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Affective events BIBREF0 are events that typically affect people in positive or negative ways. For example, getting money and playing sports are usually positive to the experiencers; catching cold and losing one's wallet are negative. Understanding affective events is important to various natural language processing (N...	[ "What is the seed lexicon?", "What are the results?", "How are relations used to propagate polarity?", "How big is the Japanese data?", "What are labels available in dataset for supervision?", "How big are improvements of supervszed learning results trained on smalled labeled data enhanced with proposed a...	[ [ "a vocabulary of positive and negative predicates that helps determine the polarity score of an event", "" ], [ "Using all data to train: AL -- BiGRU achieved 0.843 accuracy, AL -- BERT achieved 0.863 accuracy, AL+CA+CO -- BiGRU achieved 0.866 accuracy, AL+CA+CO -- BERT achieved 0.835, accuracy, A...
1.1em1.1.1em1.1.1.1emThomas Haider$^{1,3}$, Steffen Eger$^2$, Evgeny Kim$^3$, Roman Klinger$^3$, Winfried Menninghaus$^1$$^{1}$Department of Language and Literature, Max Planck Institute for Empirical Aesthetics$^{2}$NLLG, Department of Computer Science, Technische Universitat Darmstadt$^{3}$Institut für Maschinelle Sp...	[ "Does the paper report macro F1?", "How is the annotation experiment evaluated?", "What are the aesthetic emotions formalized?" ]	[ [ "", "" ], [ "" ], [ "" ] ]
“If each city is like a game of chess, the day when I have learned the rules, I shall finally possess my empire, even if I shall never succeed in knowing all the cities it contains.”— Italo Calvino, Invisible CitiesA community's identity—defined through the common interests and shared experiences of its users—shapes va...	[ "Do they report results only on English data?", "How do the various social phenomena examined manifest in different types of communities?", "What patterns do they observe about how user engagement varies with the characteristics of a community?", "How did the select the 300 Reddit communities for comparison?"...	[ [ "", "" ], [ "Dynamic communities have substantially higher rates of monthly user retention than more stable communities. More distinctive communities exhibit moderately higher monthly retention rates than more generic communities. There is also a strong positive relationship between a community's ...
Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is, how far from the tumor is cut at during the surgery, or what the spe...	[ "What data is the language model pretrained on?", "What baselines is the proposed model compared against?", "How is the clinical text structuring task defined?", "What are the specific tasks being unified?", "Is all text in this dataset a question, or are there unrelated sentences in between questions?", ...	[ [ "", "" ], [ "", "" ], [ "", "CTS is extracting structural data from medical research data (unstructured). Authors define QA-CTS task that aims to discover most related text from original text." ], [ "", "" ], [ "the dataset consists of pathology reports includin...
Deep learning has unquestionably advanced the state of the art in many natural language processing tasks, from syntactic dependency parsing BIBREF0 to named-entity recognition BIBREF1 to machine translation BIBREF2 . The same certainly applies to language modeling, where recent advances in neural language models (NLMs)...	[ "What aspects have been compared between various language models?", "what classic language models are mentioned in the paper?", "What is a commonly used evaluation metric for language models?" ]	[ [ "Quality measures using perplexity and recall, and performance measured using latency and energy usage. " ], [ "" ], [ "", "" ] ]
Automatically generated fake reviews have only recently become natural enough to fool human readers. Yao et al. BIBREF0 use a deep neural network (a so-called 2-layer LSTM BIBREF1 ) to generate fake reviews, and concluded that these fake reviews look sufficiently genuine to fool native English speakers. They train thei...	[ "Which dataset do they use a starting point in generating fake reviews?", "Do they use a pretrained NMT model to help generating reviews?", "How does using NMT ensure generated reviews stay on topic?", "What kind of model do they use for detection?", "Does their detection tool work better than human detecti...	[ [ "", "" ], [ "", "" ], [ "" ], [ "" ], [ "" ], [ "" ] ]
Ever since the LIME algorithm BIBREF0 , "explanation" techniques focusing on finding the importance of input features in regard of a specific prediction have soared and we now have many ways of finding saliency maps (also called heat-maps because of the way we like to visualize them). We are interested in this paper by...	[ "Which baselines did they compare?", "How many attention layers are there in their model?", "Is the explanation from saliency map correct?" ]	[ [ "", "" ], [ "one" ], [ "" ] ]
Word embeddings, or vector representations of words, are an important component of Natural Language Processing (NLP) models and necessary for many downstream tasks. However, word embeddings, including embeddings commonly deployed for public use, have been shown to exhibit unwanted societal stereotypes and biases, raisi...	[ "How is embedding quality assessed?", "What are the three measures of bias which are reduced in experiments?", "What are the probabilistic observations which contribute to the more robust algorithm?" ]	[ [ "", "" ], [ "RIPA, Neighborhood Metric, WEAT" ], [ "" ] ]
In recent years, word embeddings BIBREF0, BIBREF1, BIBREF2 have been proven to be very useful for training downstream natural language processing (NLP) tasks. Moreover, contextualized embeddings BIBREF3, BIBREF4 have been shown to further improve the performance of NLP tasks such as named entity recognition, question a...	[ "What turn out to be more important high volume or high quality data?", "How much is model improved by massive data and how much by quality?", "What two architectures are used?" ]	[ [ "", "" ], [ "" ], [ "" ] ]
Recently, the transformative potential of machine learning (ML) has propelled ML into the forefront of mainstream media. In Brazil, the use of such technique has been widely diffused gaining more space. Thus, it is used to search for patterns, regularities or even concepts expressed in data sets BIBREF0 , and can be ap...	[ "Does this paper target European or Brazilian Portuguese?", "What were the word embeddings trained on?", "Which word embeddings are analysed?" ]	[ [ "", "" ], [ "" ], [ "" ] ]
Analysis of the way court decisions refer to each other provides us with important insights into the decision-making process at courts. This is true both for the common law courts and for their counterparts in the countries belonging to the continental legal system. Citation data can be used for both qualitative and qu...	[ "Did they experiment on this dataset?", "How is quality of the citation measured?", "How big is the dataset?" ]	[ [ "", "" ], [ "" ], [ "903019 references" ] ]
Combat veterans diagnosed with PTSD are substantially more likely to engage in a number of high risk activities including engaging in interpersonal violence, attempting suicide, committing suicide, binge drinking, and drug abuse BIBREF0. Despite improved diagnostic screening, outpatient mental health and inpatient trea...	[ "Do they evaluate only on English datasets?", "Do the authors mention any possible confounds in this study?", "How is the intensity of the PTSD established?", "How is LIWC incorporated into this system?", "How many twitter users are surveyed using the clinically validated survey?", "Which clinically valid...	[ [ "" ], [ "" ], [ "Given we have four intensity, No PTSD, Low Risk PTSD, Moderate Risk PTSD and High Risk PTSD with a score of 0, 1, 2 and 3 respectively, the estimated intensity is established as mean squared error.", "defined into four categories from high risk, moderate risk, to low ris...
Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The disease was first identified in 2019 in Wuhan, Central China, and has since spread globally, resulting in the 2019–2020 coronavirus pandemic. On March 16th, 2020, researchers and lead...	[ "Did they experiment with the dataset?", "What is the size of this dataset?", "Do they list all the named entity types present?" ]	[ [ "" ], [ "", "" ], [ "" ] ]
Sentiment classification is an important task which requires either word level or document level sentiment annotations. Such resources are available for at most 136 languages BIBREF0 , preventing accurate sentiment classification in a low resource setup. Recent research efforts on cross-lingual transfer learning enable...	[ "how is quality measured?", "how many languages exactly is the sentiment lexica for?", "what sentiment sources do they compare with?" ]	[ [ "Accuracy and the macro-F1 (averaged F1 over positive and negative classes) are used as a measure of quality.", "" ], [ "" ], [ "" ] ]
1.1em1.1.1em1.1.1.1emru=russian$^1$Skolkovo Institute of Science and Technology, Moscow, Russiav.logacheva@skoltech.ru$^2$Ural Federal University, Yekaterinburg, Russia$^3$Universität Hamburg, Hamburg, Germany$^4$Universität Mannheim, Mannheim, Germany$^5$University of Oslo, Oslo, Norway$^6$Higher School of Economics, ...	[ "Is the method described in this work a clustering-based method?", "How are the different senses annotated/labeled? ", "Was any extrinsic evaluation carried out?" ]	[ [ "", "" ], [ "" ], [ "" ] ]
Language Identification (LI) is a problem which involves classifying the language being spoken by a speaker. LI systems can be used in call centers to route international calls to an operator who is fluent in that identified language BIBREF0. In speech-based assistants, LI acts as the first step which chooses the corre...	[ "Does the model use both spectrogram images and raw waveforms as features?", "Is the performance compared against a baseline model?", "What is the accuracy reported by state-of-the-art methods?" ]	[ [ "" ], [ "", "" ], [ "Answer with content missing: (Table 1)\nPrevious state-of-the art on same dataset: ResNet50 89% (6 languages), SVM-HMM 70% (4 languages)" ] ]
The bilingual lexicon induction task aims to automatically build word translation dictionaries across different languages, which is beneficial for various natural language processing tasks such as cross-lingual information retrieval BIBREF0 , multi-lingual sentiment analysis BIBREF1 , machine translation BIBREF2 and so...	[ "Which vision-based approaches does this approach outperform?", "What baseline is used for the experimental setup?", "Which languages are used in the multi-lingual caption model?" ]	[ [ "" ], [ "" ], [ "", "" ] ]
The proliferation of social media has made it possible to study large online communities at scale, thus making important discoveries that can facilitate decision making, guide policies, improve health and well-being, aid disaster response, etc. The wide host of languages, languages varieties, and dialects used on socia...	[ "Did they experiment on all the tasks?", "What models did they compare to?", "What datasets are used in training?" ]	[ [ "" ], [ "" ], [ "", "" ] ]
Generative adversarial nets (GAN) (Goodfellow et al., 2014) belong to a class of generative models which are trainable and can generate artificial data examples similar to the existing ones. In a GAN model, there are two sub-models simultaneously trained: a generative model INLINEFORM0 from which artificial data exampl...	[ "Which GAN do they use?", "Do they evaluate grammaticality of generated text?", "Which corpora do they use?" ]	[ [ "", "" ], [ "" ], [ "" ] ]
Understanding a user's intent and sentiment is of utmost importance for current intelligent chatbots to respond appropriately to human requests. However, current systems are not able to perform to their best capacity when presented with incomplete data, meaning sentences with missing or incorrect words. This scenario i...	[ "Do they report results only on English datasets?", "How do the authors define or exemplify 'incorrect words'?", "How many vanilla transformers do they use after applying an embedding layer?", "Do they test their approach on a dataset without incomplete data?", "Should their approach be applied only when de...	[ [ "" ], [ "typos in spellings or ungrammatical words" ], [ "" ], [ "", "" ], [ "", "" ], [ "In the sentiment classification task by 6% to 8% and in the intent classification task by 0.94% on average" ] ]
Amazon Alexa Prize BIBREF0 provides a platform to collect real human-machine conversation data and evaluate performance on speech-based social conversational systems. Our system, Gunrock BIBREF1 addresses several limitations of prior chatbots BIBREF2, BIBREF3, BIBREF4 including inconsistency and difficulty in complex s...	[ "What is the sample size of people used to measure user satisfaction?", "What are all the metrics to measure user engagement?", "What the system designs introduced?", "Do they specify the model they use for Gunrock?", "Do they gather explicit user satisfaction data on Gunrock?", "How do they correlate use...	[ [ "", "" ], [ "", "" ], [ "" ], [ "" ], [ "" ], [ "" ] ]
