similar to the one described by strubell2017fast. spaCy offers several pre-trained models in Spanish, which perform basic NLP tasks such as Named Entity Recognition (NER). In this paper, we have trained a new NER model to detect NUBes-PHI labels. As the simplest baseline, a sensitive data recogniser and classifier has been developed that consists of regular-expressions and dictionary look-ups. For each category to detect a specific method has been implemented. For instance, the Date, Age, Time and Doctor detectors are based on regular-expressions; Hospital, Sex, Kinship, Location, Patient and Job are looked up in dictionaries. The dictionaries are hand-crafted from the training data available, except for the Patient's case, for which the possible candidates considered are the 100 most common female and male names in Spain according to the Instituto Nacional de Estadística (INE; Spanish Statistical Office). 
 Question: What are the other algorithms tested?
Output: NER model CRF classifier trained with sklearn-crfsuite classifier has been developed that consists of regular-expressions and dictionary look-up
In this section, we describe the data augmentation methods we use to increase the amount of training data in order to make our NMT systems suffer less from the low-resourced situation in Japanese INLINEFORM0 Vietnamese translation. Back Translation
One of the approaches to leverage the monolingual data is to use a machine translation system to translate those data in order to create a synthetic parallel data. Normally, the monolingual data in the target language is translated, thus the name of the method: Back Translation BIBREF11 . Mix-Source Approach
Another data augmentation method considered useful in this low-resourced setting is the mix-source method BIBREF12 . 
 Question: what methods were used to reduce data sparsity effects?
Output: Back Translation Mix-Source Approach
We collected COVID-19 related Arabic tweets from January 1, 2020 until April 15, 2020, using Twitter streaming API and the Tweepy Python library.  
 Question: Over what period of time were the tweets collected?
Output: from January 1, 2020 until April 15, 2020
Through the experiments, we empirically studied our analysis on DIRL and the effectiveness of our proposed solution in dealing with the problem it suffered from. In addition, we studied the impact of each step described in §SECREF10 and §SECREF14 to our proposed solution, respectively. To performe the study, we carried out performance comparison between the following models:

SO: the source-only model trained using source domain labeled data without any domain adaptation.

CMD: the centre-momentum-based domain adaptation model BIBREF3 of the original DIRL framework that implements $\mathcal {L}_{inv}$ with $\text{CMD}_K$.

DANN: the adversarial-learning-based domain adaptation model BIBREF2 of the original DIRL framework that implements $\mathcal {L}_{inv}$ with $\text{JSD}(\rm {P}_S, \rm {P}_T)$.

$\text{CMD}^\dagger $: the weighted version of the CMD model that only applies the first step (described in §SECREF10) of our proposed method.

$\text{DANN}^\dagger $: the weighted version of the DANN model that only applies the first step of our proposed method.

$\text{CMD}^{\dagger \dagger }$: the weighted version of the CMD model that applies both the first and second (described in §SECREF14) steps of our proposed method.

$\text{DANN}^{\dagger \dagger }$: the weighted version of the DANN model that applies both the first and second steps of our proposed method.

$\text{CMD}^{*}$: a variant of $\text{CMD}^{\dagger \dagger }$ that assigns $\mathbf {w}^*$ (estimate from target labeled data) to $\mathbf {w}$ and fixes this value during model training.

