niryuu/sni-each-converted huggingface dataset:
It is true that the above-mentioned associated caption sentences for each concept-set are human-written and do describe scenes that cover all given concepts. However, they are created under specific contexts (i.e. an image or a video) and thus might be less representative for common sense. To better measure the quality and interpretability of generative reasoners, we need to evaluate them with scenes and rationales created by using concept-sets only as the signals for annotators.

We collect more human-written scenes for each concept-set in dev and test set through crowd-sourcing via the Amazon Mechanical Turk platform. Each input concept-set is annotated by at least three different humans. The annotators are also required to give sentences as the rationales, which further encourage them to use common sense in creating their scenes.
Output: Are the sentences in the dataset written by humans who were shown the concept-sets?
By choosing English (En) as the pivot language, we perform pivot alignments for identical English segments on Europarl Fr-En and En-De parallel corpora BIBREF18 , constructing a multi-parallel corpus of Fr-En-De. Then each of the Fr*-De and Fr-De* pseudo parallel corpora is established from the multi-parallel data by applying the pivot language-based translation described in the previous section.
Output: How do they align the synthetic data?
For training, three types of Recurrent Neural Network (RNN) layers are used – an encoder layer, a decoder layer and an output layer. These layers together form a LSTM model. LSTM is typically used in seq2seq translation.
Output: What is the architecture of the system?
Future work can be improving the sensationalism scorer and investigating the applications of dynamic balancing methods between RL and MLE in textGANBIBREF23. Our work also raises the ethical questions about generating sensational headlines, which can be further explored.
Output: What is future work planed?
We create a new manually annotated multimodal hate speech dataset formed by $150,000$ tweets, each one of them containing text and an image. We call the dataset MMHS150K, and made it available online . In this section, we explain the dataset creation steps.
Output: How many tweats does MMHS150k contains, 150000?
In this step we extract the AMR graphs of the summary sentences using story sentence AMRs. We divide this task in two parts. First is finding the important sentences from the story and then extracting the key information from those sentences using their AMR graphs.
Output: How are sentences selected from the summary graph?
We trained a logistic regression baseline model (line 1 in Table TABREF10 ) using character ngrams and word unigrams using TF*IDF weighting BIBREF13 , to provide a baseline since HAR has no reported results. For the SR and HATE datasets, the authors reported their trained best logistic regression model's results on their respective datasets.
Output: what was the baseline?
TF-IDF (Lexical, Non-Neural) is an important unsupervised baseline. NVDM (Lexical, Neural) is a VAE-based approach for document modeling BIBREF10 .
Output: Which lexicon-based models did they compare with?
Each training sample is generated by three steps: align, mask and select, which we call as AMS method. Each sample in the dataset consists of a question and several candidate answers, which has the same form as the CommonsenseQA dataset.
Output: How do they select answer candidates for their QA task?
The results of multilingual training in which the modeling unit is syllables are presented in Table 5. All error rates are the weighted averages of all evaluated speakers. Here, `+ both' represents the result of training with both JNAS and WSJ corpora. The multilingual training is effective in the speaker-open setting, providing a relative WER improvement of 10%. The JNAS corpus was more helpful than the WSJ corpus because of the similarities between Ainu and Japanese language.
Output: How big are improvements with multilingual ASR training vs single language training?
While the dataset was collected with simulated environments, no structure was imposed on the navigation instructions while crowd-sourcing data. Thus, many instructions in our dataset are ambiguous. Moreover, the order of the behaviors in the instructions is not always the same. For instance, a person said “turn right and advance” to describe part of a route, while another person said “go straight after turning right” in a similar situation. The high variability present in the natural language descriptions of our dataset makes the problem of decoding instructions into behaviors not trivial. See Appendix A of the supplementary material for additional details on our data collection effort.
Output: What language is the experiment done in?
We acquired the data in two rounds of annotation. In the first one, we were looking for original and uncommon sentence change suggestions.
Output: How do they introduce language variation?
In this work, we make use of the widely-recognized state of the art entailment technique – BERT BIBREF18, and train it on three mainstream entailment datasets: MNLI BIBREF19, GLUE RTE BIBREF20, BIBREF21 and FEVER BIBREF22, respectively. We convert all datasets into binary case: “entailment” vs. “non-entailment”, by changing the label “neutral” (if exist in some datasets) into “non-entailment”.