In natural language, subjectivity refers to the aspects of communication used to express opinions, evaluations, and speculationsBIBREF0, often influenced by one's emotional state and viewpoints. Writers and editors of texts like news and textbooks try to avoid the use of biased language, yet subjective bias is pervasiv...	[ "Do the authors report only on English?", "What is the baseline for the experiments?", "Which experiments are perfomed?" ]	[ [ "" ], [ "", "" ], [ "They used BERT-based models to detect subjective language in the WNC corpus" ] ]
Producing sentences which are perceived as natural by a human addressee—a property which we will denote as fluency throughout this paper —is a crucial goal of all natural language generation (NLG) systems: it makes interactions more natural, avoids misunderstandings and, overall, leads to higher user satisfaction and u...	[ "Is ROUGE their only baseline?", "what language models do they use?", "what questions do they ask human judges?" ]	[ [ "", "No, other baseline metrics they use besides ROUGE-L are n-gram overlap, negative cross-entropy, perplexity, and BLEU." ], [ "" ], [ "" ] ]
In machine translation, neural networks have attracted a lot of research attention. Recently, the attention-based encoder-decoder framework BIBREF0 , BIBREF1 has been largely adopted. In this approach, Recurrent Neural Networks (RNNs) map source sequences of words to target sequences. The attention mechanism is learned...	[ "What misbehavior is identified?", "What is the baseline used?", "Which attention mechanisms do they compare?" ]	[ [ "", "" ], [ "" ], [ "" ] ]
Making article comments is a fundamental ability for an intelligent machine to understand the article and interact with humans. It provides more challenges because commenting requires the abilities of comprehending the article, summarizing the main ideas, mining the opinions, and generating the natural language. Theref...	[ "Which paired corpora did they use in the other experiment?", "By how much does their system outperform the lexicon-based models?", "Which lexicon-based models did they compare with?", "How many comments were used?", "How many articles did they have?", "What news comment dataset was used?" ]	[ [ "", "" ], [ "Under the retrieval evaluation setting, their proposed model + IR2 had better MRR than NVDM by 0.3769, better MR by 4.6, and better Recall@10 by 20 . \nUnder the generative evaluation setting the proposed model + IR2 had better BLEU by 0.044 , better CIDEr by 0.033, better ROUGE by 0...
With ever-increasing amounts of data available, there is an increase in the need to offer tooling to speed up processing, and eventually making sense of this data. Because fully-automated tools to extract meaning from any given input to any desired level of detail have yet to be developed, this task is still at least s...	[ "By how much do they outperform standard BERT?", "What dataset do they use?", "How do they combine text representations with the knowledge graph embeddings?" ]	[ [ "" ], [ "", "" ], [ "" ] ]
The Digital Library in the TextGrid Repository represents an extensive collection of German texts in digital form BIBREF3. It was mined from http://zeno.org and covers a time period from the mid 16th century up to the first decades of the 20th century. It contains many important texts that can be considered as part of ...	[ "What is the algorithm used for the classification tasks?", "Is the outcome of the LDA analysis evaluated in any way?", "What is the corpus used in the study?" ]	[ [ "" ], [ "" ], [ "", "" ] ]
Knowledge graph(KG) has been proposed for several years and its most prominent application is in web search, for example, Google search triggers a certain entity card when a user's query matches or mentions an entity based on some statistical model. The core potential of a knowledge graph is about its capability of rea...	[ "What are the traditional methods to identifying important attributes?", "What do you use to calculate word/sub-word embeddings", "What user generated text data do you use?" ]	[ [ "", "" ], [ "" ], [ "" ] ]
Characteristic metrics are a set of unsupervised measures that quantitatively describe or summarize the properties of a data collection. These metrics generally do not use ground-truth labels and only measure the intrinsic characteristics of data. The most prominent example is descriptive statistics that summarizes a d...	[ "Did they propose other metrics?", "Which real-world datasets did they use?", "How did they obtain human intuitions?" ]	[ [ "" ], [ "", "" ], [ "" ] ]
Decisions made in international organisations are fundamental to international development efforts and initiatives. It is in these global governance arenas that the rules of the global economic system, which have a huge impact on development outcomes are agreed on; decisions are made about large-scale funding for devel...	[ "What are the country-specific drivers of international development rhetoric?", "Is the dataset multilingual?", "How are the main international development topics that states raise identified?" ]	[ [ "" ], [ "", "" ], [ " They focus on exclusivity and semantic coherence measures: Highly frequent words in a given topic that do not appear very often in other topics are viewed as making that topic exclusive. They select select the 16-topic model, which has the largest positive residual in...