$\text{DANN}^{*}$: a variant of $\text{DANN}^{\dagger \dagger }$ that assigns $\mathbf {w}^*$ to $\mathbf {w}$ and fixes this value during model training. We conducted experiments on the Amazon reviews dataset BIBREF9, which is a benchmark dataset in the cross-domain sentiment analysis field.  From this dataset, we constructed 12 binary-class cross-domain sentiment analysis tasks: B$\rightarrow $D, B$\rightarrow $E, B$\rightarrow $K, D$\rightarrow $B, D$\rightarrow $E, D$\rightarrow $K, E$\rightarrow $B, E$\rightarrow $D, E$\rightarrow $K, K$\rightarrow $B, K$\rightarrow $D, K$\rightarrow $E.  We additionally constructed 12 multi-class cross-domain sentiment classification tasks. Tasks were designed to distinguish reviews of 1 or 2 stars (class 1) from those of 4 stars (class 2) and those of 5 stars (class 3).  Table TABREF27 shows model performance on the 12 binary-class cross-domain tasks. From this table, we can obtain the following observations.  
 Question: How is DIRL evaluated?
Output: Through the experiments, we empirically studied our analysis on DIRL and the effectiveness of our proposed solution in dealing with the problem it suffered from.
For MSA, we acquired the diacritized corpus that was used to train the RDI BIBREF7 diacritizer and the Farasa diacritizer BIBREF31. The corpus contains 9.7M tokens with approximately 194K unique surface forms (excluding numbers and punctuation marks). For testing, we used the freely available WikiNews test set BIBREF31, which is composed of 70 MSA WikiNews articles (18,300 tokens) and evenly covers a variety of genres including politics, economics, health, science and technology, sports, arts and culture. For CA, we obtained a large collection of fully diacritized classical texts (2.7M tokens) from a book publisher, and we held-out a small subset of 5,000 sentences (approximately 400k words) for testing. 
 Question: what datasets were used?
Output: diacritized corpus that was used to train the RDI BIBREF7 diacritizer and the Farasa diacritizer BIBREF31 WikiNews test set BIBREF31  large collection of fully diacritized classical texts (2.7M tokens) from a book publisher
We design the word-level refine decoder because this process is similar to the cloze task in BERT's pre-train process, therefore by using the ability of the contextual language model the decoder can generate more fluent and natural sequences. 
 Question: Why masking words in the decoder is helpful?
Output: ecause this process is similar to the cloze task in BERT's pre-train process, therefore by using the ability of the contextual language model the decoder can generate more fluent and natural sequences.
We quantify the efficiency-accuracy tradeoff compared to two rule-based baselines: Unif and Stopword.  
 Question: What are the baselines used?
Output: Unif and Stopword
Yet, it is possible that the precise interpretability scores that are measured here are biased by the dataset used. However, it should be noted that the change in the interpretability scores for different word coverages might be effected by non-ideal subsampling of category words. Although our word sampling method, based on words' distances to category centers, is expected to generate categories that are represented better compared to random sampling of category words, category representations might be suboptimal compared to human designed categories. 
 Question: What are the weaknesses of their proposed interpretability quantification method?
Output: can be biased by dataset used and may generate categories which are suboptimal compared to human designed categories
In order to represent individual sentences, we insert external [cls] tokens at the start of each sentence, and each [cls] symbol collects features for the sentence preceding it. We also use interval segment embeddings to distinguish multiple sentences within a document. This way, document representations are learned hierarchically where lower Transformer layers represent adjacent sentences, while higher layers, in combination with self-attention, represent multi-sentence discourse. Position embeddings in the original Bert model have a maximum length of 512; we overcome this limitation by adding more position embeddings that are initialized randomly and fine-tuned with other parameters in the encoder. 
 Question: What is novel about their document-level encoder?
Output: Bert model have a maximum length of 512; we overcome this limitation by adding more position embeddings we insert external [cls] tokens at the start of each sentence, and each [cls] symbol collects features for the sentence preceding it document representations are learned hierarchically
Experiments ::: Evaluation Metrics ::: Setup of Automatic Evaluation ::: Language Fluency
We fine-tuned the GPT-2 medium model BIBREF51 on our collected headlines and then used it to measure the perplexity (PPL) on the generated outputs. 
 Question: How is fluency automatically evaluated?
Output: fine-tuned the GPT-2 medium model BIBREF51 on our collected headlines and then used it to measure the perplexity (PPL) on the generated outputs
From the description of music genres provided above emerges that there is a limited number of super-genres and derivation lines BIBREF19, BIBREF20, as shown in figure FIGREF1.

