niryuu/sni-each-converted huggingface dataset:
Given the full text of a scientific publication, we want to rank its citations according to the author's judgments. We collected recent publications from the open-access PLoS journals and asked the authors to rank by closeness five citations we selected from their paper. 
 Question: what crowdsourcing platform is used?
Output: asked the authors to rank by closeness five citations we selected from their paper
We obtained 1260 unique utterances having commands to AMIE from our in-cabin data-set. We expanded this data-set via Amazon Mechanical Turk and ended up with 3347 utterances having intents. The annotations for intents and slots are obtained on the transcribed utterances by majority voting of 3 annotators. 
 Question: What is the size of their collected dataset?
Output: 3347 unique utterances
Algorithm "Online Learning" demonstrates how our CNN model can be trained in a purely online setting. We first initialize the model parameters $\theta _0$ (line 1), which can be a trained model from other disaster events or it can be initialized randomly to start from scratch.

As a new batch of labeled tweets $B_t= \lbrace \mathbf {s}_1 \ldots \mathbf {s}_n \rbrace $ arrives, we first compute the log-loss (cross entropy) in Equation 11 for $B_t$ with respect to the current parameters $\theta _t$ (line 2a). Then, we use backpropagation to compute the gradients $f^{\prime }(\theta _{t})$ of the loss with respect to the current parameters (line 2b). Finally, we update the parameters with the learning rate $\eta _t$ and the mean of the gradients (line 2c). We take the mean of the gradients to deal with minibatches of different sizes. Notice that we take only the current minibatch into account to get an updated model.  
 Question: What exactly is new about this stochastic gradient descent algorithm?
Output: CNN model can be trained in a purely online setting. We first initialize the model parameters $\theta _0$ (line 1), which can be a trained model from other disaster events or it can be initialized randomly to start from scratch.

As a new batch of labeled tweets $B_t= \lbrace \mathbf {s}_1 \ldots \mathbf {s}_n \rbrace $ arrives, we first compute the log-loss (cross entropy) in Equation 11 for $B_t$ with respect to the current parameters $\theta _t$ (line 2a). Then, we use backpropagation to compute the gradients $f^{\prime }(\theta _{t})$ of the loss with respect to the current parameters (line 2b). Finally, we update the parameters with the learning rate $\eta _t$ and the mean of the gradients (line 2c). We take the mean of the gradients to deal with minibatches of different sizes. Notice that we take only the current minibatch into account to get an updated model.
For the English version, we performed both a thorough manual analysis and automatic evaluation across three commonly used TS datasets from two different domains in order to assess the performance of our framework with regard to the sentence splitting subtask. The results show that our proposed sentence splitting approach outperforms the state of the art in structural TS, returning fine-grained simplified sentences that achieve a high level of grammaticality and preserve the meaning of the input. The full evaluation methodology and detailed results are reported in niklaus-etal-2019-transforming. In addition, a comparative analysis with the annotations contained in the RST Discourse Treebank BIBREF6 demonstrates that we are able to capture the contextual hierarchy between the split sentences with a precision of almost 90% and reach an average precision of approximately 70% for the classification of the rhetorical relations that hold between them. The evaluation of the German version is in progress. 
 Question: Is the model evaluated?
Output: the English version is evaluated. The German version evaluation is in progress
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: What do they mean by answer styles?
Output: well-formed sentences vs concise answers
These are four conjunctively written Nguni languages (zul, xho, nbl, ssw), Afrikaans (afr) and English (eng), three disjunctively written Sotho languages (nso, sot, tsn), as well as tshiVenda (ven) and Xitsonga (tso). The Nguni languages are similar to each other and harder to distinguish. The same is true of the Sotho languages. Similar languages are to each other are:
- Nguni languages: zul, xho, nbl, ssw
- Sotho languages: nso, sot, tsn 
 Question: Which languages are similar to each other?
Output: Nguni languages (zul, xho, nbl, ssw) Sotho languages (nso, sot, tsn)
We evaluate our method on three tagging tasks: POS tagging (Pos), morphological tagging (Morph) and supertagging (Stag). We select these tasks as examples for tagging applications because they differ strongly in tag set sizes. The test results for the three tasks are shown in Table TABREF17 in three groups. The first group of seven columns are the results for Pos, where both LSTM and CNN have three variations of input features: word only ( INLINEFORM0 ), character only ( INLINEFORM1 ) and both ( INLINEFORM2 ). For Morph and Stag, we only use the INLINEFORM3 setting for both LSTM and CNN. 
 Question: Do they jointly tackle multiple tagging problems?
Output: No
The second data batch consists of event-related tweets for five natural disasters occurring in the U.S. in 2018. These are: the East Coast Bomb Cyclone (Jan. 2 - 6); the Mendocino, California wildfires (Jul. 27 - Sept. 18); Hurricane Florence (Aug. 31 - Sept. 19); Hurricane Michael (Oct. 7 - 16); and the California Camp Fires (Nov. 8 - 25).  
 Question: Which five natural disasters were examined?
Output: the East Coast Bomb Cyclone  the Mendocino, California wildfires Hurricane Florence Hurricane Michael the California Camp Fires
The Block Zoo is an open framework, and more modules can be added in the future. Embedding Layer: Word/character embedding and extra handcrafted feature embedding such as pos-tagging are supported. Neural Network Layers: Block zoo provides common layers like RNN, CNN, QRNN BIBREF2 , Transformer BIBREF3 , Highway network, Encoder Decoder architecture, etc. Furthermore, attention mechanisms are widely used in neural networks. Thus we also support multiple attention layers, such as Linear/Bi-linear Attention, Full Attention BIBREF4 , Bidirectional attention flow BIBREF5 , etc. Meanwhile, regularization layers such as Dropout, Layer Norm, Batch Norm, etc are also supported for improving generalization ability. Loss Function: Besides of the loss functions built in PyTorch, we offer more options such as Focal Loss BIBREF6 .