QnAMaker aims to simplify the process of bot creation by extracting Question-Answer (QA) pairs from data given by users into a Knowledge Base (KB) and providing a conversational layer over it. KB here refers to one instance of azure search index, where the extracted QA are stored. Whenever a developer creates a KB usin...	[ "What experiments do the authors present to validate their system?", "How does the conversation layer work?", "What components is the QnAMaker composed of?" ]	[ [ "" ], [ "" ], [ "", "" ] ]
Since Och BIBREF0 proposed minimum error rate training (MERT) to exactly optimize objective evaluation measures, MERT has become a standard model tuning technique in statistical machine translation (SMT). Though MERT performs better by improving its searching algorithm BIBREF1, BIBREF2, BIBREF3, BIBREF4, it does not wo...	[ "How they measure robustness in experiments?", "Is new method inferior in terms of robustness to MIRAs in experiments?", "What experiments with large-scale features are performed?" ]	[ [ "", "" ], [ "" ], [ "" ] ]
Currently, voice-controlled smart devices are widely used in multiple areas to fulfill various tasks, e.g. playing music, acquiring weather information and booking tickets. The SLU system employs several modules to enable the understanding of the semantics of the input speeches. When there is an incoming speech, the AS...	[ "Which ASR system(s) is used in this work?", "What are the series of simple models?", "Over which datasets/corpora is this work evaluated?" ]	[ [ "" ], [ "" ], [ "", "" ] ]
We developed a syntactic text simplification (TS) approach that can be used as a preprocessing step to facilitate and improve the performance of a wide range of artificial intelligence (AI) tasks, such as Machine Translation, Information Extraction (IE) or Text Summarization. Since shorter sentences are generally bette...	[ "Is the semantic hierarchy representation used for any task?", "What are the corpora used for the task?", "Is the model evaluated?" ]	[ [ "Yes, Open IE", "" ], [ "" ], [ "the English version is evaluated. The German version evaluation is in progress " ] ]
Word embeddings have great practical importance since they can be used as pre-computed high-density features to ML models, significantly reducing the amount of training data required in a variety of NLP tasks. However, there are several inter-related challenges with computing and consistently distributing word embeddin...	[ "What new metrics are suggested to track progress?", "What intrinsic evaluation metrics are used?", "What experimental results suggest that using less than 50% of the available training examples might result in overfitting?" ]	[ [ "" ], [ "", "" ], [ "" ] ]
A great deal of commonsense knowledge about the world we live is procedural in nature and involves steps that show ways to achieve specific goals. Understanding and reasoning about procedural texts (e.g. cooking recipes, how-to guides, scientific processes) are very hard for machines as it demands modeling the intrinsi...	[ "What multimodality is available in the dataset?", "What are previously reported models?", "How better is accuracy of new model compared to previously reported models?" ]	[ [ "", "" ], [ "" ], [ "Average accuracy of proposed model vs best prevous result:\nSingle-task Training: 57.57 vs 55.06\nMulti-task Training: 50.17 vs 50.59" ] ]
Electronic health records (EHRs) systematically collect patients' clinical information, such as health profiles, histories of present illness, past medical histories, examination results and treatment plans BIBREF0 . By analyzing EHRs, many useful information, closely related to patients, can be discovered BIBREF1 . Si...	[ "How does the scoring model work?", "How does the active learning model work?", "Which neural network architectures are employed?" ]	[ [ "", "" ], [ "Active learning methods has a learning engine (mainly used for training of classification problems) and the selection engine (which chooses samples that need to be relabeled by annotators from unlabeled data). Then, relabeled samples are added to training set for classifier to re-trai...
A script is “a standardized sequence of events that describes some stereotypical human activity such as going to a restaurant or visiting a doctor” BIBREF0 . Script events describe an action/activity along with the involved participants. For example, in the script describing a visit to a restaurant, typical events are ...	[ "What are the key points in the role of script knowledge that can be studied?", "Did the annotators agreed and how much?", "How many subjects have been used to create the annotations?" ]	[ [ "" ], [ "For event types and participant types, there was a moderate to substantial level of agreement using the Fleiss' Kappa. For coreference chain annotation, there was average agreement of 90.5%.", "Moderate agreement of 0.64-0.68 Fleiss’ Kappa over event type labels, 0.77 Fleiss’ Kappa over p...
Knowledge graphs (KG) play a critical role in many real-world applications such as search, structured data management, recommendations, and question answering. Since KGs often suffer from incompleteness and noise in their facts (links), a number of recent techniques have proposed models that embed each entity and relat...	[ "What datasets are used to evaluate this approach?", "How is this approach used to detect incorrect facts?", "Can this adversarial approach be used to directly improve model accuracy?" ]	[ [ " Kinship and Nations knowledge graphs, YAGO3-10 and WN18KGs knowledge graphs ", "" ], [ "" ], [ "" ] ]
Topic models, such as latent Dirichlet allocation (LDA), allow us to analyze large collections of documents by revealing their underlying themes, or topics, and how each document exhibits them BIBREF0 . Therefore, it is not surprising that topic models have become a standard tool in data analysis, with many application...	[ "what are the advantages of the proposed model?", "what are the state of the art approaches?", "what datasets were used?" ]	[ [ "" ], [ "" ], [ "", "" ] ]
Recently, there have been a variety of task-oriented dialogue models thanks to the prosperity of neural architectures BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5. However, the research is still largely limited by the availability of large-scale high-quality dialogue data. Many corpora have advanced the researc...	[ "How was the dataset collected?", "What are the benchmark models?", "How was the corpus annotated?" ]	[ [ "", "They crawled travel information from the Web to build a database, created a multi-domain goal generator from the database, collected dialogue between workers an automatically annotated dialogue acts. " ], [ "" ], [ "" ] ]
As traditional word embedding algorithms BIBREF1 are known to struggle with rare words, several techniques for improving their representations have been proposed over the last few years. These approaches exploit either the contexts in which rare words occur BIBREF2, BIBREF3, BIBREF4, BIBREF5, their surface-form BIBREF6...	[ "What models other than standalone BERT is new model compared to?", "How much is representaton improved for rare/medum frequency words compared to standalone BERT and previous work?", "What are three downstream task datasets?", "What is dataset for word probing task?" ]	[ [ "Only Bert base and Bert large are compared to proposed approach." ], [ "" ], [ "", "" ], [ "" ] ]