From a computational perspective, genres are classes and, although can be treated by machine learning algorithms, they do not include information about the relations between them. In order to formalize the relations between genres for computing purposes, we define a continuous genre scale from the most experimental and introverted super-genre to the most euphoric and inclusive one. We selected from Wikipedia the 77 genres that we mentioned in bold in the previous paragraph and asked to two independent raters to read the Wikipedia pages of the genres, listen to samples or artists of the genres (if they did not know already) and then annotate the following dimensions: 
 Question: How many genres did they collect from?
Output: 77 genres
We found that the unsupervised morphological analyzer capable of using lattices improved the accuracy of phoneme recognition and word segmentation. Consequently, this result suggests that this word segmentation method considers the multiple hypothesis of speech recognition as a whole and reduces uncertainty such as variability in recognition by using the syllable recognition results in the lattice format. 
 Question: How do they show that acquiring names of places helps self-localization?
Output: unsupervised morphological analyzer capable of using lattices improved the accuracy of phoneme recognition and word segmentation Consequently, this result suggests that this word segmentation method considers the multiple hypothesis of speech recognition as a whole and reduces uncertainty such as variability in recognition
The challenge is addressed as follows: given a natural language input sequence describing the scene, such as a piece of a story coming from a transcript, the goal is to infer which action is most likely to happen next. 
 Question: Do they literally just treat this as "predict the next spell that appears in the text"?
Output: Yes
Several recent works have investigated jointly training the acoustic model with a masking speech enhancement model BIBREF11, BIBREF12, BIBREF13, but these works did not evaluate their system on speech enhancement metrics. Indeed, our internal experiments show that without access to the clean data, joint training severely harms performance on these metrics. 
 Question: Which frozen acoustic model do they use?
Output: a masking speech enhancement model BIBREF11, BIBREF12, BIBREF13
The best baseline model is NO-MOVE, reaching an accuracy of 30.3% on single sentences and 0.3 on complete paragraphs. 
 Question: How well did the baseline perform?
Output: accuracy of 30.3% on single sentences and 0.3 on complete paragraphs
The embeddings for words and POS tags were pre-trained on a large unannotated corpus consisting of the first 1 billion characters from Wikipedia. 
 Question: Do they use pretrained models as part of their parser?
Output: Yes
Logistic regression model with character-level n-gram features is presented as a strong baseline for comparison since it was shown very effective. 
 Question: What is their baseline?
Output: Logistic regression model with character-level n-gram features
In this work, we introduce IndoSum, a new benchmark dataset for Indonesian text summarization, and evaluated several well-known extractive single-document summarization methods on the dataset. The dataset consists of online news articles and has almost 200 times more documents than the next largest one of the same domain BIBREF2 We used a dataset provided by Shortir, an Indonesian news aggregator and summarizer company. The dataset contains roughly 20K news articles.  
 Question: What is the size of the dataset?
Output: 20K
As a baseline for the RNN models, we apply a uni-directional RNN which predicts the relation after processing the whole sentence. 
 Question: Which variant of the recurrent neural network do they use?
Output: uni-directional RNN
In the future, we aim to use the corpus for annotation projects such as parts-of-speech tagging, named entity recognition. Furthermore, the generated word embeddings will be utilized for the automatic construction of Sindhi WordNet. 
 Question: Are trained word embeddings used for any other NLP task?
Output: No
We evaluate our pointer-generator performance using BLEU score. The baseline language model is trained using RNNLM BIBREF23 . Perplexity measure is used in the evaluation.

 
 Question: Did they use other evaluation metrics?
Output: Yes
Drawing on the concept of variance in mathematics, local variance loss is defined as the reciprocal of its variance expecting the attention model to be able to focus on more salient parts. The standard variance calculation is based on the mean of the distribution. However, as previous work BIBREF15, BIBREF16 mentioned that the median value is more robust to outliers than the mean value, we use the median value to calculate the variance of the attention distribution. Thus, local variance loss can be calculated as:

where $\hat{\cdot }$ is a median operator and $\epsilon $ is utilized to avoid zero in the denominator. 
 Question: How do they define local variance?
Output: The reciprocal of the variance of the attention distribution
In almost all genres, DenseNMT models are significantly better than the baselines. 
 Question: did they outperform previous methods?
Output: Yes
The De7 database used for this study was designed by Marc Schroeder as one of the first attempts of creating diphone databases for expressive speech synthesis BIBREF2. The database contains three voice qualities (modal, soft and loud) uttered by a German female speaker, with about 50 minutes of speech available for each voice quality. 
 Question: What large corpus is used for experiments?
Output: The De7 database
In BIBREF8 a refined collection of tweets gathered from twitter is presented. Their dataset which is labeled for named entity recognition task contains 8,257 tweets. There are 12,784 entities in total in this dataset. Table TABREF19 shows statistics related to each named entity in training, development and test sets.

