niryuu/sni-each-converted huggingface dataset:
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). Reviews contain star ratings (from 1 to 5) which are converted into binary labels as follows: reviews rated with more than 3 stars are labeled as positive, and those with less than 3 stars as negative. In each domain, there are 1000 positive and 1000 negative reviews.
Output: How long is the dataset?
The stories from each scenario were distributed among four different annotators. 
Output: How many subjects have been used to create the annotations?
To evaluate our model capability with processing the Chinese language, we also tested the performance of LCF-ATEPC on four Chinese comment datasets BIBREF35, BIBREF36, BIBREF29 (Car, Phone, Notebook, Camera).
Output: In what four Chinese review datasets does LCF-ATEPC achieves state of the art?
WikiSmall consists of ordinary and simplified sentences from the ordinary and simple English Wikipedias, which has been used as benchmark for evaluating text simplification BIBREF17 , BIBREF18 , BIBREF8 . The training set has 89,042 sentence pairs, and the test set has 100 pairs. WikiLarge is also from Wikipedia corpus whose training set contains 296,402 sentence pairs BIBREF19 , BIBREF20 . WikiLarge includes 8 (reference) simplifications for 2,359 sentences split into 2,000 for development and 359 for testing.
Output: what are the sizes of both datasets?
Fig. FIGREF4 shows the distributions of seven question types grouped deterministically from the lexicons.
Output: How many question types do they find in the datasets analyzed?
We use the morphological tagging datasets provided by the Universal Dependencies (UD) treebanks (the concatenation of the $4^\text{th}$ and $6^\text{th}$ columns of the file format) BIBREF13 .
Output: On which dataset is the experiment conducted?
However, we found that explicitly initializing these embeddings with vectors pre-trained over a large collection of unlabeled data significantly improved performance (see Section "Effects of initialized embeddings and corrupt-sampling schemes" ).
Output: What is the new initialization method proposed in this paper?
Our target data set consists of a corpus of discharge summaries, admission notes, individual encounter notes, and other clinical notes from 220 patients in the OnTrackTM program at McLean Hospital. OnTrackTM is an outpatient program, focusing on treating adults ages 18 to 30 who are experiencing their first episodes of psychosis. The length of time in the program varies depending on patient improvement and insurance coverage, with an average of two to three years. The program focuses primarily on early intervention via individual therapy, group therapy, medication evaluation, and medication management. See Table TABREF2 for a demographic breakdown of the 220 patients, for which we have so far extracted approximately 240,000 total EHR paragraphs spanning from 2011 to 2014 using Meditech, the software employed by McLean for storing and organizing EHR data.

These patients are part of a larger research cohort of approximately 1,800 psychosis patients, which will allow us to connect the results of this EHR study with other ongoing research studies incorporating genetic, cognitive, neurobiological, and functional outcome data from this cohort.