Entity Linking (EL), which is also called Entity Disambiguation (ED), is the task of mapping mentions in text to corresponding entities in a given knowledge Base (KB). This task is an important and challenging stage in text understanding because mentions are usually ambiguous, i.e., different named entities may share t...	[ "How fast is the model compared to baselines?", "How big is the performance difference between this method and the baseline?", "What datasets used for evaluation?", "what are the mentioned cues?" ]	[ [ "" ], [ "Comparing with the highest performing baseline: 1.3 points on ACE2004 dataset, 0.6 points on CWEB dataset, and 0.86 points in the average of all scores." ], [ "", "" ], [ "" ] ]
The BioASQ Challenge includes a question answering task (Phase B, part B) where the aim is to find the “ideal answer” — that is, an answer that would normally be given by a person BIBREF0. This is in contrast with most other question answering challenges where the aim is normally to give an exact answer, usually a fact...	[ "How did the author's work rank among other submissions on the challenge?", "What approaches without reinforcement learning have been tried?", "What classification approaches were experimented for this task?", "Did classification models perform better than previous regression one?" ]	[ [ "" ], [ "classification, regression, neural methods", "" ], [ "" ], [ "" ] ]
The two largest standardized, cross-lingual datasets for morphological annotation are provided by the Universal Dependencies BIBREF1 and Universal Morphology BIBREF2 , BIBREF3 projects. Each project's data are annotated according to its own cross-lingual schema, prescribing how features like gender or case should be ma...	[ "What are the main sources of recall errors in the mapping?", "Do they look for inconsistencies between different languages' annotations in UniMorph?", "Do they look for inconsistencies between different UD treebanks?", "Which languages do they validate on?" ]	[ [ "" ], [ "" ], [ "" ], [ "Ar, Bg, Ca, Cs, Da, De, En, Es, Eu, Fa, Fi, Fr, Ga, He, Hi, Hu, It, La, Lt, Lv, Nb, Nl, Nn, PL, Pt, Ro, Ru, Sl, Sv, Tr, Uk, Ur", "" ] ]
Automatic emotion recognition is commonly understood as the task of assigning an emotion to a predefined instance, for example an utterance (as audio signal), an image (for instance with a depicted face), or a textual unit (e.g., a transcribed utterance, a sentence, or a Tweet). The set of emotions is often following t...	[ "Does the paper evaluate any adjustment to improve the predicion accuracy of face and audio features?", "How is face and audio data analysis evaluated?", "What is the baseline method for the task?", "What are the emotion detection tools used for audio and face input?" ]	[ [ "" ], [ "" ], [ "For the emotion recognition from text they use described neural network as baseline.\nFor audio and face there is no baseline." ], [ "", "" ] ]
While neural machine translation (NMT) has achieved impressive performance in high-resource data conditions, becoming dominant in the field BIBREF0 , BIBREF1 , BIBREF2 , recent research has argued that these models are highly data-inefficient, and underperform phrase-based statistical machine translation (PBSMT) or uns...	[ "what amounts of size were used on german-english?", "what were their experimental results in the low-resource dataset?", "what are the methods they compare with in the korean-english dataset?", "what pitfalls are mentioned in the paper?" ]	[ [ "Training data with 159000, 80000, 40000, 20000, 10000 and 5000 sentences, and 7584 sentences for development", "" ], [ "" ], [ "" ], [ "" ] ]
Over the past two decades, the rise of social media and the digitization of news and discussion platforms have radically transformed how individuals and groups create, process and share news and information. As Alan Rusbridger, former-editor-in-chief of the newspaper The Guardian has it, these technologically-driven sh...	[ "Does the paper report the results of previous models applied to the same tasks?", "How is the quality of the discussion evaluated?", "What is the technique used for text analysis and mining?", "What are the causal mapping methods employed?" ]	[ [ "", "" ], [ "" ], [ "" ], [ "" ] ]
Hinglish is a linguistic blend of Hindi (very widely spoken language in India) and English (an associate language of urban areas) and is spoken by upwards of 350 million people in India. While the name is based on the Hindi language, it does not refer exclusively to Hindi, but is used in India, with English words blend...	[ "What is the previous work's model?", "What dataset is used?", "How big is the dataset?", "How is the dataset collected?", "Was each text augmentation technique experimented individually?", "What models do previous work use?", "Does the dataset contain content from various social media platforms?", "W...	[ [ "" ], [ "", "" ], [ "", "Resulting dataset was 7934 messages for train and 700 messages for test." ], [ "" ], [ "" ], [ "" ], [ "" ], [ "" ] ]
Multilingual BERT (mBERT; BIBREF0) is gaining popularity as a contextual representation for various multilingual tasks, such as dependency parsing BIBREF1, BIBREF2, cross-lingual natural language inference (XNLI) or named-entity recognition (NER) BIBREF3, BIBREF4, BIBREF5.BIBREF3 present an exploratory paper showing th...	[ "How they demonstrate that language-neutral component is sufficiently general in terms of modeling semantics to allow high-accuracy word-alignment?", "Are language-specific and language-neutral components disjunctive?", "How they show that mBERT representations can be split into a language-specific component an...	[ [ "", "" ], [ "" ], [ "" ], [ "" ] ]
Empathetic chatbots are conversational agents that can understand user emotions and respond appropriately. Incorporating empathy into the dialogue system is essential to achieve better human-robot interaction because naturally, humans express and perceive emotion in natural language to increase their sense of social bo...	[ "What is the performance of their system?", "What evaluation metrics are used?", "What is the source of the dialogues?", "What pretrained LM is used?" ]	[ [ "" ], [ "" ], [ "" ], [ "", "" ] ]
Fueled by recent advances in deep-learning and language processing, NLP systems are increasingly being used for prediction and decision-making in many fields BIBREF0, including sensitive ones such as health, commerce and law BIBREF1. Unfortunately, these highly flexible and highly effective neural models are also opaqu...	[ "What approaches they propose?", "What faithfulness criteria does they propose?", "Which are three assumptions in current approaches for defining faithfulness?", "Which are key points in guidelines for faithfulness evaluation?" ]	[ [ "" ], [ "" ], [ "", "" ], [ "" ] ]