For our label-fully-unseen setup, we directly apply this pretrained entailment model on the test sets of all $\textsc {0shot-tc}$ aspects. For label-partially-unseen setup in which we intentionally provide annotated data, we first pretrain BERT on the MNLI/FEVER/RTE, then fine-tune on the provided training data.
Output: Do they use pretrained models?
We compare our method to the following baselines: (1) Single-task CNN: training a CNN model for each task individually; (2) Single-task FastText: training one FastText model BIBREF23 with fixed embeddings for each individual task; (3) Fine-tuned the holistic MTL-CNN: a standard transfer-learning approach, which trains one MTL-CNN model on all the training tasks offline, then fine-tunes the classifier layer (i.e. $\mathrm {M}^{(cls)}$ Figure 1 (a)) on each target task; (4) Matching Network: a metric-learning based few-shot learning model trained on all training tasks; (5) Prototypical Network: a variation of matching network with different prediction function as Eq. 9 ; (6) Convex combining all single-task models: training one CNN classifier on each meta-training task individually and taking the encoder, then for each target task training a linear combination of all the above single-task encoders with Eq. ( 24 ). 
Output: Do they compare with the MAML algorithm?
Since INLINEFORM0 is constant, we only need to minimize INLINEFORM1 , therefore the loss function becomes: DISPLAYFORM0
Output: How is the expectation regularization loss defined?
For a given transcribed utterance, it is firstly encoded with Byte Pair Encoding (BPE) BIBREF14, a compression algorithm splitting words to fundamental subword units (pairs of bytes or BPs) and reducing the embedded vocabulary size. Then we use a BiLSTM BIBREF15 encoder and the output state of the BiLSTM is regarded as a vector representation for this utterance. Finally, a fully connected Feed-forward Neural Network (FNN) followed by a softmax layer, labeled as a multilayer perceptron (MLP) module, is used to perform the domain/intent classification task based on the vector. We name it Oracle simply because we assume that hypotheses are noisy versions of transcription.
Output: Which ASR system(s) is used in this work?
We manually inspect 148 samples from the seen part of the test set, containing 440 relations, counting expressed, omitted, wrong and over-generated (hallucinated) facts.
Output: How is faithfulness of the resulting text evaluated?
In contrast to other work, we do not show the documents to the workers at all, but provide only a description of the document cluster's topic along with the propositions. This ensures that tasks are small, simple and can be done quickly (see Figure FIGREF4 ).
Output: How were crowd workers instructed to identify important elements in large document collections?
In tab:blueperpl we report BLEU scores for the reconstruction of test set sentences from their content and feature representations, as well as the model perplexities of the reconstruction. For both models, we use beam decoding with a beam size of eight. Note that for both models, the all and top classification accuracy tends to be quite similar, though for the Baseline they are often almost exactly the same when the Baseline has little to no diversity in the outputs.
Output: What metrics are used for automatic evaluation?
A “coding scheme” is defined, which is a set of labels, annotations, or codes that items in the corpus may have. Schemes include formal definitions or procedures, and often include examples, particularly for borderline cases. Next, coders are trained with the coding scheme, which typically involves interactive feedback. Training sometimes results in changes to the coding scheme, in which the first round becomes a pilot test. Then, annotators independently review at least a portion of the same items throughout the entire process, with a calculation of “inter-annotator agreement” or “inter-rater reliability.” Finally, there is a process of “reconciliation” for disagreements, which is sometimes by majority vote without discussion and other times discussion-based.
Output: What are the core best practices of structured content analysis?
The goal of an language model is to assign meaningful probabilities to a sequence of words. Given a set of tokens $\mathbf {X}=(x_1,....,x_T)$, where $T$ is the length of a sequence, our task is to estimate the joint conditional probability $P(\mathbf {X})$ which is

were $(x_{1}, \ldots , x_{i-1})$ is the context. An Intrinsic evaluation of the performance of Language Models is perplexity (PPL) which is defined as the inverse probability of the set of the tokens and taking the $T^{th}$ root were $T$ is the number of tokens We propose an approximation of the joint probability as,

This type of approximations has been previously explored with Bi-directional RNN LM's BIBREF9 but not for deep transformer models. We therefore, define a pseudo-perplexity score from the above approximated joint probability.
Output: How is pseudo-perplexity defined?
Our in-house dataset includes manually annotated RE data for 6 languages: English, German, Spanish, Italian, Japanese and Portuguese. It defines 56 entity types (e.g., Person, Organization, Geo-Political Entity, Location, Facility, Time, Event_Violence, etc.) and 53 relation types between the entities (e.g., AgentOf, LocatedAt, PartOf, TimeOf, AffectedBy, etc.). The ACE05 dataset includes manually annotated RE data for 3 languages: English, Arabic and Chinese. It defines 7 entity types (Person, Organization, Geo-Political Entity, Location, Facility, Weapon, Vehicle) and 6 relation types between the entities (Agent-Artifact, General-Affiliation, ORG-Affiliation, Part-Whole, Personal-Social, Physical). the ACE (Automatic Content Extraction) 2005 multilingual dataset BIBREF11.
Output: What datasets are used?
INSIGHT 1: Political handles are more likely to engage in profile changing behavior as compared to their followers.  We analyze the trends in Figure FIGREF12 and find that the political handles do not change their usernames at all. This is in contrast to the trend in Figure FIGREF15 where we see that there are a lot of handles that change their usernames multiple times.  INSIGHT 3: Political handles tend to make new changes related to previous attribute values. However, the followers make comparatively less related changes to previous attribute values.
Output: How do profile changes vary for influential leads and their followers over the social movement?
Thus, to limit the number of different inputs to the classifier, we wish to reduce the number of distinct word recognition outputs that an attacker can induce, not just the number of words on which the model is “fooled”. We denote this property of a model as its sensitivity.
Output: What does the "sensitivity" quantity denote?
However, recent work has found that many NLI datasets contain biases, or annotation artifacts, i.e., features present in hypotheses that enable models to perform surprisingly well using only the hypothesis, without learning the relationship between two texts BIBREF2 , BIBREF3 , BIBREF4 . For instance, in some datasets, negation words like “not” and “nobody” are often associated with a relationship of contradiction. As a ramification of such biases, models may not generalize well to other datasets that contain different or no such biases.
Output: Is such bias caused by bad annotation?
We employ a multi-class Naive Bayes classifier as the second stage classification mechanism, for categorizing tweets appropriately, depending on the type of emergencies they indicate.
Output: What classifier is used for emergency categorization?
In this section, we recommend a set of evaluation design changes that we believe are needed for assessing human–machine parity, and will strengthen the human evaluation of MT in general.