We also use an additional data set for training our vector space model, comprised of EHR texts queried from the Research Patient Data Registry (RPDR), a centralized regional data repository of clinical data from all institutions in the Partners HealthCare network. These records are highly comparable in style and vocabulary to our target data set. The corpus consists of discharge summaries, encounter notes, and visit notes from approximately 30,000 patients admitted to the system's hospitals with psychiatric diagnoses and symptoms. This breadth of data captures a wide range of clinical narratives, creating a comprehensive foundation for topic extraction.
Output: What datasets did the authors use?
We then propose a group of new interesting tasks, such as analogy query and analogy browsing, and discuss how they can be used in modern digital libraries.
Output: What new interesting tasks can be solved based on the uncanny semantic structures of the embedding space?
We then evaluate the ranks of the target entities $t$ with the commonly used mean rank (MR), mean reciprocal rank (MRR), as well as Hits@1, Hits@3, and Hits@10.
Output: what was the evaluation metrics studied in this work?
CoinCollector BIBREF8 is a class of text-based games where the objective is to find and collect a coin from a specific location in a given set of connected rooms . The agent wins the game after it collects the coin, at which point (for the first and only time) a reward of +1 is received by the agent. The environment parses only five admissible commands (go north, go east, go south, go west, and take coin) made by two worlds; CookingWorld BIBREF14 in this challenge, there are 4,440 games with 222 different levels of difficulty, with 20 games per level of difficulty, each with different entities and maps. The goal of each game is to cook and eat food from a given recipe, which includes the task of collecting ingredients (e.g. tomato, potato, etc.), objects (e.g. knife), and processing them according to the recipe (e.g. cook potato, slice tomato, etc.). The parser of each game accepts 18 verbs and 51 entities with a predefined grammar, but the overall size of the vocabulary of the observations is 20,000. In Appendix SECREF36 we provide more details about the levels and the games' grammar.
Output: On what Text-Based Games are experiments performed?
Modern Standard Arabic (MSA) and Classical Arabic (CA) have two types of vowels, namely long vowels, which are explicitly written, and short vowels, aka diacritics, which are typically omitted in writing but are reintroduced by readers to properly pronounce words. We show that our model achieves a case ending error rate (CEER) of 3.7% for MSA and 2.5% for CA. For MSA, this CEER is more than 60% lower than other state-of-the-art systems such as Farasa and the RDI diacritizer, which are trained on the same dataset and achieve CEERs of 10.7% and 14.4% respectively. 
Output: what are the previous state of the art?
we apply our domain adaptation method to a neural captioning model and show performance improvement over other standard methods on several datasets and metrics. 
Output: Did they only experiment with captioning task?
Because our approach is specifically intended to yield sentences that are grammatical, we additionally consider the following log ratio (i.e., the grammatical phrase over the ungrammatical phrase):
Output: How do they measure grammaticality?
To enable sentence-level classification we need to obtain a sentence representation from the word vectors INLINEFORM0 . We achieved this by using a BiLSTM with max pooling, which was shown to be a good universal sentence encoding mechanism BIBREF13 .
Output: Which model architecture do they use to obtain representations?
We collected tweets related to five different DDoS attacks on three different American banks. For each attack, all the tweets containing the bank's name posted from one week before the attack until the attack day were collected. There are in total 35214 tweets in the dataset. Only the tweets from the Bank of America attack on 09/19/2012 were used in this experiment. In this subsection we evaluate how good the model generalizes. To achieve that, the dataset is divided into two groups, one is about the attacks on Bank of America and the other group is about PNC and Wells Fargo. The only difference between this experiment and the experiment in section 4.4 is the dataset. In this experiment setting $D_a$ contains only the tweets collected on the days of attack on PNC and Wells Fargo. $D_b$ only contains the tweets collected before the Bank of America attack.
Output: What is the training and test data used?
The selection model selects the best answer from the set $\lbrace a_i\rbrace _{i=1}^N$ observed during the interaction by predicting the difference of the F1 score to the average F1 of all variants. We train another neural network to pick the best answer from the candidates. We frame the task as binary classification, distinguishing between above and below average performance. In training, we compute the F1 score of the answer for every instance. If the rewrite produces an answer with an F1 score greater than the average score of the other rewrites the instance is assigned a positive label.
Output: how are multiple answers from multiple reformulated questions aggregated?
For the Russian language, with its rich morphology, lemmatizing the training and testing data for ELMo representations yields small but consistent improvements in the WSD task. 
Output: What other examples of morphologically-rich languages do the authors give?
Since we were also interested in whether argumentation differs across registers, we included four different registers — namely (1) user comments to newswire articles or to blog posts, (2) posts in discussion forums (forum posts), (3) blog posts, and (4) newswire articles. 
Output: How is the data in the new corpus come sourced?
In other words, there exist a few directions in the embedding space which disproportionately explain the variance. It is clear that the visual features in the case of How2 dataset are much more dominated by the "common" dimensions, when compared to the Multi30k dataset. Further, this analysis is still at the sentence level, i.e. the visual features are much less discriminative among individual sentences, further aggravating the problem at the token level. This suggests that the existing visual features aren't sufficient enough to expect benefits from the visual modality in NMT, since they won't provide discriminativeness among the vocabulary elements at the token level during prediction.
Output: What is result of their Principal Component Analysis?
Our AV in-cabin data-set includes 30 hours of multimodal data collected from 30 passengers (15 female, 15 male) in 20 rides/sessions. 10 types of passenger intents are identified and annotated as: Set/Change Destination, Set/Change Route (including turn-by-turn instructions), Go Faster, Go Slower, Stop, Park, Pull Over, Drop Off, Open Door, and Other (turn music/radio on/off, open/close window/trunk, change AC/temp, show map, etc.). Relevant slots are identified and annotated as: Location, Position/Direction, Object, Time-Guidance, Person, Gesture/Gaze (this, that, over there, etc.), and None. In addition to utterance-level intent types and their slots, word-level intent keywords are annotated as Intent as well. 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.
Output: What are the supported natural commands?
We compare the SBERT sentence embeddings to other sentence embeddings methods on the following seven SentEval transfer tasks:

MR: Sentiment prediction for movie reviews snippets on a five start scale BIBREF25.

CR: Sentiment prediction of customer product reviews BIBREF26.

SUBJ: Subjectivity prediction of sentences from movie reviews and plot summaries BIBREF27.

MPQA: Phrase level opinion polarity classification from newswire BIBREF28.

SST: Stanford Sentiment Treebank with binary labels BIBREF29.

TREC: Fine grained question-type classification from TREC BIBREF30.