Metrics: For classification task, AUC, Accuracy, Precision/Recall, F1 metrics are supported. For sequence labeling task, F1/Accuracy are supported. For knowledge distillation task, MSE/RMSE are supported. For MRC task, ExactMatch/F1 are supported. 
 Question: What neural network modules are included in NeuronBlocks?
Output: Embedding Layer Neural Network Layers Loss Function Metrics
Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs.  
 Question: How big is QA-CTS task dataset?
Output: 17,833 sentences, 826,987 characters and 2,714 question-answer pairs
Here we describe the components of our probabilistic model of question generation.  The details of optimization are as follows. First, a large set of 150,000 questions is sampled in order to approximate the gradient at each step via importance sampling. Second, to run the procedure for a given model and training set, we ran 100,000 iterations of gradient ascent at a learning rate of 0.1. 
 Question: Is it a neural model? How is it trained?
Output: No, it is a probabilistic model trained by finding feature weights through gradient ascent
We collect HLA data from TV Tropes BIBREF3, a knowledge-based website dedicated to pop culture, containing information on a plethora of characters from a variety of sources. Similar to Wikipedia, its content is provided and edited collaboratively by a massive user-base. These attributes are determined by human viewers and their impressions of the characters, and are correlated with human-like characteristics. We believe that TV Tropes is better for our purpose of fictional character modeling than data sources used in works such as BIBREF25 shuster2019engaging because TV Tropes' content providers are rewarded for correctly providing content through community acknowledgement. 
 Question: How does dataset model character's profiles?
Output: attributes are determined by human viewers and their impressions of the characters, and are correlated with human-like characteristics
We use three datasets for our experiments: IWSLT14 German-English, Turkish-English, and WMT14 English-German. 
 Question: what datasets were used?
Output: IWSLT14 German-English, IWSLT14 Turkish-English, WMT14 English-German
We train both of the systems on the WMT15 German-to-English training data, see Table TABREF18 for some statistics. For this purpose, we use manual alignments provided by RWTH German-English dataset as the hard alignments. 
 Question: What datasets are used?
Output: WMT15 German-to-English RWTH German-English dataset
The dataset needed to train the W2V was obtained using the information extracted from a dump of the Italian Wikipedia (dated 2019.04.01), from the main categories of Italian Google News (WORLD, NATION, BUSINESS, TECHNOLOGY, ENTERTAINMENT, SPORTS, SCIENCE, HEALTH) and from some anonymized chats between users and a customer care chatbot (Laila). The dataset (composed of 2.6 GB of raw text) includes $421\,829\,960$ words divided into $17\,305\,401$ sentences. All the words were then transformed to lowercase (to avoid a double presence) finally producing a vocabulary of $618\,224$ words. 
 Question: What is the dataset used as input to the Word2Vec algorithm?
Output: Italian Wikipedia and Google News extraction producing final vocabulary of 618224 words
We re-implement the model proposed in BIBREF3, and use it as a baseline for our problem. The rationale behind choosing this particular model as a baseline is it's proven good predictive performance on multilingual text classification. 
 Question: What is their baseline model?
Output: the model proposed in BIBREF3
For the labeled data, we use audio from the IEMOCAP dataset, which comes with labels for activation and valence, both measured on a 5-point Likert scale from three distinct annotators. 
 Question: Which multitask annotated corpus is used?
Output: IEMOCAP
The aim of this section is to validate the applicability of our theoretical results—which state that self-attention can perform convolution—and to examine whether self-attention layers in practice do actually learn to operate like convolutional layers, when being trained on standard image classification tasks. In particular, we study the relationship between self-attention and convolution with quadratic and learned relative positional encodings. We find that for both cases, the attention probabilities learned tend to respect the conditions of Lemma UNKREF15, corroborating our hypothesis. Nevertheless, to validate that our model learns a meaningful classifier we compare it to the standard ResNet18 BIBREF14 on the CIFAR-10 dataset BIBREF15. 
 Question: What numerical experiments they perform?
Output: attention probabilities learned tend to respect the conditions of Lemma UNKREF15, corroborating our hypothesis validate that our model learns a meaningful classifier we compare it to the standard ResNet18
To implement the first tier of our framework and detect moral relevance, we complement our morally relevant seed words with a corresponding set of seed words approximating moral irrelevance based on the notion of valence, i.e., the degree of pleasantness or unpleasantness of a stimulus. We refer to the emotional valence ratings collected by BIBREF28 for approximately 14,000 English words, and choose the words with most neutral valence rating that do not occur in the MFD as our set of morally irrelevant seed words, for an equal total number of morally relevant and morally irrelevant words. 
 Question: How do they quantify moral relevance?
Output: By complementing morally relevant seed words with a set of morally irrelevant seed words based on the notion of valence
The first, referred to here as seq2seq, is the standard RNN-based neural machine translation system with attention BIBREF0 . This baseline does not use the parsed data. The multi-source systems improve strongly over both baselines, with improvements of up to 1.5 BLEU over the seq2seq baseline and up to 1.1 BLEU over the parse2seq baseline. 
 Question: Whas is the performance drop of their model when there is no parsed input?
Output:  improvements of up to 1.5 BLEU over the seq2seq baseline
We propose a new unsupervised model, Sent2Vec, for learning universal sentence embeddings Formally, we learn a source (or context) embedding INLINEFORM0 and target embedding INLINEFORM1 for each word INLINEFORM2 in the vocabulary, with embedding dimension INLINEFORM3 and INLINEFORM4 as in ( EQREF6 ). The sentence embedding is defined as the average of the source word embeddings of its constituent words, as in ( EQREF8 ). We augment this model furthermore by also learning source embeddings for not only unigrams but also n-grams present in each sentence, and averaging the n-gram embeddings along with the words, i.e., the sentence embedding INLINEFORM5 for INLINEFORM6 is modeled as DISPLAYFORM0 
 Question: How do the n-gram features incorporate compositionality?
Output: by also learning source embeddings for not only unigrams but also n-grams present in each sentence, and averaging the n-gram embeddings along with the words
In this paper we have formalized the problem of automatic fill-on-the-blanks quiz generation using two well-defined learning schemes: sequence classification and sequence labeling. 
 Question: Which two schemes are used?
Output: sequence classification sequence labeling
We used two different corpora for the experiments: small_parallel_enja and Asian Scientific Paper Excerpt Corpus (ASPEC) BIBREF5. 
 Question: Which dataset do they use?
Output: small_parallel_enja Asian Scientific Paper Excerpt Corpus (ASPEC) BIBREF5
Thus, for this work, we build upon Hybrid Code Networks (HCN) BIBREF4 since HCNs achieve state-of-the-art performance in a data-efficient way for task-oriented dialogs, and propose AE-HCNs which extend HCNs with an autoencoder (Figure FIGREF8 ).  AE-HCN(-CNN) outperforms HCN on Test-OOD by a large margin about 17(20) points on average while keeping the minimum performance trade-off compared to Test.  
 Question: By how much does their method outperform state-of-the-art OOD detection?
Output: AE-HCN outperforms by 17%, AE-HCN-CNN outperforms by 20% on average
In sub-task C the goal is to classify the target of the offensive language. Only posts labeled as targeted insults (TIN) in sub-task B are considered in this task BIBREF17 . Samples are annotated with one of the following:

Individual (IND): Posts targeting a named or unnamed person that is part of the conversation. In English this could be a post such as @USER Is a FRAUD Female @USER group paid for and organized by @USER. In Danish this could be a post such as USER du er sku da syg i hoved. These examples further demonstrate that this category captures the characteristics of cyberbullying, as it is defined in section "Background" .

Group (GRP): Posts targeting a group of people based on ethnicity, gender or sexual orientation, political affiliation, religious belief, or other characteristics. In English this could be a post such as #Antifa are mentally unstable cowards, pretending to be relevant. In Danish this could be e.g. Åh nej! Svensk lorteret!

Other (OTH): The target of the offensive language does not fit the criteria of either of the previous two categories. BIBREF17 . In English this could be a post such as And these entertainment agencies just gonna have to be an ass about it.. In Danish this could be a post such as Netto er jo et tempel over lort. 
 Question: How many categories of offensive language were there?
Output: 3
To test the difficulty of our dataset, we checked the majority class label and the accuracies of five state-of-the-art NLI models adopting different approaches: BiMPM (Bilateral Multi-Perspective Matching Model; BIBREF31 , BIBREF31 ), ESIM (Enhanced Sequential Inference Model; BIBREF32 , BIBREF32 ), Decomposable Attention Model BIBREF33 , KIM (Knowledge-based Inference Model; BIBREF34 , BIBREF34 ), and BERT (Bidirectional Encoder Representations from Transformers model; BIBREF35 , BIBREF35 ). Regarding BERT, we checked the performance of a model pretrained on Wikipedia and BookCorpus for language modeling and trained with SNLI and MultiNLI. 
 Question: What NLI models do they analyze?
Output: BiMPM ESIM Decomposable Attention Model KIM BERT
In contrast to existing bottlenecks, this work targets three different types of social networks (Formspring: a Q&A forum, Twitter: microblogging, and Wikipedia: collaborative knowledge repository) for three topics of cyberbullying (personal attack, racism, and sexism) without doing any explicit feature engineering by developing deep learning based models along with transfer learning. 
 Question: What cyberbulling topics did they address?
Output: personal attack, racism, and sexism
In the first task, participants were instructed to read the sentences naturally, without any specific task other than comprehension. Participants were told to read the sentences normally without any special instructions. 
 Question: What is a normal reading paradigm?
Output: read the sentences normally without any special instructions
Comparing Eq.DISPLAY_FORM14 with Eq.DISPLAY_FORM22, we can see that Eq.DISPLAY_FORM14 is actually a soft form of $F1$, using a continuous $p$ rather than the binary $\mathbb {I}( p_{i1}>0.5)$. This gap isn't a big issue for balanced datasets, but is extremely detrimental if a big proportion of training examples are easy-negative ones: easy-negative examples can easily dominate training since their probabilities can be pushed to 0 fairly easily. Meanwhile, the model can hardly distinguish between hard-negative examples and positive ones, which has a huge negative effect on the final F1 performance.