Recommendations ::: (R1) Choose professional translators as raters.
In our blind experiment (Section SECREF3), non-experts assess parity between human and machine translation where professional translators do not, indicating that the former neglect more subtle differences between different translation outputs.

Recommendations ::: (R2) Evaluate documents, not sentences.
When evaluating sentences in random order, professional translators judge machine translation more favourably as they cannot identify errors related to textual coherence and cohesion, such as different translations of the same product name. Our experiments show that using whole documents (i. e., full news articles) as unit of evaluation increases the rating gap between human and machine translation (Section SECREF4).

Recommendations ::: (R3) Evaluate fluency in addition to adequacy.
Raters who judge target language fluency without access to the source texts show a stronger preference for human translation than raters with access to the source texts (Sections SECREF4 and SECREF24). In all of our experiments, raters prefer human translation in terms of fluency while, just as in BIBREF3's BIBREF3 evaluation, they find no significant difference between human and machine translation in sentence-level adequacy (Tables TABREF21 and TABREF30). Our error analysis in Table TABREF34 also indicates that MT still lags behind human translation in fluency, specifically in grammaticality.

Recommendations ::: (R4) Do not heavily edit reference translations for fluency.
In professional translation workflows, texts are typically revised with a focus on target language fluency after an initial translation step. As shown in our experiment in Section SECREF24, aggressive revision can make translations more fluent but less accurate, to the degree that they become indistinguishable from MT in terms of accuracy (Table TABREF30).

Recommendations ::: (R5) Use original source texts.
Raters show a significant preference for human over machine translations of texts that were originally written in the source language, but not for source texts that are translations themselves (Section SECREF35). Our results are further evidence that translated texts tend to be simpler than original texts, and in turn easier to translate with MT.
Output: What recommendations do they offer?
For the CNN-Dailymail dataset, the Lead-3 model is considered a strong baseline; both the abstractive BIBREF16 and extractive BIBREF14 state-of-the art methods on this dataset beat this baseline only marginally. For the proxy report section of the AMR bank, we consider the Lead-1-AMR model as the baseline.
Output: Which other methods do they compare with?
We try 3 variants of each training set to fine-tune our models: (i) the original one in English (Orig), (ii) an English paraphrase of it generated through back-translation using Spanish or Finnish as pivot (BT-ES and BT-FI), and (iii) a machine translated version in Spanish or Finnish (MT-ES and MT-FI).
Output: What languages do they use in their experiments?
To train our model, we generated a dataset of 20,000 demonstrated 7 DOF trajectories (6 robot joints and 1 gripper dimension) in our simulated environment together with a sentence generator capable of creating natural task descriptions for each scenario. In order to create the language generator, we conducted an human-subject study to collect sentence templates of a placement task as well as common words and synonyms for each of the used features. By utilising these data, we are able to generate over 180,000 unique sentences, depending on the generated scenario. To test our model, we generated 500 new scenario testing each of the three features to identify the correct target among other bowls. 
Output: Does proposed end-to-end approach learn in reinforcement or supervised learning manner?
$\mathit {\texttt {+}PPMI}$ only falters on the RW and analogy tasks, and we hypothesize this is where $\mathit {\texttt {-}PMI}$ is useful: in the absence of positive information, negative information can be used to improve rare word representations and word analogies.
Output: What are the disadvantages to clipping negative PMI?
When testing across languages, we report accuracy for two setups: average accuracy over each single-language model (Avg), and accuracy obtained when training on the concatenation of all languages but the target one (All). The latter setting is also used for the embeddings model. We report accuracy for all experiments.
Output: What are the evaluation metrics used?
Knowledge Base Question Answering (KBQA) systems answer questions by obtaining information from KB tuples BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 .
Output: What is te core component for KBQA?
Drawing on the concept of variance in mathematics, local variance loss is defined as the reciprocal of its variance expecting the attention model to be able to focus on more salient parts. The standard variance calculation is based on the mean of the distribution. However, as previous work BIBREF15, BIBREF16 mentioned that the median value is more robust to outliers than the mean value, we use the median value to calculate the variance of the attention distribution. Thus, local variance loss can be calculated as:

where $\hat{\cdot }$ is a median operator and $\epsilon $ is utilized to avoid zero in the denominator.
Output: How do they define local variance?
The final code-mixed tweets were forwarded to a group of three annotators who were university students and fluent in both English and Hindi.
Output: How many annotators tagged each text?
Word Lattice
As shown in Figure FIGREF4 , a word lattice is a directed graph INLINEFORM0 , where INLINEFORM1 represents a node set and INLINEFORM2 represents a edge set. For a sentence in Chinese, which is a sequence of Chinese characters INLINEFORM3 , all of its possible substrings that can be considered as words are treated as vertexes, i.e. INLINEFORM4 . Then, all neighbor words are connected by directed edges according to their positions in the original sentence, i.e. INLINEFORM5 .
Output: How do they obtain word lattices from words?
Previous attempts to annotate QA-SRL initially involved trained annotators BIBREF4 but later resorted to crowdsourcing BIBREF5 to achieve scalability.
Output: How was the corpus obtained?
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.
Output: What neural network modules are included in NeuronBlocks?
We first experiment with a suite of non-neural models, including Support Vector Machines (SVMs), logistic regression, Naïve Bayes, Perceptron, and decision trees.  We finally experiment with neural models, although our dataset is relatively small. We train both a two-layer bidirectional Gated Recurrent Neural Network (GRNN) BIBREF20 and Convolutional Neural Network (CNN) (as designed in BIBREF21) with parallel filters of size 2 and 3, as these have been shown to be effective in the literature on emotion detection in text (e.g., BIBREF22, BIBREF23). 
Output: What supervised methods are used?
We chose three different datasets for the experiments: SNLI, MultiNLI and SICK. The Stanford Natural Language Inference (SNLI) corpus BIBREF4 is a dataset of 570k human-written sentence pairs manually labeled with the labels entailment, contradiction, and neutral.  The Multi-Genre Natural Language Inference (MultiNLI) corpus BIBREF5 consisting of 433k human-written sentence pairs labeled with entailment, contradiction and neutral. SICK BIBREF6 is a dataset that was originally constructed to test compositional distributional semantics (DS) models. 
Output: Which datasets were used?
As a result, the neutral class in SNLI-VE has substantial labelling errors. Vu BIBREF3 estimated ${\sim }31\%$ errors in this class, and ${\sim }1\%$ for the contradiction and entailment classes.
Output: What is the class with highest error rate in SNLI-VE?
image feature pre-selection part which models the tendency where people focus to ask questions We propose to perform saliency-like pre-selection operation to alleviate the problems and model the RoI patterns. The image is first divided into $g\times g$ grids as illustrated in Figure. 2 . Taking $m\times m$ grids as a region, with $s$ grids as the stride, we obtain $n\times n$ regions, where $n=\left\lfloor \frac{g-m}{s}\right\rfloor +1$ . We then feed the regions to a pre-trained ResNet BIBREF24 deep convolutional neural network to produce $n\times n\times d_I$ -dimensional region features, where $d_I$ is the dimension of feature from the layer before the last fully-connected layer.
Output: Does the new system utilize pre-extracted bounding boxes and/or features?
Conditional Random Fields
Conditional Random Fields (CRF) BIBREF10 are a standard approach when dealing with sequential data in the context of sequence labeling. BiLSTM-CRF
Prior to the emergence of deep neural language models, BiLSTM-CRF models BIBREF11 had achieved state-of-the-art results for the task of sequence labeling. Multi-Task Learning
Multi-Task Learning (MTL) BIBREF15 has become popular with the progress in deep learning. BioBERT
Deep neural language models have recently evolved to a successful method for representing text. In particular, Bidirectional Encoder Representations from Transformers (BERT) outperformed previous state-of-the-art methods by a large margin on various NLP tasks BIBREF17.
Output: What baseline systems are proposed?
To simulate the scenario, we split the Microsoft COCO dataset into food and non-food domain datasets. the food dataset has 3,806 images for training and 1,775 for validation. The non-food dataset has 78,976 images for training and 38,749 for validation. Adaptation to food domain captioning
Output: How many examples are there in the source domain?
The test-set accuracies obtained by different learning methods, including the current state-of-the-art results, are presented in Table TABREF11 .
Output: what models did they compare to?
In general, the model transforms the segments into object-pairs by the TC-CNN and passes sentence through bi-GRU to obtain the global representation. Then we integrate object-pairs with global representation and make a pair-wise inference to detect the relationship among the segments. Ablation studies show that the proposed SCRN at the segment level has the capacity for relational reasoning and promotes the result significantly.
Output: How is Relation network used to infer causality at segment level?
With this approach we improve the standard BERT models by up to four percentage points in accuracy.
Output: By how much do they outperform standard BERT?
Here is a list of architectures for which reference implementations and pretrained weights are currently provided in Transformers. These models fall into two main categories: generative models (GPT, GPT-2, Transformer-XL, XLNet, XLM) and models for language understanding (Bert, DistilBert, RoBERTa, XLM).