Deep learning has achieved tremendous success for many NLP tasks. However, unlike traditional methods that provide optimized weights for human understandable features, the behavior of deep learning models is much harder to interpret. Due to the high dimensionality of word embeddings, and the complex, typically recurren...	[ "Did they use the state-of-the-art model to analyze the attention?", "What is the performance of their model?", "How many layers are there in their model?", "Did they compare with gradient-based methods?" ]	[ [ "" ], [ "", "" ], [ "" ], [ "" ] ]
Enabling computers to understand given documents and answer questions about their content has recently attracted intensive interest, including but not limited to the efforts as in BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 . Many specific problems such as machine comprehension and question answering ofte...	[ "What MC abbreviate for?", "how much of improvement the adaptation model can get?", "what is the architecture of the baseline model?", "What is the exact performance on SQUAD?" ]	[ [ "machine comprehension" ], [ "" ], [ "", "" ], [ "" ] ]
Quality Estimation (QE) is a term used in machine translation (MT) to refer to methods that measure the quality of automatically translated text without relying on human references BIBREF0, BIBREF1. In this study, we address QE for summarization. Our proposed model, Sum-QE, successfully predicts linguistic qualities of...	[ "What are their correlation results?", "What dataset do they use?", "What simpler models do they look at?", "What linguistic quality aspects are addressed?" ]	[ [ "High correlation results range from 0.472 to 0.936" ], [ "" ], [ "", "BiGRUs with attention, ROUGE, Language model, and next sentence prediction " ], [ "Grammaticality, non-redundancy, referential clarity, focus, structure & coherence" ] ]
Knowledge graphs are usually collections of factual triples—(head entity, relation, tail entity), which represent human knowledge in a structured way. In the past few years, we have witnessed the great achievement of knowledge graphs in many areas, such as natural language processing BIBREF0, question answering BIBREF1...	[ "What benchmark datasets are used for the link prediction task?", "What are state-of-the art models for this task?", "How better does HAKE model peform than state-of-the-art methods?", "How are entities mapped onto polar coordinate system?" ]	[ [ "", "" ], [ "" ], [ "" ], [ "" ] ]
Removing computer-human language barrier is an inevitable advancement researchers are thriving to achieve for decades. One of the stages of this advancement will be coding through natural human language instead of traditional programming language. On naturalness of computer programming D. Knuth said, “Let us change our...	[ "What additional techniques are incorporated?", "What dataset do they use?", "Do they compare to other models?", "What is the architecture of the system?", "How long are expressions in layman's language?", "What additional techniques could be incorporated to further improve accuracy?", "What programming...	[ [ "", "" ], [ "A parallel corpus where the source is an English expression of code and the target is Python code.", "" ], [ "" ], [ "" ], [ "" ], [ "" ], [ "" ], [ "" ] ]
“ (GANs), and the variations that are now being proposed is the most interesting idea in the last 10 years in ML, in my opinion.” (2016)– Yann LeCunA picture is worth a thousand words! While written text provide efficient, effective, and concise ways for communication, visual content, such as images, is a more comprehe...	[ "Is text-to-image synthesis trained is suppervized or unsuppervized manner?", "What challenges remain unresolved?", "What is the conclusion of comparison of proposed solution?", "What is typical GAN architecture for each text-to-image synhesis group?" ]	[ [ "", "" ], [ "" ], [ "" ], [ "Semantic Enhancement GANs: DC-GANs, MC-GAN\nResolution Enhancement GANs: StackGANs, AttnGAN, HDGAN\nDiversity Enhancement GANs: AC-GAN, TAC-GAN etc.\nMotion Enhancement GAGs: T2S, T2V, StoryGAN" ] ]
Incorporating sub-word structures like substrings, morphemes and characters to the creation of word representations significantly increases their quality as reflected both by intrinsic metrics and performance in a wide range of downstream tasks BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 .The reason for this improvement is r...	[ "Where do they employ feature-wise sigmoid gating?", "Which model architecture do they use to obtain representations?", "Which downstream sentence-level tasks do they evaluate on?", "Which similarity datasets do they use?" ]	[ [ "" ], [ "" ], [ "" ], [ "", "" ] ]
Distantly-supervised information extraction systems extract relation tuples with a set of pre-defined relations from text. Traditionally, researchers BIBREF0, BIBREF1, BIBREF2 use pipeline approaches where a named entity recognition (NER) system is used to identify the entities in a sentence and then a classifier is us...	[ "Are there datasets with relation tuples annotated, how big are datasets available?", "Which one of two proposed approaches performed better in experiments?", "What is previous work authors reffer to?", "How higher are F1 scores compared to previous work?" ]	[ [ "" ], [ "" ], [ "" ], [ "", "" ] ]
[block]I.1em[block]i.1em Learning to Rank Scientific Documents from the CrowdLearning to Rank Scientific Documents from the Crowd -4[1]1The number of biomedical research papers published has increased dramatically in recent years. As of October, 2016, PubMed houses over 26 million citations, with almost 1 million from ...	[ "what were the baselines?", "what is the supervised model they developed?", "what is the size of this built corpus?", "what crowdsourcing platform is used?" ]	[ [ "", "" ], [ "" ], [ "" ], [ "" ] ]
In recent years, social media, forums, blogs and other forms of online communication tools have radically affected everyday life, especially how people express their opinions and comments. The extraction of useful information (such as people's opinion about companies brand) from the huge amount of unstructured data is ...	[ "Which deep learning model performed better?", "By how much did the results improve?", "What was their performance on the dataset?", "How large is the dataset?" ]	[ [ "", "" ], [ "" ], [ "" ], [ "" ] ]
0pt0.03.03 0pt0.030.03 0pt0.030.03We introduce “Talk The Walk”, the first large-scale dialogue dataset grounded in action and perception. The task involves two agents (a “guide” and a “tourist”) that communicate via natural language in order to achieve a common goal: having the tourist navigate to a given target loca...	[ "Did the authors use crowdsourcing platforms?", "How was the dataset collected?", "What language do the agents talk in?", "What evaluation metrics did the authors look at?", "What data did they use?" ]	[ [ "", "" ], [ "" ], [ "English" ], [ "" ], [ "" ] ]