To address this issue, we propose to multiply the soft probability $p$ with a decaying factor $(1-p)$, changing Eq.DISPLAY_FORM22 to the following form:

One can think $(1-p_{i1})$ as a weight associated with each example, which changes as training proceeds. The intuition of changing $p_{i1}$ to $(1-p_{i1}) p_{i1}$ is to push down the weight of easy examples. For easy examples whose probability are approaching 0 or 1, $(1-p_{i1}) p_{i1}$ makes the model attach significantly less focus to them. Figure FIGREF23 gives gives an explanation from the perspective in derivative: the derivative of $\frac{(1-p)p}{1+(1-p)p}$ with respect to $p$ approaches 0 immediately after $p$ approaches 0, which means the model attends less to examples once they are correctly classified.

A close look at Eq.DISPLAY_FORM14 reveals that it actually mimics the idea of focal loss (FL for short) BIBREF16 for object detection in vision. Focal loss was proposed for one-stage object detector to handle foreground-background tradeoff encountered during training. It down-weights the loss assigned to well-classified examples by adding a $(1-p)^{\beta }$ factor, leading the final loss to be $(1-p)^{\beta }\log p$. 
 Question: How are weights dynamically adjusted?
Output: One can think $(1-p_{i1})$ as a weight associated with each example, which changes as training proceeds. The intuition of changing $p_{i1}$ to $(1-p_{i1}) p_{i1}$ is to push down the weight of easy examples. For easy examples whose probability are approaching 0 or 1, $(1-p_{i1}) p_{i1}$ makes the model attach significantly less focus to them. Figure FIGREF23 gives gives an explanation from the perspective in derivative: the derivative of $\frac{(1-p)p}{1+(1-p)p}$ with respect to $p$ approaches 0 immediately after $p$ approaches 0, which means the model attends less to examples once they are correctly classified.
We use the unshuffled version of the French OSCAR corpus, which amounts to 138GB of uncompressed text and 32.7B SentencePiece tokens. 
 Question: What data is used for training CamemBERT?
Output: unshuffled version of the French OSCAR corpus
For the benchmarks, we selected five systems. We picked first the langid.py library which is frequently used to compare systems in the literature. Since our work is in neural-network LID, we selected two neural network systems from the literature, specifically the encoder-decoder EquiLID system of BIBREF6 and the GRU neural network LanideNN system of BIBREF10. Finally, we included CLD2 and CLD3, two implementations of the Naïve Bayes LID software used by Google in their Chrome web browser BIBREF4, BIBREF0, BIBREF8 and sometimes used as a comparison system in the LID literature BIBREF7, BIBREF6, BIBREF8, BIBREF2, BIBREF10. 
 Question: Which existing language ID systems are tested?
Output: langid.py library encoder-decoder EquiLID system GRU neural network LanideNN system CLD2 CLD3
We use three creative English datasets with distinct linguistic characteristics: (1) A corpus of 740 classical and contemporary English poems, (2) a corpus of 14950 metaphor sentences retrieved from a metaphor database website and (3) a corpus of 1500 song lyrics ranging across genres.  We first pre-train our generator on the Gutenberg dataset BIBREF24 for 20 epochs and then fine-tune BIBREF19 them to our target datasets with a language modeling objective. 
 Question: Which datasets are used?
Output: A corpus of 740 classical and contemporary English poems  a corpus of 14950 metaphor sentences retrieved from a metaphor database website  a corpus of 1500 song lyrics ranging across genres Gutenberg dataset
A representation that is more in line with observed user behavior is a concept map BIBREF5 , a labeled graph showing concepts as nodes and relationships between them as edges (Figure FIGREF2 ). 
 Question: How do the authors define a concept map?
Output: concept map BIBREF5 , a labeled graph showing concepts as nodes and relationships between them as edges
Experiment 1 directly tested the hypothesis that speakers increase their specificity in contexts with asymmetry in visual access. We found that speakers are not only context-sensitive in choosing referring expressions that distinguish target from distractors in a shared context, but are occlusion-sensitive, adaptively compensating for uncertainty. These results strongly suggest that the speaker's informativity influences listener accuracy. In support of this hypothesis, we found a strong negative correlation between informativity and error rates across items and conditions: listeners make fewer errors when utterances are a better fit for the target relative to the distractor ( $\rho = -0.81$ , bootstrapped 95% CI $= [-0.9, -0.7]$ ; Fig. 6 B). This result suggests that listener behavior is driven by an expectation of speaker informativity: listeners interpret utterances proportionally to how well they fit objects in context. Our Rational Speech Act (RSA) formalization of cooperative reasoning in this context predicts that speakers (directors) naturally increase the informativity of their referring expressions to hedge against the increased risk of misunderstanding; Exp. 1 presents direct evidence in support of this hypothesis. Exp. 2 is consistent with this hypothesis; when directors used underinformative scripted instructions (taken from prior work), listeners made significantly more errors than when speakers were allowed to provide referring expressions at their natural level of informativity, and speaker informativeness strongly modulated listener error rates. 
 Question: Did participants behave unexpectedly?
Output: No
We provide a hand-curated collection of complete inflection tables for 198 lemmata. The morphological tags follow the guidelines of the UniMorph schema BIBREF6, BIBREF7, in order to allow for the potential of cross-lingual transfer learning, and they are tagged with respect to:

Person: first (1), second (2), and third (3)

Number: singular (SG) ad plural (PL)

Inclusivity (only applicable to first person plural verbs: inclusive (INCL) and exclusive (EXCL)

Aspect/mood: completive (CPL), progressive (PROG), potential (POT), and habitual (HAB). 
 Question: How was annotation done?
Output:  hand-curated collection of complete inflection tables for 198 lemmata
The study of the mathematical structure of grammar has indicated that the fundamental things making up sentences are not the words, but some atomic grammatical types, such as the noun-type and the sentence-type BIBREF23 , BIBREF24 , BIBREF25 . The transitive verb-type is not an atomic grammatical type, but a composite made up of two noun-types and one sentence-type. Hence, particularly interesting here is that atomic doesn't really mean smallest... 
 Question: Do they argue that all words can be derived from other (elementary) words?
Output: No
Tweets related to Forex, specifically to EUR and USD, were acquired through the Twitter search API with the following query: “EURUSD”, “USDEUR”, “EUR”, or “USD”. In the period of three years (January 2014 to December 2016) almost 15 million tweets were collected. A subset of them (44,000 tweets) was manually labeled by knowledgeable students of finance.  
 Question: How many tweets were manually labelled? 
Output: 44,000 tweets
Plackett-Luce Model for SMT Reranking
After being de-duplicated, the N-best list has an average size of around 300, and with 7491 features. This experiment displays, in large-scale features, the Plackett-Luce model correlates with BLEU score very well, and alleviates overfitting in some degree. 
 Question: What experiments with large-scale features are performed?
Output: Plackett-Luce Model for SMT Reranking
We propose extended middle context, a new context representation for CNNs for relation classification. The contexts are split into three disjoint regions based on the two relation arguments: the left context, the middle context and the right context.  Hence, we propose to use two contexts: (1) a combination of the left context, the left entity and the middle context; and (2) a combination of the middle context, the right entity and the right context. The two contexts are processed by two independent convolutional and max-pooling layers. After pooling, the results are concatenated to form the sentence representation. 
 Question: How do they obtain the new context represetation?
Output: They use two independent convolutional and max-pooling layers on (1) a combination of the left context, the left entity and the middle context; and (2) a combination of the middle context, the right entity and the right context. They concatenated the two results after pooling to get the new context representation.
There are 8757 news records in our preprocessed data set.  
 Question: How large is the dataset?
Output: 8757 news records
We experiment with five benchmark attacking methods for texts: FGM, FGVM, DeepFool BIBREF5, HotFlip BIBREF3) and TYC BIBREF4. 
 Question: What are the benchmark attacking methods?
Output: FGM, FGVM, DeepFool BIBREF5, HotFlip BIBREF3) and TYC BIBREF4
We extract data from the WMT'14 English-French (En-Fr) and English-German (En-De) datasets. To create a larger discrepancy between the tasks, so that there is a clear dataset size imbalance, the En-De data is artificially restricted to only 1 million parallel sentences, while the full En-Fr dataset, comprising almost 40 million parallel sentences, is used entirely.  
 Question: What datasets are used for experiments?
Output: the WMT'14 English-French (En-Fr) and English-German (En-De) datasets.
Kneser–Ney smoothing In particular, we compare Kneser–Ney smoothing, widely accepted as the state of the art prior to NLMs, to the best NLMs today. 
 Question: what classic language models are mentioned in the paper?
Output: Kneser–Ney smoothing
The BioNLP 2009 Shared Task BIBREF195 was based on the GENIA corpus BIBREF196 which contains PubMed abstracts of articles on transcription factors in human blood cells. 
 Question: Which datasets are used in this work?
Output: GENIA corpus
In other cases, the model points out plausible signals which were passed over by an annotator, and may be considered errors in the gold standard. For example, the model easily notices that question marks indicate the solutionhood relation, even where these were skipped by annotators in favor of marking WH words instead:

. [RGB]230, 230, 230Which [RGB]230, 230, 230previous [RGB]230, 230, 230Virginia [RGB]230, 230, 230Governor(s) [RGB]230, 230, 230do [RGB]230, 230, 230you [RGB]230, 230, 230most [RGB]230, 230, 230admire [RGB]230, 230, 230and [RGB]230, 230, 230why [RGB]12, 12, 12? $\xrightarrow[\text{pred:solutionhood}]{\text{gold:solutionhood}}$ [RGB]230, 230, 230Thomas [RGB]230, 230, 230Jefferson [RGB]183, 183, 183. However, it also picks up on a recurring tendency in how-to guides in which the second person pronoun referring to the reader is often the benefactee of some action, which contributes to the purpose reading and helps to disambiguate so, despite not being considered a signal by annotators. 
 Question: Where does proposed metric differ from juman judgement?
Output: model points out plausible signals which were passed over by an annotator it also picks up on a recurring tendency in how-to guides in which the second person pronoun referring to the reader is often the benefactee of some action
Datasets: We experimented on four standard datasets: WN18 and FB15k are extracted by BIBREF5 from Wordnet BIBREF32 Freebase BIBREF33 . 
 Question: What datasets are used to evaluate the model?
Output: WN18 and FB15k
In this section we detail the discussions we use to test our metric and how we determine the ground truth (i.e. if the discussion is controversial or not). We use thirty different discussions that took place between March 2015 and June 2019, half of them with controversy and half without it. We considered discussions in four different languages: English, Portuguese, Spanish and French, occurring in five regions over the world: South and North America, Western Europe, Central and Southern Asia. We also studied these discussions taking first 140 characters and then 280 from each tweet to analyze the difference in performance and computing time wrt the length of the posts.

 
 Question: How many languages do they experiment with?
Output: four different languages: English, Portuguese, Spanish and French
Named Entity Recognition (NER) in the Biomedical domain usually includes recognition of entities such as proteins, genes, diseases, treatments, drugs, etc.  
 Question: What is NER?
Output: Named Entity Recognition
Our WordDecoding (WDec) model achieves F1 scores that are $3.9\%$ and $4.1\%$ higher than HRL on the NYT29 and NYT24 datasets respectively. Similarly, our PtrNetDecoding (PNDec) model achieves F1 scores that are $3.0\%$ and $1.3\%$ higher than HRL on the NYT29 and NYT24 datasets respectively. 
 Question: How higher are F1 scores compared to previous work?
Output: WordDecoding (WDec) model achieves F1 scores that are $3.9\%$ and $4.1\%$ higher than HRL on the NYT29 and NYT24 datasets respectively PtrNetDecoding (PNDec) model achieves F1 scores that are $3.0\%$ and $1.3\%$ higher than HRL on the NYT29 and NYT24 datasets respectively
Results show that the CJFA encoder obtains significantly better phone classification accuracy than the VAE baseline and also than the CJFS encoder. These results are replicated for speaker recognition tasks. 
 Question: Which approach out of two proposed in the paper performed better in experiments?
Output: CJFA encoder
Here a small portion of the large parallel corpus for English-German is used as a simulation for the scenario where we do not have much parallel data: Translating texts in English to German.  
 Question: Which languages do they test on for the under-resourced scenario?
Output: English German
INLINEFORM0 in the subsampled BIBREF2 training corpus and incrementing cell INLINEFORM1 for every context word INLINEFORM2 appearing within this window (forming a INLINEFORM3 pair). LexVec adjusts the PPMI matrix using context distribution smoothing BIBREF3 . We compare 1) the use of n-gram subwords, like fastText, and 2) unsupervised morphemes identified using Morfessor BIBREF11 to learn whether more linguistically motivated subwords offer any advantage over simple n-grams. 
 Question: What types of subwords do they incorporate in their model?
Output: n-gram subwords unsupervised morphemes identified using Morfessor BIBREF11 to learn whether more linguistically motivated subwords
Using the fine-grained gating mechanism conditioned on the lexical features, we can accurately control the information flows between word-level and char-level. Intuitively, the formulation is as follows: INLINEFORM0