BERT (BIBREF13) is a bi-directional Transformer-based encoder pretrained with a linear combination of masked language modeling and next sentence prediction objectives.

RoBERTa (BIBREF5) is a replication study of BERT which showed that carefully tuning hyper-parameters and training data size lead to significantly improved results on language understanding.

DistilBERT (BIBREF32) is a smaller, faster, cheaper and lighter version BERT pretrained with knowledge distillation.

GPT (BIBREF34) and GPT2 (BIBREF9) are two large auto-regressive language models pretrained with language modeling. GPT2 showcased zero-shot task transfer capabilities on various tasks such as machine translation or reading comprehension.

Transformer-XL (BIBREF35) introduces architectural modifications enabling Transformers to learn dependency beyond a fixed length without disrupting temporal coherence via segment-level recurrence and relative positional encoding schemes.

XLNet (BIBREF4) builds upon Transformer-XL and proposes an auto-regressive pretraining scheme combining BERT's bi-directional context flow with auto-regressive language modeling by maximizing the expected likelihood over permutations of the word sequence.

XLM (BIBREF8) shows the effectiveness of pretrained representations for cross-lingual language modeling (both on monolingual data and parallel data) and cross-lingual language understanding.

We systematically release the model with the corresponding pretraining heads (language modeling, next sentence prediction for BERT) for adaptation using the pretraining objectives. Some models fine-tuned on downstream tasks such as SQuAD1.1 are also available. Overall, more than 30 pretrained weights are provided through the library including more than 10 models pretrained in languages other than English. Some of these non-English pretrained models are multi-lingual models (with two of them being trained on more than 100 languages) .
Output: What state-of-the-art general-purpose pretrained models are made available under the unified API?
The words are represented as fixed length vectors INLINEFORM0 resulting from the concatenation of GloVe pre-trained embeddings and DepecheMood BIBREF19 lexicon representation. Since we cannot directly concatenate token-based embeddings (provided in GloVe) with the lemma#PoS-based representation available in DepecheMood, we proceeded to re-build the latter in token-based form, applying the exact same methodology albeit with two differences: we started from a larger dataset (51.9K news articles instead of 25.3K) and used a frequency cut-off, i.e. keeping only those tokens that appear at least 5 times in the corpus.
Output: How do they incorporate lexicon into the neural network?
69.10%/78.38%
Output: how much of improvement the adaptation model can get?
However, adversarial misspellings constitute a longstanding real-world problem. Spammers continually bombard email servers, subtly misspelling words in efforts to evade spam detection while preserving the emails' intended meaning BIBREF1 , BIBREF2 .
Output: Why is the adversarial setting appropriate for misspelling recognition?
For all the tasks in our experimental study, we use 36 millions English tweets collected between August and September 2017. 
Output: Do they report results only on English datasets?
We use MS COCO, Bing and Flickr datasets from BIBREF26 to train the model that generates questions. These datasets contain natural questions about images with the purpose of knowing more about the picture. As can be seen in the Figure FIGREF8, questions cannot be answered by only looking at the image. Each source contains 5,000 images with 5 questions per image, adding a total of 15,000 images with 75,000 questions. We use two datasets to train our chatbot model. The first one is the Persona-chat BIBREF15 which contains dialogues between two people with different profiles that are trying to know each other. It is complemented by the Cornell-movie dialogues dataset BIBREF27, which contains a collection of fictional conversations extracted from raw movie scripts. Persona-chat's sentences have a maximum of 15 words, making it easier to learn for machines and a total of 162,064 utterances over 10,907 dialogues. While Cornell-movie dataset contains 304,713 utterances over 220,579 conversational exchanges between 10,292 pairs of movie characters.
Output: How big dataset is used for training this system?
Traditional text-to-speech (TTS) systems are composed of complex pipelines BIBREF0 , these often include accoustic frontends, duration model, acoustic prediction model and vocoder models. Neural text-to-speech systems have garnered large research interest in the past 2 years. The first to fully explore this avenue of research was Google's tacotron BIBREF1 system The architecture of our model utilizes RNN-based Seq2Seq model for generating mel spectrogram from text. The architecture is similar to that of Tacotron 2 BIBREF4 .  Direct comparison of model parameters between ours and the open-source tacotron 2, our model contains 4.5 million parameters, whereas the Tacotron 2 contains around 13 million parameters with default setting
Output: How do they measure the size of models?
We find that dynamic communities, such as Seahawks or starcraft, have substantially higher rates of monthly user retention than more stable communities (Spearman's INLINEFORM0 = 0.70, INLINEFORM1 0.001, computed with community points averaged over months; Figure FIGREF11 .A, left). Similarly, more distinctive communities, like Cooking and Naruto, exhibit moderately higher monthly retention rates than more generic communities (Spearman's INLINEFORM2 = 0.33, INLINEFORM3 0.001; Figure FIGREF11 .A, right). As with monthly retention, we find a strong positive relationship between a community's dynamicity and the average number of months that a user will stay in that community (Spearman's INLINEFORM0 = 0.41, INLINEFORM1 0.001, computed over all community points; Figure FIGREF11 .B, left). This verifies that the short-term trend observed for monthly retention translates into longer-term engagement and suggests that long-term user retention might be strongly driven by the extent to which a community continually provides novel content. Interestingly, there is no significant relationship between distinctiveness and long-term engagement (Spearman's INLINEFORM2 = 0.03, INLINEFORM3 0.77; Figure FIGREF11 .B, right). Thus, while highly distinctive communities like RandomActsOfMakeup may generate focused commitment from users over a short period of time, such communities are unlikely to retain long-term users unless they also have sufficiently dynamic content.