In recent years, the spread of misinformation has become a growing concern for researchers and the public at large BIBREF1 . Researchers at MIT found that social media users are more likely to share false information than true information BIBREF2 . Due to renewed focus on finding ways to foster healthy political conver...	[ "Do the authors report results only on English data?", "How is the accuracy of the system measured?", "How is an incoming claim used to retrieve similar factchecked claims?", "What existing corpus is used for comparison in these experiments?", "What are the components in the factchecking algorithm? " ]	[ [ "", "" ], [ "" ], [ "" ], [ "" ], [ "" ] ]
Reading comprehension (RC) has become a key benchmark for natural language understanding (NLU) systems and a large number of datasets are now available BIBREF0, BIBREF1, BIBREF2. However, these datasets suffer from annotation artifacts and other biases, which allow systems to “cheat”: Instead of learning to read texts,...	[ "What is the baseline?", "What dataset was used in the experiment?", "Did they use any crowdsourcing platform?", "How was the dataset annotated?", "What is the source of the proposed dataset?" ]	[ [ "" ], [ "" ], [ "", "" ], [ "" ], [ "" ] ]
Over the past few years, microblogs have become one of the most popular online social networks. Microblogging websites have evolved to become a source of varied kinds of information. This is due to the nature of microblogs: people post real-time messages about their opinions and express sentiment on a variety of topics...	[ "How many label options are there in the multi-label task?", "What is the interannotator agreement of the crowd sourced users?", "Who are the experts?", "Who is the crowd in these experiments?", "How do you establish the ground truth of who won a debate?" ]	[ [ "" ], [ "" ], [ "", "" ], [ "" ], [ "" ] ]
In the past decade, many large-scale Knowledge Graphs (KGs), such as Freebase BIBREF0, DBpedia BIBREF1 and YAGO BIBREF2 have been built to represent human complex knowledge about the real-world in the machine-readable format. The facts in KGs are usually encoded in the form of triples $(\textit {head entity}, relation,...	[ "How much better is performance of proposed method than state-of-the-art methods in experiments?", "What further analysis is done?", "What seven state-of-the-art methods are used for comparison?", "What three datasets are used to measure performance?", "How does KANE capture both high-order structural and a...	[ [ "Accuracy of best proposed method KANE (LSTM+Concatenation) are 0.8011, 0.8592, 0.8605 compared to best state-of-the art method R-GCN + LR 0.7721, 0.8193, 0.8229 on three datasets respectively." ], [ "" ], [ "" ], [ "", "" ], [ "" ], [ "" ] ]
The ubiquity of communication devices has made social media highly accessible. The content on these media reflects a user's day-to-day activities. This includes content created under the influence of alcohol. In popular culture, this has been referred to as `drunk-texting'. In this paper, we introduce automatic `drunk-...	[ "Do they report results only on English data?", "Do the authors mention any confounds to their study?", "What baseline model is used?", "What stylistic features are used to detect drunk texts?", "Is the data acquired under distant supervision verified by humans at any stage?", "What hashtags are used for ...	[ [ "" ], [ "" ], [ "Human evaluators" ], [ "LDA unigrams (Presence/Count), POS Ratio, #Named Entity Mentions, #Discourse Connectors, Spelling errors, Repeated characters, Capitalisation, Length, Emoticon (Presence/Count ) \n and Sentiment Ratio", "LDA unigrams (Presence/Count), POS ...
Effective question answering (QA) systems have been a long-standing quest of AI research. Structured curated KBs have been used successfully for this task BIBREF0 , BIBREF1 . However, these KBs are expensive to build and typically domain-specific. Automatically constructed open vocabulary (subject; predicate; object) s...	[ "What corpus was the source of the OpenIE extractions?", "What is the accuracy of the proposed technique?", "Is an entity linking process used?", "Are the OpenIE extractions all triples?", "What method was used to generate the OpenIE extractions?", "Can the method answer multi-hop questions?", "What was...	[ [ "", "" ], [ "51.7 and 51.6 on 4th and 8th grade question sets with no curated knowledge. 47.5 and 48.0 on 4th and 8th grade question sets when both solvers are given the same knowledge" ], [ "" ], [ "" ], [ "" ], [ "" ], [ "" ], [ "" ], [ "" ...
Word sense disambiguation (WSD) is a natural language processing task of identifying the particular word senses of polysemous words used in a sentence. Recently, a lot of attention was paid to the problem of WSD for the Russian language BIBREF0 , BIBREF1 , BIBREF2 . This problem is especially difficult because of both ...	[ "Do the authors offer any hypothesis about why the dense mode outperformed the sparse one?", "What evaluation is conducted?", "Which corpus of synsets are used?", "What measure of semantic similarity is used?" ]	[ [ "" ], [ "" ], [ "" ], [ "" ] ]
Factoid Question Answering (QA) aims to extract answers, from an underlying knowledge source, to information seeking questions posed in natural language. Depending on the knowledge source available there are two main approaches for factoid QA. Structured sources, including Knowledge Bases (KBs) such as Freebase BIBREF1...	[ "Which retrieval system was used for baselines?" ]	[ [ "The dataset comes with a ranked set of relevant documents. Hence the baselines do not use a retrieval system." ] ]
Named Entity Recognition (NER) is one of information extraction subtasks that is responsible for detecting entity elements from raw text and can determine the category in which the element belongs, these categories include the names of persons, organizations, locations, expressions of times, quantities, monetary values...	[ "What word embeddings were used?", "What type of errors were produced by the BLSTM-CNN-CRF system?", "How much better was the BLSTM-CNN-CRF than the BLSTM-CRF?" ]	[ [ "" ], [ "" ], [ "Best BLSTM-CNN-CRF had F1 score 86.87 vs 86.69 of best BLSTM-CRF " ] ]
Community question answering (cQA) is a paradigm that provides forums for users to ask or answer questions on any topic with barely any restrictions. In the past decade, these websites have attracted a great number of users, and have accumulated a large collection of question-comment threads generated by these users. H...	[ "What supplemental tasks are used for multitask learning?", "Is the improvement actually coming from using an RNN?", "How much performance gap between their approach and the strong handcrafted method?", "What is a strong feature-based method?", "Did they experimnet in other languages?" ]	[ [ "Multitask learning is used for the task of predicting relevance of a comment on a different question to a given question, where the supplemental tasks are predicting relevance between the questions, and between the comment and the corresponding question" ], [ "" ], [ "0.007 MAP on Task A, 0.0...