MRPC: Microsoft Research Paraphrase Corpus from parallel news sources BIBREF31.
Output: What transfer learning tasks are evaluated?
Our training data consists of 2.09M sentence pairs extracted from LDC corpus. To test different approaches on Chinese-to-English translation task, we use NIST 2003(MT03) dataset as the validation set, and NIST 2004(MT04), NIST 2005(MT05), NIST 2006(MT06) datasets as our test sets. For English-to-Chinese translation task, we also use NIST 2003(MT03) dataset as the validation set, and NIST 2008(MT08) will be used as test set.
Output: What dataset did they use?
In this section, we present the experimental setting for this task

Corpus: SEAME (South East Asia Mandarin-English), a conversational Mandarin-English code-switching speech corpus consists of spontaneously spoken interviews and conversations BIBREF8 . 
Output: What languages are explored in the work?
We test our proposed approach on three public short text datasets.  SearchSnippets. This dataset was selected from the results of web search transaction using predefined phrases of 8 different domains by Phan et al. BIBREF41 . StackOverflow. We use the challenge data published in Kaggle.com.  Biomedical. We use the challenge data published in BioASQ's official website. 
Output: What datasets did they use?
Our idea is the BQ generation for MQ and, at the same time, we only want the minimum number of BQ to represent the MQ, so modeling our problem as $LASSO$ optimization problem is an appropriate way
Output: What they formulate the question generation as?
The model is sequential in nature, consisting of the following steps: read text, process it into a dynamic relational memory and then attention conditioned on the question generates the answer. We model the dynamic memory in a fashion similar to Recurrent Entity Networks BIBREF17 and then equip it with an additional relational memory.
Output: What is the architecture of the neural network?
A gated neural network is employed over character embeddings to generate distributed representations of candidate words, which are sent to a LSTM model.
Output: Which neural network architectures are employed?
In this work, we have restricted ourselves to the same datasets as BIBREF7 . These include nine (real-valued) numerical features, which are latitude, longitude, elevation, population, and five climate related features (avg. temperature, avg. precipitation, avg. solar radiation, avg. wind speed, and avg. water vapor pressure). In addition, 180 categorical features were used, which are CORINE land cover classes at level 1 (5 classes), level 2 (15 classes) and level 3 (44 classes) and 116 soil types (SoilGrids). Note that each location should belong to exactly 4 categories: one CORINE class at each of the three levels and a soil type.
Output: what dataset is used in this paper?
For the English version, we performed both a thorough manual analysis and automatic evaluation across three commonly used TS datasets from two different domains  For the English version, we performed both a thorough manual analysis and automatic evaluation across three commonly used TS datasets from two different domains  The evaluation of the German version is in progress.
Output: What are the corpora used for the task?
The total F-1 score on the OntoNotes dataset is 88%, and the total F-1 cross-validation score on the 112 class Wiki(gold) dataset is 53%. 
Output: What results do they achieve using their proposed approach?
Especially for Davidson-dataset, some tweets with specific language (written within the African American Vernacular English) and geographic restriction (United States of America) are oversampled such as tweets containing disparage words “nigga", “faggot", “coon", or “queer", result in high rates of misclassification. However, these misclassifications do not confirm the low performance of our classifier because annotators tended to annotate many samples containing disrespectful words as hate or offensive without any presumption about the social context of tweeters such as the speaker’s identity or dialect, whereas they were just offensive or even neither tweets. 
Output: What biases does their model capture?
Since, we there was no publicly available standard Nepali NER dataset and did not receive any dataset from the previous researchers, we had to create our own dataset. This dataset contains the sentences collected from daily newspaper of the year 2015-2016. 
Output: What is the source of their dataset?
Suppose there are $M$ total layers, and define a subset of these layers at which to apply the loss function: $K = \lbrace k_1, k_2, ..., k_L\rbrace \subseteq \lbrace 1,..,M-1\rbrace $. The total objective function is then defined as