Output: How do the various social phenomena examined manifest in different types of communities?
This study assumes that a robot does not have any vocabularies in advance but can recognize syllables or phonemes.
Output: Does their model start with any prior knowledge of words?
Following titovcrosslingual, we run our experiments on the English (EN) and German (DE) sections of the CoNLL 2009 corpus BIBREF13 , and EN-DE section of the Europarl corpus BIBREF14 .
Output: Which parallel corpora are used?
Similarly the agents utterances can be clustered to identify system responses. However, we argue that rather than treating user utterances and agents responses in an isolated manner, there is merit in jointly clustering them. There is adjacency information of these utterances that can be utilized to identify better user intents and system responses.
Output: Do they study frequent user responses to help automate modelling of those?
For each task, the training data that was made available by the organizers is used, which is a selection of tweets with for each tweet a label describing the intensity of the emotion or sentiment BIBREF1 .  Since the present study focuses on Spanish tweets, all tweets from the English datasets were translated into Spanish. This new set of “Spanish” data was then added to our original training set. Again, the machine translation platform Apertium BIBREF5 was used for the translation of the datasets.
Output: What dataset did they use?
This effect of context on human ratings is very similar to the one reported in BIBREF5 . They find that sentences rated as ill formed out of context are improved when they are presented in their document contexts. BIBREF5 suggest that adding context causes speakers to focus on broader semantic and pragmatic issues of discourse coherence, rather than simply judging syntactic well formedness (measured as naturalness) when a sentence is considered in isolation.
Output: What provisional explanation do the authors give for the impact of document context?
We evaluate our proposed approach for joint sentiment and emotion analysis on the benchmark dataset of SemEval 2016 Task 6 BIBREF7 and Stance Sentiment Emotion Corpus (SSEC) BIBREF15.
Output: What are the datasets used for training?
We use a standard set of supervised as well as unsupervised benchmark tasks from the literature to evaluate our trained models, following BIBREF16 Sentence embeddings are evaluated for various supervised classification tasks as follows. The predefined training split is used to tune the L2 penalty parameter using cross-validation and the accuracy and F1 scores are computed on the test set.  We perform unsupervised evaluation of the learnt sentence embeddings using the sentence cosine similarity, on the STS 2014 BIBREF31 and SICK 2014 BIBREF32 datasets. These similarity scores are compared to the gold-standard human judgements using Pearson's INLINEFORM0 BIBREF33 and Spearman's INLINEFORM1 BIBREF34 correlation scores. 
Output: What metric is used to measure performance?
Baselines. We compare our models against a neural baseline models, hierarchical LSTM (hLSTM), with the attention ablated but with access to the complete context, and a strong, open-sourced feature-rich baseline BIBREF7 . We choose BIBREF7 over other prior works such as BIBREF0 since we do not have access to the dataset or the system used in their papers for replication. BIBREF7 is a logistic regression classifier with features inclusive of bag-of-words representation of the unigrams and thread length, normalised counts of agreements to previous posts, counts of non-lexical reference items such as URLs, and the Coursera forum type in which a thread appeared. We also report aggregated results from a hLSTM model with access only to the last post as context for comparison. Table TABREF17 compares the performance of these baselines against our proposed methods.
Output: What was the previous state of the art for this task?
ch As continuous word embedding spaces exhibit similar structures across (even distant) languages BIBREF35, we use a multilingual word representation which aims to learn a linear mapping from a source to a target embedding space.
Output: What multilingual word representations are used?
Word embedding models, and BERT in particular, contain vast amounts of information collected through the course of their training. BERT Base for instance, has 110 Million parameters and was trained on both Wikipedea Corpus and BooksCorpus BIBREF0, a combined collection of over 3 Billion words.
Output: What is the language model pre-trained on?
The Zurich Cognitive Language Processing Corpus (ZuCo) 2.0, including raw and preprocessed eye-tracking and electroencephalography (EEG) data of 18 subjects, as well as the recording and preprocessing scripts, is publicly available. It contains physiological data of each subject reading 739 English sentences from Wikipedia (see example in Figure FIGREF1). During the recording session, the participants read 739 sentences that were selected from the Wikipedia corpus provided by culotta2006integrating. 
Output: What kind of sentences were read?