 
 Question: What datasets did they use?
Output: BIBREF8 a refined collection of tweets gathered from twitter
The retrieved pairs are compared to the gold standard and evaluated using precision at $k$ (P@$k$, evaluating how often the correct translation is within the $k$ retrieved nearest neighbours of the query). Throughout this work we report P@1, which is equivalent to accuracy, but we also provide results with P@5 and P@10 in the Appendix. 
 Question: What evaluation metrics did they use?
Output: we report P@1, which is equivalent to accuracy we also provide results with P@5 and P@10 in the Appendix
To deal with this problem, BIBREF1 introduced a semantically conditioned generation model using Hierarchical Disentangled Self-Attention (HDSA) . 
 Question: what semantically conditioned models did they compare with?
Output: Hierarchical Disentangled Self-Attention
They annotated 7.8K verbs, reporting an average of 2.4 QA pairs per predicate. Even though multiple annotators were shown to produce greater coverage, their released dataset was produced using only a single annotator per verb. 
 Question: How was coverage measured?
Output: QA pairs per predicate
Input features are 40 mel-scaled log filterbank enegries (FBanks) computed every 10 ms with 25 ms window, concatenated with deltas and delta-deltas (120 features in vector). We also tried to use spectrogram and experimented with different normalization techniques. 
 Question: What features do they experiment with?
Output: 40 mel-scaled log filterbank enegries (FBanks) computed every 10 ms with 25 ms window deltas and delta-deltas (120 features in vector) spectrogram
We focus primarily on the word vector representations (word embeddings) created specifically using the twitter dataset. GloVe BIBREF13 is an unsupervised learning algorithm for obtaining vector representations for words. 200-dimensional GloVe embeddings trained on 2 Billion tweets are integrated. Edinburgh embeddings BIBREF14 are obtained by training skip-gram model on Edinburgh corpus BIBREF15 . Since tweets are abundant with emojis, Emoji embeddings BIBREF16 which are learned from the emoji descriptions have been used. Embeddings for each tweet are obtained by summing up individual word vectors and then dividing by the number of tokens in the tweet. 
 Question: what pretrained word embeddings were used?
Output: Pretrained word embeddings  were not used
The Online Retail Data Set consists of a clean list of 25873 invoices, totaling 541909 rows and 8 columns. InvoiceNo, CustomerID and StockCode are mostly 5 or 6-digit integers with occasional letters. Quantity is mostly 1 to 3-digit integers, a part of them being negative, and UnitPrice is composed of 1 to 6 digits floating values. InvoiceDate are dates all in the same format, Country contains strings representing 38 countries and Description is 4224 strings representing names of products. We reconstruct text mails from this data, by separating each token with a blank space and stacking the lines for a given invoice, grouped by InvoiceNo. 
 Question: What is the source of the tables?
Output: The Online Retail Data Set consists of a clean list of 25873 invoices, totaling 541909 rows and 8 columns.
For PHASE-ONE, we randomly shuffled and divided the data (1913 signals from 14 individuals) into train (80%), development (10%) and test sets (10%). In PHASE-TWO, in order to perform a fair comparison with the previous methods reported on the same dataset, we perform a leave-one-subject out cross-validation experiment using the best settings we learn from PHASE-ONE. 
 Question: How many electrodes were used on the subject in EEG sessions?
Output: 1913 signals
Contributors record voice clips by reading from a bank of donated sentences. 
 Question: What domains are covered in the corpus?
Output: No specific domain is covered in the corpus.
In the unsupervised scenario similarity is computed as the cosine of the produced $h_{L}$ and $h_{R}$ sentence/image representations. 
 Question: How they compute similarity between the representations?
Output: similarity is computed as the cosine of the produced $h_{L}$ and $h_{R}$ sentence/image representations
To make the annotated data publicly available, we selected 70 news articles from Arabic WikiNews site. These articles cover recent news from year 2013 to year 2015 in multiple genres (politics, economics, health, science and technology, sports, arts, and culture.) Articles contain 18,300 words, and they are evenly distributed among these 7 genres with 10 articles per each. 
 Question: What is the size of the dataset?
Output: Articles contain 18,300 words, and they are evenly distributed among these 7 genres with 10 articles per each
To better exploit such existing data sources, we propose an end-to-end (E2E) model based on pointer networks with attention, which can be trained end-to-end on the input/output pairs of human IE tasks, without requiring token-level annotations. Since our model does not need token-level labels, we create an E2E version of each data set without token-level labels by chunking the BIO-labeled words and using the labels as fields to extract. 
 Question: Do they assume sentence-level supervision?
Output: No
We collect data by using the Twitter API for saved data, available for public use. For our experiments we collect 3200 tweets filtered by keywords like “fire”, “earthquake”, “theft”, “robbery”, “drunk driving”, “drunk driving accident” etc.  
 Question: Do the tweets come from any individual?
Output: Yes
The Title-to-Story system is a baseline, which generates directly from topic. 
 Question: What are the baselines?
Output: Title-to-Story system
KryptoOracle has been built in the Apache ecosystem and uses Apache Spark. Data structures in Spark are based on resilient distributed datasets (RDD), a read only multi-set of data which can be distributed over a cluster of machines and is fault tolerant.  Spark RDD has the innate capability to recover itself because it stores all execution steps in a lineage graph. In case of any faults in the system, Spark redoes all the previous executions from the built DAG and recovers itself to the previous steady state from any fault such as memory overload. Spark RDDs lie in the core of KryptoOracle and therefore make it easier for it to recover from faults. Moreover, faults like memory overload or system crashes may require for the whole system to hard reboot. However, due to the duplicate copies of the RDDs in Apache Hive and the stored previous state of the machine learning model, KryptoOracle can easily recover to the previous steady state.