where $Z_{k_l}$ is the $k_l$-th Transformer layer activations, $Y$ is the ground-truth transcription for CTC and context dependent states for hybrid ASR, and $Loss(P, Y)$ can be defined as CTC objective BIBREF11 or cross entropy for hybrid ASR. T
Output: Do they just sum up all the loses the calculate to end up with one single loss?
We examine a deep-learning approach to sequence labeling using a vanilla recurrent neural network (RNN) with word embeddings, as well as a joint inference, structured prediction approach using Stanford's knowledge base construction framework DeepDive BIBREF1 .
Output: Which structured prediction approach do they adopt for temporal entity extraction?
Note that ESIM models have two LSTM layers, the first (input) LSTM performs the input encoding and the second (inference) LSTM generates the representation for inference.
Output: How many layers are there in their model?
We achieve significant gains over our strong baseline by leveraging decompositions from our best decomposition model, trained with USeq2Seq on FastText pseudo-decompositions; we find a 3.1 F1 gain on the original dev set, 11 F1 gain on the multi-hop dev set, and 10 F1 gain on the out-of-domain dev set. 
Output: How large is the improvement over the baseline?
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.
Output: How do they select monotonicity facts?
Methods ::: Combining the two methods
We further propose to use the two methods together to combine their strengths. In fact, while the length token acts as a soft constraint to bias NMT to produce short or long translation with respect to the source, actually no length information is given to the network. On the other side, length encoding leverages information about the target length, but it is agnostic of the source length.
Output: Do they experiment with combining both methods?
The problem with morpheme segmentation is that the vocabulary of stem units is still very large, which leads to many rare and unknown words at the training time.  Therefore, we learn a BPE model on the stem units in the training corpus rather than the words, and then apply it on the stem unit of each word after morpheme segmentation.
Output: How is morphology knowledge implemented in the method?
We evaluate an RNN model which uses bidirectionally summed GRU memory cells BIBREF18 and uses the final states as embeddings; a CNN model which uses sentence-max-pooled convolutional filters as embeddings BIBREF19 ; an RNN-CNN model which puts the CNN on top of per-token GRU outputs rather than the word embeddings BIBREF20 ; and an attn1511 model inspired by BIBREF20 that integrates the RNN-CNN model with per-word attention to build hypothesis-specific evidence embeddings. We also report the baseline results of avg mean of word embeddings in the sentence with projection matrix and DAN Deep Averaging Network model that employs word-level dropout and adds multiple nonlinear transformations on top of the averaged embeddings BIBREF21 .
Output: what were the baselines?
The corpus used for sentiment analysis is the IMDb dataset of movie reviews by BIBREF11 while that for NER is Groningen Meaning Bank (GMB) by BIBREF12, containing 47,959 sentence samples.
Output: What sentiment analysis dataset is used?
Intuitively, when the Ranker makes more accurate passage selections, the Reasoner will work with less noisy data and thus is easier to succeed. Specifically, the Reasoner learns to extract the linking entity from chains selected by a well-trained Ranker, and it benefits the Ranker training by providing extra rewards.
Output: How do two models cooperate to select the most confident chains?
The datasets used for training, validation and testing contain sentences extracted from the Europarl corpus BIBREF1 and SoNaR corpus BIBREF2. The Europarl corpus is an open-source parallel corpus containing proceedings of the European Parliament. The Dutch section consists of 2,333,816 sentences and 53,487,257 words. The SoNaR corpus comprises two corpora: SONAR500 and SONAR1. The SONAR500 corpus consists of more than 500 million words obtained from different domains.
Output: What are the sizes of both datasets?
We compare our model with several baselines including:

Attn seq2seq BIBREF22: A model with simple attention over the input context at each time step during decoding.

Ptr-UNK BIBREF23: Ptr-UNK is the model which augments a sequence-to-sequence architecture with attention-based copy mechanism over the encoder context.

KV Net BIBREF6: The model adopted and argumented decoder which decodes over the concatenation of vocabulary and KB entities, which allows the model to generate entities.

Mem2Seq BIBREF7: Mem2Seq is the model that takes dialogue history and KB entities as input and uses a pointer gate to control either generating a vocabulary word or selecting an input as the output.

DSR BIBREF9: DSR leveraged dialogue state representation to retrieve the KB implicitly and applied copying mechanism to retrieve entities from knowledge base while decoding.
Output: What were the baseline systems?
Both systems were optimized on the tst2014 using Minimum error rate training BIBREF20 . A detailed description of the systems can be found in BIBREF21 .
Output: How is the PBMT system trained?
Vanilla ST baseline: The vanilla ST BIBREF9 has only a speech encoder and a decoder. It is trained from scratch on the ST-TED corpus.

Pre-training baselines: We conduct three pre-training baseline experiments: 1) encoder pre-training, in which the ST encoder is initialized from an ASR model; 2) decoder pre-training, in which the ST decoder is initialized from an MT model; and 3) encoder-decoder pre-training, where both the encoder and decoder are pre-trained. The ASR model has the same architecture with vanilla ST model, trained on the mixture of ST-TED and TED-LIUM2 corpus. The MT model has a text encoder and decoder with the same architecture of which in TCEN. It is first trained on WMT data (out-of-domain) and then fine-tuned on in-domain data.

Multi-task baselines: We also conduct three multi-task baseline experiments including one-to-many setting, many-to-one setting, and many-to-many setting. In the first two settings, we train the model with $\alpha _{st}=0.75$ while $\alpha _{asr}=0.25$ or $\alpha _{mt}=0.25$. For many-to-many setting, we use $\alpha _{st}=0.6, \alpha _{asr}=0.2$ and $\alpha _{mt}=0.2$.. For MT task, we use only in-domain data.