where INLINEFORM0 is the element-wise multiplication operator. when the gate has high value, more information flows from the word-level representation; otherwise, char-level will take the dominating place. It is practical in real scenarios. For example, for unfamiliar noun entities, the gates tend to bias towards char-level representation in order to care richer morphological structure. 
 Question: How does the gatint mechanism combine word and character information?
Output: when the gate has high value, more information flows from the word-level representation; otherwise, char-level will take the dominating place  for unfamiliar noun entities, the gates tend to bias towards char-level representation in order to care richer morphological structure
Table 1 shows the results of all models on WikiLarge dataset. We can see that our method (NMT+synthetic) can obtain higher BLEU, lower FKGL and high SARI compared with other models, except Dress on FKGL and SBMT-SARI on SARI. It verified that including synthetic data during training is very effective, and yields an improvement over our baseline NMF by 2.11 BLEU, 1.7 FKGL and 1.07 SARI.  Results on WikiSmall dataset are shown in Table 2. We see substantial improvements (6.37 BLEU) than NMT from adding simplified training data with synthetic ordinary sentences.  
 Question: by how much did their model improve?
Output: For the WikiLarge dataset, the improvement over baseline NMT is 2.11 BLEU, 1.7 FKGL and 1.07 SARI.
For the WikiSmall dataset, the improvement over baseline NMT is  8.37 BLEU.
The dataset consists of 198,112 news articles. 
 Question: How many articles did they have?
Output: 198,112
To verify our assumption that target encoding and orthogonal regularization help to boost the diversity of generated sequences, we use two metrics, one quantitative and one qualitative, to measure diversity of generation. First, we simply calculate the average unique predictions produced by both INLINEFORM0 and INLINEFORM1 in experiments shown in Section SECREF36 .  In this example there are 29 ground truth phrases. Neither of the models is able to generate all of the keyphrases, but it is obvious that the predictions from INLINEFORM0 all start with “test”, while predictions from INLINEFORM1 are diverse. 
 Question: How is keyphrase diversity measured?
Output: average unique predictions illustrate the difference of predictions between our proposed models, we show an example chosen from the KP20k validation set
While the mixing strategy compensates for most of the gap between the Fr-De* and the Fr*-De (3.01 $\rightarrow $ 0.17) in the De $\rightarrow $ Fr case, the resulting PSEUDOmix still shows lower BLEU than the target-originated Fr-De* corpus. We thus enhance the quality of the synthetic examples of the source-originated Fr*-De data by further training its mother translation model (En $\rightarrow $ Fr). As illustrated in Figure 2 , with the target-originated Fr-De* corpus being fixed, the quality of the models trained with the source-originated Fr*-De data and PSEUDOmix increases in proportion to the quality of the mother model for the Fr*-De corpus. Eventually, PSEUDOmix shows the highest BLEU, outperforming both Fr*-De and Fr-De* data.  As presented in Table 6 , we observe that fine-tuning using ground truth parallel data brings substantial improvements in the translation qualities of all NMT models. Among all fine-tuned models, PSEUDOmix shows the best performance in all experiments. This is particularly encouraging for the case of De $\rightarrow $ Fr, where PSEUDOmix reported lower BLEU than the Fr-De* data before it was fine-tuned. Even in the case where PSEUDOmix shows comparable results with other synthetic corpora in the Pseudo Only scenario, it shows higher improvements in the translation quality when fine-tuned with the real parallel data.  
 Question: How many improvements on the French-German translation benchmark?
Output: one
Database Construction: we crawled travel information in Beijing from the Web, including Hotel, Attraction, and Restaurant domains (hereafter we name the three domains as HAR domains). Then, we used the metro information of entities in HAR domains to build the metro database. Goal Generation: a multi-domain goal generator was designed based on the database. The relation across domains is captured in two ways. One is to constrain two targets that locate near each other. The other is to use a taxi or metro to commute between two targets in HAR domains mentioned in the context.  Dialogue Collection: before the formal data collection starts, we required the workers to make a small number of dialogues and gave them feedback about the dialogue quality. Then, well-trained workers were paired to converse according to the given goals.  Dialogue Annotation: we used some rules to automatically annotate dialogue acts according to user states, system states, and dialogue histories.  
 Question: How was the dataset collected?
Output: Database Construction: we crawled travel information in Beijing from the Web, including Hotel, Attraction, and Restaurant domains (hereafter we name the three domains as HAR domains). Then, we used the metro information of entities in HAR domains to build the metro database.  Goal Generation: a multi-domain goal generator was designed based on the database. The relation across domains is captured in two ways. One is to constrain two targets that locate near each other. The other is to use a taxi or metro to commute between two targets in HAR domains mentioned in the context. Dialogue Collection: before the formal data collection starts, we required the workers to make a small number of dialogues and gave them feedback about the dialogue quality. Then, well-trained workers were paired to converse according to the given goals. The workers were also asked to annotate both user states and system states. Dialogue Annotation: we used some rules to automatically annotate dialogue acts according to user states, system states, and dialogue histories.
There are various possible extensions for this work. For example, using all frames assigned to a phone, rather than using only the middle frame. 
 Question: Do they propose any further additions that could be made to improve generalisation to unseen speakers?
Output: Yes
To support this claim, we measure our system's performance for datasets across various domains. The evaluations are done by managed judges who understands the knowledge base and then judge user queries relevance to the QA pairs (binary labels). Each query-QA pair is judged by two judges. We filter out data for which judges do not agree on the label. Chit-chat in itself can be considered as a domain. Thus, we evaluate performance on given KB both with and without chit-chat data (last two rows in Table TABREF19), as well as performance on just chit-chat data (2nd row in Table TABREF19). 
 Question: What experiments do the authors present to validate their system?
Output:  we measure our system's performance for datasets across various domains evaluations are done by managed judges who understands the knowledge base and then judge user queries relevance to the QA pairs
Deep convolutional neural networks (CNNs) with 2D convolutions and small kernels BIBREF1, have achieved state-of-the-art results for several speech recognition tasks BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6. r Models: Our baseline CNN model BIBREF21 consists of 15 convolutional and one fully-connected layer. 
 Question: Is model compared against state of the art models on these datasets?
Output: Yes
As the first step, we build three baseline LID systems, one based on the i-vector model, and the other two based on LSTM-RNN, using the speech data of two languages from Babel: Assamese and Georgian (AG). The two RNN LID baselines are: a standard RNN LID system (AG-RNN-LID) that discriminates between the two languages in its output, and a multi-task system (AG-RNN-MLT) that was trained to discriminate between the two languages as well as the phones. 
 Question: Which is the baseline model?
Output: The three baseline models are the i-vector model, a standard RNN LID system and a multi-task RNN LID system.
Eventually, we have a balanced testing dataset, where each term-sense pair has around 15 samples for testing (on average, each pair has 14.56 samples and the median sample number is 15), and a comparison with training dataset is shown in Figure FIGREF11. Due to the difficulty for collecting the testing dataset, we decided to only collect for a random selection of 30 terms.  
 Question: How big is dataset for testing?
Output: 30 terms, each term-sanse pair has around 15 samples for testing
It may be difficult to spot patterns by just looking at a collection of images. Another method is to tag all descriptions with part-of-speech information, so that it becomes possible to see e.g. which adjectives are most commonly used for particular nouns. One method readers may find particularly useful is to leverage the structure of Flickr30K Entities BIBREF8 . Following this, I applied Louvain clustering BIBREF9 to the coreference graph, resulting in clusters of expressions that refer to similar entities. 
 Question: Which methods are considered to find examples of biases and unwarranted inferences??
Output: spot patterns by just looking at a collection of images tag all descriptions with part-of-speech information I applied Louvain clustering
We then proceed to connect these mentions i) if they co-occur within the same document (we will refer to this as DOC-BASED edges), ii) if the pair of named entity mentions is identical (MATCH edges—these may connect nodes across and within documents), or iii) if they are in the same coreference chain, as predicted by the external coreference system (COREF edges).  
 Question: How did they get relations between mentions?
Output: Assign a value to the relation based on whether mentions occur in the same document, if mentions are identical, or if mentions are in the same coreference chain.
Cloze Track User Query Track 
 Question: What two types the Chinese reading comprehension dataset consists of?
Output: cloze-style reading comprehension and user query reading comprehension questions
We use an unsupervised word segmentation method latticelm that can directly segment words from the lattices of the speech recognition results of the uttered sentences BIBREF22 . 
 Question: Which method do they use for word segmentation?
Output: unsupervised word segmentation method latticelm
We compare our approach to two other, the first one uses almost the same tweet data as we use for training, and the second one is the CrowdFlower dataset annotated for emotions. In the first one Wang et al. BIBREF21 downloaded over 5M tweets which included one of 131 emotional hashtags based on Parrott's three-level categorization of emotions in seven categories: joy, sadness, anger, love, fear, thankfulness, surprise.  
 Question: What data is used in experiments?
Output: Wang et al. CrowdFlower dataset
Our model BiLSTM+CNN(grapheme-level) and BiLSTM+CNN(G)+POS outperforms all other model experimented in OurNepali and ILPRL dataset respectively. 
 Question: What is the best model?
Output: BiLSTM+CNN(grapheme-level) and BiLSTM+CNN(G)+POS
We also run additional CTC experiments with 36 layers Transformer (total parameters $\pm $120 millions). The baseline with 36 layers has the same performance with 24 layers, but by adding the proposed methods, the 36 layer performance improved to give the best results.  
 Question: How many layers do they use in their best performing network?
Output: 36
E2ECM BIBREF11: In dialogue policy maker, it adopts a classic classification for skeletal sentence template. In our implement, we construct multiple binary classifications for each act to search the sentence template according to the work proposed by BIBREF11.