Furthermore, we do not restrict ourselves to a test set of sequences of fixed lengths during testing. Rather, we exhaustively enumerate all the sequences in a language by their lengths and then go through the sequences in the test set one by one until our network errs $k$ times, thereby providing a more fine-grained evaluation criterion of its generalization capabilities. To study the effect of various length distributions on the learning capability and speed of LSTM models, we experimented with four discrete probability distributions supported on bounded intervals (Figure 2 ) to sample the lengths of sequences for the languages. 
Output: Are the unobserved samples from the same distribution as the training data?
We collected all available comments in the stories from Reddit from August 2015. 
Output: what is the source of the new dataset?
To tackle this issue, we present a new evaluation dataset that covers a wide range of monotonicity reasoning that was created by crowdsourcing and collected from linguistics publications (Section "Dataset" ).
Output: Do they release MED?
For the other techniques, we extract paragraphs containing any word from a predetermined list of LGTBQ terms (shown in Table TABREF19)
Output: How do they identify discussions of LGBTQ people in the New York Times?
For the McGurk effect, we attempt an illusion for a language token (e.g. phoneme, word, sentence) $x$ by creating a video where an audio stream of $x$ is visually dubbed over by a person saying $x^{\prime }\ne x$ . The illusion $f(x^{\prime },x)$ affects a listener if they perceive what is being said to be $y\ne x$ if they watched the illusory video whereas they perceive $x$ if they had either listened to the audio stream without watching the video or had watched the original unaltered video, depending on specification.  A prototypical example is that the audio of the phoneme “baa,” accompanied by a video of someone mouthing “vaa”, can be perceived as “vaa” or “gaa” (Figure 1 ).
Output: What is the McGurk effect?
We compare our approaches with related approaches of pivoting, multilingual NMT (MNMT) BIBREF19, and cross-lingual transfer without pretraining BIBREF16. Although it is challenging for one model to translate all zero-shot directions between multiple distant language pairs of MultiUN, MLM+BRLM-SA still achieves better performances on Es $\rightarrow $ Ar and Es $\rightarrow $ Ru than strong pivoting$_{\rm m}$, which uses MNMT to translate source to pivot then to target in two separate steps with each step receiving supervised signal of parallel corpora. 
Output: what are the pivot-based baselines?
Traditional text-to-speech (TTS) systems are composed of complex pipelines BIBREF0 , these often include accoustic frontends, duration model, acoustic prediction model and vocoder models. The architecture of our model utilizes RNN-based Seq2Seq model for generating mel spectrogram from text. The architecture is similar to that of Tacotron 2 BIBREF4  Direct comparison of model parameters between ours and the open-source tacotron 2, our model contains 4.5 million parameters, whereas the Tacotron 2 contains around 13 million parameters with default setting. By helping our model learn attention alignment faster, we can afford to use a smaller overall model to achieve similar quality speech quality.
Output: Do they reduce the number of parameters in their architecture compared to other direct text-to-speech models?
We evaluated SDNet on CoQA dataset
Output: Is the model evaluated on other datasets?
This paper used the real-time method to randomly collect 10% of publicly available English tweets using several pre-defined DDEO-related queries (Table TABREF6 ) within a specific time frame. 
Output: Do they evaluate only on English data?
To tackle this issue we applying a back-translation method BIBREF13, where we translate indirect and physical harassment tweets of the train set from english to german, french and greek. After that, we translate them back to english in order to achieve data augmentation.
Output: What language(s) is/are represented in the dataset?
The resource is composed of data from two different surveys. In both surveys subjects were asked to draw on a map (displayed under a Mercator projection) a polygon representing a given geographical descriptor, in the context of the geography of Galicia in Northwestern Spain (see Fig. FIGREF1 ). The first survey was run in order to obtain a high number of responses to be used as an evaluation testbed for modeling algorithms. It was answered by 15/16 year old students in a high school in Pontevedra (located in Western Galicia). 99 students provided answers for a list of 7 descriptors (including cardinal points, coast, inland, and a proper name). The second survey was addressed to meteorologists in the Galician Weather Agency BIBREF12 .
Output: Which two datasets does the resource come from?
To study how the multimodal context can boost the performance compared to an unimodal context we evaluate different models: a Feature Concatenation Model (FCM), a Spatial Concatenation Model (SCM) and a Textual Kernels Model (TKM)
Output: What models do they propose?
The Digital Library in the TextGrid Repository represents an extensive collection of German texts in digital form BIBREF3.
Output: What is the corpus used in the study?
We perform our experiments using ActivityNet Captions dataset BIBREF2 that is considered as the standard benchmark for dense video captioning task. The dataset contains approximately 20k videos from YouTube and split into 50/25/25 % parts for training, validation, and testing, respectively. 
Output: What domain does the dataset fall into?
Three metrics in text simplification are chosen in this paper. BLEU BIBREF5 is one traditional machine translation metric to assess the degree to which translated simplifications differed from reference simplifications. FKGL measures the readability of the output BIBREF23 . A small FKGL represents simpler output. SARI is a recent text-simplification metric by comparing the output against the source and reference simplifications BIBREF20 .
Output: what evaluation metrics did they use?
Most of the results seem to match with the views of some experts such as the political pundits of the Washington Post.
Output: Who are the experts?
A selection of results from this study are that:

Non-English code is a large-scale phenomena.

Transliteration is common in identifiers for all languages.

Languages clusters into three distinct groups based on how speakers use identifiers/comments/transliteration.

Non-latin script users write comments in their L1 script but write identifiers in English.

Right-to-left (RTL) language scripts, such as Arabic, have no observed prevalence on GitHub identifiers, implying that existing coders who speak RTL languages have substantial barriers in using their native script in code.
Output: What are results of public code repository study?
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). 
Output: How did they get relations between mentions?
Similar equations were discovered using Euclidean distance computed between the context vector representations of the equations ( INLINEFORM2 ). We give additional example results in Appendix B.
Output: How do they define similar equations?
We first introduce a method that detects bottlenecks in text-games using the overall reward gained and the knowledge graph state. This method freezes the policy used to reach the bottleneck and restarts the training from there on out, additionally conducting a backtracking search to ensure that a sub-optimal policy has not been frozen. The second contribution explore how to leverage knowledge graphs to improve existing exploration algorithms for dealing with combinatorial action-spaces such as Go-Explore BIBREF9. 
Output: What are the two new strategies?
Comparing these topics with those that appeared on a Catholic women forum, it seems that both ISIS and non-violent groups use topics about motherhood, spousal relationship, and marriage/divorce when they address women. Moreover, we used Depechemood methods to analyze the emotions that these materials are likely to elicit in readers. The result of our emotion analysis suggests that both corpuses used words that aim to inspire readers while avoiding fear. However, the actual words that lead to these effects are very different in the two contexts. Overall, our findings indicate that, using proper methods, automated analysis of large bodies of textual data can provide novel insight insight into extremist propaganda that can assist the counterterrorism community.
Output: What conclusions do the authors draw from their finding that the emotional appeal of ISIS and Catholic materials are similar?
We further combine the supervised IR with our unsupervised model, which is trained with full unpaired data (4.8M) and different number of paired data (from 50K to 4.8M).
Output: How many comments were used?
To answer RQ3, which is concerned with domain adaptation, we can see in the grayed out cells in tab:results, which correspond to the cross-domain adaption case where the BERT language model is trained on the target domain, that domain adaptation works well with $2.2\%$ absolute accuracy improvement on the laptops test set and even $3.6\%$ accuracy improvement on the restaurants test set compared to BERT-base.
Output: By how much does their model outperform the baseline in the cross-domain evaluation?
We use the full-length ROUGE F1 and METEOR as our main evaluation metrics.
Output: What evaluation metrics do they use?
We present in this section the baseline model from See et al. See2017 trained on the CNN/Daily Mail dataset. The baseline model is a deep sequence-to-sequence encoder/decoder model with attention. The encoder is a bidirectional Long-Short Term Memory(LSTM) cell BIBREF14 and the decoder a single LSTM cell with attention mechanism. The attention mechanism is computed as in BIBREF9 and we use a greedy search for decoding. We train end-to-end including the words embeddings. The embedding size used is of 128 and the hidden state size of the LSTM cells is of 254.
Output: Which baselines did they compare?
The clinical notes we used for the experiment are provided by domain experts, consisting of 1,160 physician logs of Medical ICU admission requests at a tertiary care center affiliated to Mount Sanai. The enriched corpus contains 42,506 Wikipedia articles, each of which corresponds to one candidate, 6 research papers and 2 critical care medicine textbooks, besides our raw ICU data.
Output: Which dataset do they use to build their model?
In order to evaluate the models trained on generated data, we manually annotated a named entities dataset comprising 53453 tokens and 2566 sentences selected from over 250 news texts from ilur.am.
Output: what is the source of the news sentences?
Meanwhile, attending colleges in the Northeast, West and South regions increases the possibility of posting about sexual harassment (positive coefficients), over the Midwest region. 
Output: Which geographical regions correlate to the trend?