Many-to-many+pre-training: We train a many-to-many multi-task model where the encoders and decoders are derived from pre-trained ASR and MT models. Triangle+pre-train: BIBREF18 DBLP:conf/naacl/AnastasopoulosC18 proposed a triangle multi-task strategy for speech translation. Their model solves the subnet waste issue by concatenating an ST decoder to an ASR encoder-decoder model. Notably, their ST decoder can consume representations from the speech encoder as well as the ASR decoder. For a fair comparison, the speech encoder and the ASR decoder are initialized from the pre-trained ASR model. The Triangle model is fine-tuned under their multi-task manner.
Output: What are the baselines?
As shown in Figure FIGREF7 , KAR is an end-to-end MRC model consisting of five layers:

Lexicon Embedding Layer. This layer maps the words to the lexicon embeddings. Context Embedding Layer. This layer maps the lexicon embeddings to the context embeddings. Coarse Memory Layer. This layer maps the context embeddings to the coarse memories. Refined Memory Layer. This layer maps the coarse memories to the refined memories. Answer Span Prediction Layer. This layer predicts the answer start position and the answer end position based on the above layers.
Output: What type of model is KAR?
We modeled the relationship between word count and the two metrics of user engagement (overall rating, mean number of turns) in separate linear regressions.
Output: What are all the metrics to measure user engagement?
We obtained 9,892 stories of sexual harassment incidents that was reported on Safecity. Those stories include a text description, along with tags of the forms of harassment, e.g. commenting, ogling and groping. A dataset of these stories was published by Karlekar and Bansal karlekar2018safecity. In addition to the forms of harassment, we manually annotated each story with the key elements (i.e. “harasser", “time", “location", “trigger"), because they are essential to uncover the harassment patterns. An example is shown in Figure FIGREF3. Furthermore, we also assigned each story classification labels in five dimensions (Table TABREF4). The detailed definitions of classifications in all dimensions are explained below.
Output: What is the size of the dataset?
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. Later, we manually label tweets with and labels for classification as stage one. Our dataset contains 1313 tweet with positive label and 1887 tweets with a negative label . We create another dataset with the positively labeled tweets and provide them with category labels like “fire”, “accident”, “earthquake” etc.
Output: Are the tweets specific to a region?
We pit our model against the following baselines: 1) SVM with unigram, bigram, and trigram features, which is a standard yet rather strong classifier for text features; 2) SVM with average word embedding, where a document is represented as a continuous representation by averaging the embeddings of the composite words; 3) SVM with average transformed word embeddings (the INLINEFORM0 in equation EQREF6 ), where a document is represented as a continuous representation by averaging the transformed embeddings of the composite words; 4) two mature deep learning models on text classification, CNN BIBREF3 and Recurrent Convolutional Neural Networks (RCNN) BIBREF0 , where the hyperparameters are based on their work; 5) the above SVM and deep learning models with comment information; 
Output: What are the baselines?
We explored different normalization techniques. FBanks with cepstral mean normalization (CMN) perform better than raw FBanks. We found using variance with mean normalization (CMVN) unnecessary for the task. Using deltas and delta-deltas improves model, so we used them in other experiments. Models trained with spectrogram features converge slower and to worse minimum, but the difference when using CMN is not very big compared to FBanks.
Output: What normalization techniques are mentioned?
To have an estimation about human performance in each metric, we iteratively treat every reference sentence in dev/test data as the prediction to be compared with all references (including itself). That is, if a model has the same reasoning ability with average performance of our crowd workers, its results should exceed this “human bound”.
Output: What measures were used for human evaluation?
Training Dataset UM Inventory BIBREF5 is a public dataset created by researchers from the University of Minnesota, containing about 37,500 training samples with 75 abbreviation terms. Existing work reports abbreviation disambiguation results on 50 abbreviation terms BIBREF6, BIBREF5, BIBREF17. However, after carefully reviewing this dataset, we found that it contains many samples where medical professionals disagree: wrong samples and uncategorized samples. Due to these mistakes and flaws of this dataset, we removed the erroneous samples and eventually selected 30 abbreviation terms as our training dataset that can be made public.
Output: What existing dataset is re-examined and corrected for training?
We used two strategies in combining prediction results of two types of models. Specifically, the Max Score Ensemble model made the final decisions based on the maximum of two scores assigned by the two separate models; instead, the Average Score Ensemble model used the average score to make final decisions.