 
 Question: How is the architecture fault-tolerant?
Output: By using Apache Spark which stores all executions in a lineage graph and recovers to the previous steady state from any fault
The answer styles considered in the experiments corresponded to the two tasks. The NLG task requires a well-formed answer that is an abstractive summary of the question and ten passages, averaging 16.6 words. The Q&A task also requires an abstractive answer but prefers a more concise answer than the NLG task, averaging 13.1 words, where many of the answers do not contain the context of the question. 
 Question: Is there exactly one "answer style" per dataset?
Output: Yes
An overview of the provided dataset is given in Table TABREF16 . Notice that the available amount of data differs per language. 
 Question: is the dataset balanced across the four languages?
Output: No
The experimental results are shown in Table 1, where it can be seen that autoencoders outperformed MLP and CNN outperformed autoencoders with the highest achieved accuracy of 82.6%. 
 Question: Which deep learning model performed better?
Output: autoencoders
We would like to thank the organizers of the WASSA-2017 Shared Task on Emotion Intensity, for providing the data, the guidelines and timely support. 
 Question: what dataset was used?
Output: WASSA-2017 Shared Task on Emotion Intensity
Experimental results show that our joint model outperforms the visual-only model in all cases, and the text-only model on Wikipedia and two subsets of arXiv. 
 Question: Do the methods that work best on academic papers also work best on Wikipedia?
Output: Yes
Unfortunately, $\mathit {PMI}(w,c)$ goes to negative infinity when the word-context pair $(w,c)$ does not appear in the training corpus. Due to unreliable statistics, this happens very frequently in finite corpora.  
 Question: Why are statistics from finite corpora unreliable?
Output: $\mathit {PMI}(w,c)$ goes to negative infinity when the word-context pair $(w,c)$ does not appear in the training corpus
(1) AutoJudge consistently and significantly outperforms all the baselines, including RC models and other neural text classification models, which shows the effectiveness and robustness of our model. (2) RC models achieve better performance than most text classification models (excluding GRU+Attention), which indicates that reading mechanism is a better way to integrate information from heterogeneous yet complementary inputs. (3) Comparing with conventional RC models, AutoJudge achieves significant improvement with the consideration of additional law articles. 
 Question: what are their results on the constructed dataset?
Output: AutoJudge consistently and significantly outperforms all the baselines RC models achieve better performance than most text classification models (excluding GRU+Attention) Comparing with conventional RC models, AutoJudge achieves significant improvement
Intuitively, under-translated input words should contribute little to the NMT outputs, yielding much smaller word importance.  By exploiting the word importance calculated by Attribution method, we can identify the under-translation errors automatically without the involvement of human interpreters.  
 Question: How do they measure which words are under-translated by NMT models?
Output: They measured the under-translated words with low word importance score as calculated by Attribution.
method
For the competition, we provided 1,000 labeled dialogues from each dataset for training, and 240 unlabeled dialogues from each dataset for evaluation.  
 Question: How large is the first dataset?
Output: 1 000 labeled dialogues for training and 240 unlabeled dialogues for evaluation