CDM BIBREF10: This approach designs a group of classifications (two multi-class classifications and some binary classifications) to model the dialogue policy. 
 Question: What are state-of-the-art baselines?
Output: E2ECM CDM
The word intrusion test is expensive to apply since it requires manual evaluations by human observers separately for each embedding dimension. Furthermore, the word intrusion test does not quantify the interpretability levels of the embedding dimensions, instead it yields a binary decision as to whether a dimension is interpretable or not. However, using continuous values is more adequate than making binary evaluations since interpretability levels may vary gradually across dimensions. 
 Question: What advantages does their proposed method of quantifying interpretability have over the human-in-the-loop evaluation they compare to?
Output: it is less expensive and quantifies interpretability using continuous values rather than binary evaluations
In this paper we proposed a methodology to identify words that could lead to confusion at any given node of a speech recognition based system. We used edit distance as the metric to identifying the possible confusion between the active words.  There is a significant saving in terms of being able to identify recognition bottlenecks in a menu based speech solution through this analysis because it does not require actual people testing the system.  Actual use of this analysis was carried out for a speech solution developed for Indian Railway Inquiry System to identify bottlenecks in the system before its actual launch. 
 Question: what bottlenecks were identified?
Output: Confusion in recognizing the words that are active at a given node by a speech recognition solution developed for Indian Railway Inquiry System.
MonaLog utilizes two auxiliary sets. First, a knowledge base ${K}$ that stores the world knowledge needed for inference, e.g., semanticist $\le $ linguist and swim $\le $ move, which captures the facts that $[\![\mbox{\em semanticist}]\!]$ denotes a subset of $[\![\mbox{\em linguist}]\!]$, and that $[\![\mbox{\em swim}]\!]$ denotes a subset of $[\![\mbox{\em move}]\!]$, respectively. Such world knowledge can be created manually for the problem at hand, or derived easily from existing resources such as WordNet BIBREF22. Note that we do not blindly add all relations from WordNet to our knowledge base, since this would hinge heavily on word sense disambiguation (we need to know whether the “bank” is a financial institution or a river bank to extract its relations correctly). In the current implementation, we avoid this by adding x $\le $ y or x $\perp $ y relations only if both x and y are words in the premise-hypothesis pair. Additionally, some relations that involve quantifiers and prepositions need to be hard-coded, since WordNet does not include them: every $=$ all $=$ each $\le $ most $\le $ many $\le $ a few $=$ several $\le $ some $=$ a; the $\le $ some $=$ a; on $\perp $ off; up $\perp $ down; etc. 
 Question: How do they select monotonicity facts?
Output: They derive it from Wordnet