Output: How do they combine the models?
First, in order to deal with the unseen relations, we propose to break the relation names into word sequences for question-relation matching. Second, noticing that original relation names can sometimes help to match longer question contexts, we propose to build both relation-level and word-level relation representations. Third, we use deep bidirectional LSTMs (BiLSTMs) to learn different levels of question representations in order to match the different levels of relation information. Finally, we propose a residual learning method for sequence matching, which makes the model training easier and results in more abstract (deeper) question representations, thus improves hierarchical matching.
Output: What they use in their propsoed framework?
Multi-task Pairwise Neural Ranking
We propose a multi-task pairwise neural ranking approach to better incorporate and distinguish the relative order between the candidate segmentations of a given hashtag. Our model adapts to address single- and multi-token hashtags differently via a multi-task learning strategy without requiring additional annotations. In this section, we describe the task setup and three variants of pairwise neural ranking models (Figure FIGREF11 ). Pairwise Neural Ranking Model Margin Ranking (MR) Loss Adaptive Multi-task Learning
Output: What set of approaches to hashtag segmentation are proposed?
We see that gated architectures almost always outperform recurrent, attention and linear models BoW, TFIDF, PV. This is largely because while training and testing on same domains, these models especially recurrent and attention based may perform better. However, for Domain Adaptation, as they lack gated structure which is trained in parallel to learn importance, their performance on target domain is poor as compared to gated architectures. As gated architectures are based on convolutions, they exploit parallelization to give significant boost in time complexity as compared to other models. The effectiveness of gated architectures rely on the idea of training a gate with sole purpose of identifying a weightage. In the task of sentiment analysis this weightage corresponds to what weights will lead to a decrement in final loss or in other words, most accurate prediction of sentiment. In doing so, the gate architecture learns which words or n-grams contribute to the sentiment the most, these words or n-grams often co-relate with domain independent words. On the other hand the gate gives less weightage to n-grams which are largely either specific to domain or function word chunks which contribute negligible to the overall sentiment. This is what makes gated architectures effective at Domain Adaptation.
Output: Does the fact that GCNs can perform well on this tell us that the task is simpler than previously thought?
Each game's video ranges from 30 to 50 minutes in length which contains image and chat data linked to the specific timestamp of the game.
Output: What is the average length of the recordings?
For the task-oriented system, although there are some objective evaluation metrics, such as the number of turns in a dialogue, the ratio of task completion, etc., there is no gold standard for automatically evaluating two (or more) dialogue systems when considering the satisfaction of the human and the fluency of the generated dialogue.
Output: What problems are found with the evaluation scheme?
BIBREF0 (BIBREF0) developed Moral Choice Machine computes the cosine similarity in a sentence embedding space of an arbitrary action embedded in question/answer pairs.
Output: What is the Moral Choice Machine?
Defining Faithfulness ::: Assumption 1 (The Model Assumption).
Two models will make the same predictions if and only if they use the same reasoning process. Defining Faithfulness ::: Assumption 2 (The Prediction Assumption).
On similar inputs, the model makes similar decisions if and only if its reasoning is similar. Defining Faithfulness ::: Assumption 3 (The Linearity Assumption).
Certain parts of the input are more important to the model reasoning than others. Moreover, the contributions of different parts of the input are independent from each other.
Output: Which are three assumptions in current approaches for defining faithfulness?
The participants are asked to score each summary on three indicators: relevance, conciseness and readability on a 1 to 5 scale, and rank the summary pair (tie allowed). We observe that SPNet reaches the highest score in both ROUGE and CIC
Output: What automatic and human evaluation metrics are used to compare SPNet to its counterparts?
For estimating the sense of the word INLINEFORM0 in a sentence, we search for such a synset INLINEFORM1 that maximizes the cosine similarity to the sentence vector: DISPLAYFORM0
Output: What measure of semantic similarity is used?
For the overlapped speech recognition problem, the conditional independence assumption in the output label streams is still made as in Equation ( 5 ). Then the cross-entropy based PIT can be transformed to sequence discriminative criterion based PIT as below,

$$\begin{split} \mathcal {J}_{\text{SEQ-PIT}}=\sum _u \min _{s^{\prime }\in \mathbf {S}} \frac{1}{N} \sum _{n\in [1,N]}-\mathcal {J}_{\text{SEQ}}(\mathbf {L}_{un}^{(s^{\prime })},\mathbf {L}_{un}^{(r)}) \end{split}$$ (Eq. 44)

Different from Equation ( 7 ), the best permutation is decided by $\mathcal {J}_{\text{SEQ}}(\mathbf {L}_{un}^{(s^{\prime })},\mathbf {L}_{un}^{(r)})$ , which is the sequence discriminative criterion of taking the $s^{\prime }$ -th permutation in $n$ -th output inference stream at utterance $u$ . Similar to CE-PIT, $\mathcal {J}_{\text{SEQ}}$ of all the permutations are calculated and the minimum permutation is taken to do the optimization.
Output: How is the discriminative training formulation different from the standard ones?
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.
Output: Did participants behave unexpectedly?
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.
Output: Which is the baseline model?
Since the ground-truth reference captions in ShapeWorld are randomly sampled, we take the sampled captions accompanying the test images as a proxy for optimal caption diversity, and compare it with the empirical output diversity of the evaluated model on these test images. Practically, we look at language constructions used and compute the corresponding diversity score as the ratio of observed number versus optimal number
Output: How is diversity measured?
We use three encoder-decoder neural architectures as baselines: (1) LSTM+attention as an LSTM BIBREF19 with attention mechanism BIBREF20; (2) Transformer BIBREF21 and (3) Universal Transformer BIBREF22.
Output: What three machine architectures are analyzed?
The bossa-nova and jovem-guarda genres, which have few instances in the dataset, are among the most difficult ones to classify using the model.
Output: what genre was the most difficult to classify?
The median age is 17 for depressed class versus 19 for control class suggesting either likely depressed-user population is younger, or depressed youngsters are more likely to disclose their age for connecting to their peers (social homophily.) Our findings are consistent with the medical literature BIBREF10 as according to BIBREF52 more women than men were given a diagnosis of depression.
Output: What insights into the relationship between demographics and mental health are provided?
Our Transformer Transducer model architecture has 18 audio and 2 label encoder layers. Every layer is identical for both audio and label encoders. The details of computations in a layer are shown in Figure FIGREF10 and Table TABREF11. All the models for experiments presented in this paper are trained on 8x8 TPU with a per-core batch size of 16 (effective batch size of 2048).
Output: Does model uses pretrained Transformer encoders?
Here we consider five baselines to compare with GraLap: (i) Uniform: assign 3 to all the references assuming equal intensity, (ii) SVR+W: recently proposed Support Vector Regression (SVR) with the feature set mentioned in BIBREF4 , (iii) SVR+O: SVR model with our feature set, (iv) C4.5SSL: C4.5 semi-supervised algorithm with our feature set BIBREF23 , and (v) GLM: the traditional graph-based LP model with our feature set BIBREF9 .
Output: What are the baselines model?
The resulting dataset is nearly balanced, with 52.3% of the data (1,857 instances) labeled stressful.
Output: Is the dataset balanced across categories?
We hypothesized that question answering can benefit from synergistic interaction between the two tasks through parameter sharing and joint training under this multitask setting.
Output: Which components of QA and QG models are shared during training?
We obtain word vectors of size 300 from the learned word embeddings. To represent a Twitter profile, we retrieve word vectors for all the words that appear in a particular profile including the words appear in tweets, profile description, words extracted from emoji, cover and profile images converted to textual formats, and words extracted from YouTube video comments and descriptions for all YouTube videos shared in the user's timeline. Those word vectors are combined to compute the final feature vector for the Twitter profile.
Output: How in YouTube content translated into a vector format?
In this work, the metrics detailed below are proposed and we evaluate their quality through a human evaluation in subsection SECREF32. In addition to the automatic metrics, we proceeded to a human evaluation. We chose to use the data from our SQuAD-based experiments in order to also to measure the effectiveness of the proposed approach to derive Curiosity-driven QG data from a standard, non-conversational, QA dataset. We randomly sampled 50 samples from the test set. Three professional English speakers were asked to evaluate the questions generated by: humans (i.e. the reference questions), and models trained using pre-training (PT) or (RL), and all combinations of those methods.

Before submitting the samples for human evaluation, the questions were shuffled. Ratings were collected on a 1-to-5 likert scale, to measure to what extent the generated questions were: answerable by looking at their context; grammatically correct; how much external knowledge is required to answer; relevant to their context; and, semantically sound. The results of the human evaluation are reported in Table TABREF33.
Output: How they evaluate quality of generated output?
With the purpose of comparing the proposed model with a popular state-of-the-art approach for image classification, for the LabelMe dataset, the following baseline was introduced:

Bosch 2006 (mv): This baseline is similar to one in BIBREF33 . The authors propose the use of pLSA to extract the latent topics, and the use of k-nearest neighbor (kNN) classifier using the documents' topics distributions. For this baseline, unsupervised LDA is used instead of pLSA, and the labels from the different annotators for kNN (with INLINEFORM0 ) are aggregated using majority voting (mv).

The results obtained by the different approaches for the LabelMe data are shown in Fig. FIGREF94 , where the svi version is using mini-batches of 200 documents.

Analyzing the results for the Reuters-21578 and LabelMe data, we can observe that MA-sLDAc outperforms all the baselines, with slightly better accuracies for the batch version, especially in the Reuters data. Interestingly, the second best results are consistently obtained by the multi-annotator approaches, which highlights the need for accounting for the noise and biases of the answers of the different annotators. Both the batch and the stochastic variational inference (svi) versions of the proposed model (MA-sLDAc) are compared with the following baselines:

[itemsep=0.02cm]

LDA + LogReg (mv): This baseline corresponds to applying unsupervised LDA to the data, and learning a logistic regression classifier on the inferred topics distributions of the documents. The labels from the different annotators were aggregated using majority voting (mv). Notice that, when there is a single annotator label per instance, majority voting is equivalent to using that label for training. This is the case of the 20-Newsgroups' simulated annotators, but the same does not apply for the experiments in Section UID89 .

LDA + Raykar: For this baseline, the model of BIBREF21 was applied using the documents' topic distributions inferred by LDA as features.

LDA + Rodrigues: This baseline is similar to the previous one, but uses the model of BIBREF9 instead.

Blei 2003 (mv): The idea of this baseline is to replicate a popular state-of-the-art approach for document classification. Hence, the approach of BIBREF0 was used. It consists of applying LDA to extract the documents' topics distributions, which are then used to train a SVM. Similarly to the previous approach, the labels from the different annotators were aggregated using majority voting (mv).

sLDA (mv): This corresponds to using the classification version of sLDA BIBREF2 with the labels obtained by performing majority voting (mv) on the annotators' answers.
Output: what are the state of the art approaches?
Furthermore, the gains in WER over the baseline are significantly larger for the Density Ratio method than for Shallow Fusion, with up to 28% relative reduction in WER (17.5% $\rightarrow $ 12.5%) compared to up to 17% relative reduction (17.5% $\rightarrow $ 14.5%) for Shallow Fusion, in the no fine-tuning scenario.
Output: What metrics are used for evaluation?
Starting with this list, we can locate the profile page for a user, and subsequently extract additional information, which includes fields such as name, email, occupation, industry, and so forth. We also generate two maps that delineate the gender distribution in the dataset. Our dataset provides mappings between location, profile information, and language use, which we can leverage to generate maps that reflect demographic, linguistic, and psycholinguistic properties of the population represented in the dataset.
Output: Which demographic dimensions of people do they obtain?
We evaluate performance of our models on the SQUAD BIBREF16 dataset (denoted $\mathcal {S}$ ). We use the same split as that of BIBREF4 , where a random subset of 70,484 instances from $\mathcal {S}\ $ are used for training ( ${\mathcal {S}}^{tr}$ ), 10,570 instances for validation ( ${\mathcal {S}}^{val}$ ), and 11,877 instances for testing ( ${\mathcal {S}}^{te}$ ).
Output: Which datasets are used to train this model?
We proposed to use visual renderings of documents to capture implicit document quality indicators, such as font choices, images, and visual layout, which are not captured in textual content. We applied neural network models to capture visual features given visual renderings of documents. Experimental results show that we achieve a 2.9% higher accuracy than state-of-the-art approaches based on textual features over Wikipedia, and performance competitive with or surpassing state-of-the-art approaches over arXiv. We further proposed a joint model, combining textual and visual representations, to predict the quality of a document. 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. These results underline the feasibility of assessing document quality via visual features, and the complementarity of visual and textual document representations for quality assessment.
Output: Which is more useful, visual or textual features?
the basic model yields good performance for recognizing explicit discourse relations as well, which is comparable with previous best result (92.05% macro F1-score and 93.09% accuracy as reported in BIBREF11 ). After untying parameters in the softmax prediction layer, implicit discourse relation classification performance was improved across all four relations, meanwhile, the explicit discourse relation classification performance was also improved. Then we also created ensemble models by applying majority voting to combine results of ten runs. From table 5 , each ensemble model obtains performance improvements compared with single model. The full model achieves performance boosting of (51.84 - 48.82 = 3.02) and (94.17 - 93.21 = 0.96) in macro F1-scores for predicting implicit and explicit discourse relations respectively. 
Output: What discourse relations does it work best/worst for?
As shown in Table TABREF3 , we collect Amazon review keywords for 2,896 e-books (publishers: Kiwi, Rowohlt, Fischer, and Droemer), which leads to 33,663 distinct review keywords and on average 30 keyword assignments per e-book.
Output: how large is the vocabulary?
In the sixth international workshop on Vietnamese Language and Speech Processing (VLSP 2019), the Hate Speech Detection (HSD) task is proposed as one of the shared-tasks to handle the problem related to controlling content in SNSs. The fundamental idea of this system is how to make a system that has the diversity of viewing an input. That because of the variety of the meaning in Vietnamese language especially with the acronym, teen code type.
Output: Is the data all in Vietnamese?