Targeted sentiment classification is a fine-grained sentiment analysis task, which aims at determining the sentiment polarities (e.g., negative, neutral, or positive) of a sentence over “opinion targets” that explicitly appear in the sentence. For example, given a sentence “I hated their service, but their food was gre...	[ "Do they use multi-attention heads?", "How big is their model?", "How is their model different from BERT?" ]	[ [ "" ], [ "Proposed model has 1.16 million parameters and 11.04 MB." ], [ "" ] ]
Opinion mining BIBREF0 is a huge field that covers many NLP tasks ranging from sentiment analysis BIBREF1 , aspect extraction BIBREF2 , and opinion summarization BIBREF3 , among others. Despite the vast literature on opinion mining, the task on suggestion mining has given little attention. Suggestion mining BIBREF4 is ...	[ "What datasets were used?", "How did they do compared to other teams?" ]	[ [ "" ], [ "" ] ]
Humans experience a variety of complex emotions in daily life. These emotions are heavily reflected in our language, in both spoken and written forms.Many recent advances in natural language processing on emotions have focused on product reviews BIBREF0 and tweets BIBREF1, BIBREF2. These datasets are often limited in l...	[ "Which tested technique was the worst performer?", "How many emotions do they look at?", "What are the baseline benchmarks?", "What is the size of this dataset?", "How many annotators were there?" ]	[ [ "" ], [ "9" ], [ "" ], [ "" ], [ "" ] ]
State-of-the-art speech recognition accuracy has significantly improved over the past few years since the application of deep neural networks BIBREF0 , BIBREF1 . Recently, it has been shown that with the application of both neural network acoustic model and language model, an automatic speech recognizer can approach hu...	[ "Can SCRF be used to pretrain the model?" ]	[ [ "" ] ]
A common way for marking information about gender, number, and case in language is morphology, or the structure of a given word in the language. However, different languages mark such information in different ways – for example, in some languages gender may be marked on the head word of a syntactic dependency relation,...	[ "What conclusions are drawn from the syntactic analysis?", "What type of syntactic analysis is performed?", "How is it demonstrated that the correct gender and number information is injected using this system?", "Which neural machine translation system is used?", "What are the components of the black-box co...	[ [ "" ], [ "" ], [ "" ], [ "" ], [ "" ] ]
Although development of the first speech recognition systems began half a century ago, there has been a significant increase of the accuracy of ASR systems and number of their applications for the recent ten years, even for low-resource languages BIBREF0 , BIBREF1 .This is mainly due to widespread applying of deep lear...	[ "What normalization techniques are mentioned?", "What features do they experiment with?", "Which architecture is their best model?", "What kind of spontaneous speech is used?" ]	[ [ "" ], [ "" ], [ "" ], [ "" ] ]
The task of reading comprehension, where systems must understand a single passage of text well enough to answer arbitrary questions about it, has seen significant progress in the last few years. With models reaching human performance on the popular SQuAD dataset BIBREF0, and with much of the most popular reading compre...	[ "What approach did previous models use for multi-span questions?", "How they use sequence tagging to answer multi-span questions?", "What is difference in peformance between proposed model and state-of-the art on other question types?", "What is the performance of proposed model on entire DROP dataset?", "W...	[ [ "Only MTMSM specifically tried to tackle the multi-span questions. Their approach consisted of two parts: first train a dedicated categorical variable to predict the number of spans to extract and the second was to generalize the single-span head method of extracting a span" ], [ "" ], [ "For ...
Data annotation is a key bottleneck in many data driven algorithms. Specifically, deep learning models, which became a prominent tool in many data driven tasks in recent years, require large datasets to work well. However, many tasks require manual annotations which are relatively hard to obtain at scale. An attractive...	[ "How much more data does the model trained using XR loss have access to, compared to the fully supervised model?", "Does the system trained only using XR loss outperform the fully supervised neural system?", "How accurate is the aspect based sentiment classifier trained only using the XR loss?", "How is the e...	[ [ "" ], [ "" ], [ "BiLSTM-XR-Dev Estimation accuracy is 83.31 for SemEval-15 and 87.68 for SemEval-16.\nBiLSTM-XR accuracy is 83.31 for SemEval-15 and 88.12 for SemEval-16.\n" ], [ "" ] ]
While producing a sentence, humans combine various types of knowledge to produce fluent output—various shades of meaning are expressed through word selection and tone, while the language is made to conform to underlying structural rules via syntax and morphology. Native speakers are often quick to identify disfluency, ...	[ "What were the non-neural baselines used for the task?" ]	[ [ "The Lemming model in BIBREF17" ] ]
Research in Conversational AI (also known as Spoken Dialogue Systems) has applications ranging from home devices to robotics, and has a growing presence in industry. A key problem in real-world Dialogue Systems is Natural Language Understanding (NLU) – the process of extracting structured representations of meaning fro...	[ "Which publicly available NLU dataset is used?", "What metrics other than entity tagging are compared?" ]	[ [ "" ], [ "" ] ]
Many machine reading comprehension (MRC) datasets have been released in recent years BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4 to benchmark a system's ability to understand and reason over natural language. Typically, these datasets require an MRC model to read through a document to answer a question about informatio...	[ "Do they provide decision sequences as supervision while training models?", "What are the models evaluated on?", "How do they train models in this setup?", "What commands does their setup provide to models seeking information?" ]	[ [ "" ], [ "They evaluate F1 score and agent's test performance on their own built interactive datasets (iSQuAD and iNewsQA)" ], [ "" ], [ "" ] ]
Social Media platforms such as Facebook, Twitter or Reddit have empowered individuals' voices and facilitated freedom of expression. However they have also been a breeding ground for hate speech and other types of online harassment. Hate speech is defined in legal literature as speech (or any form of expression) that e...	[ "What models do they propose?", "Are all tweets in English?", "How large is the dataset?", "What is the results of multimodal compared to unimodal models?", "What is author's opinion on why current multimodal models cannot outperform models analyzing only text?", "What metrics are used to benchmark the re...	[ [ "" ], [ "" ], [ "" ], [ "Unimodal LSTM vs Best Multimodal (FCM)\n- F score: 0.703 vs 0.704\n- AUC: 0.732 vs 0.734 \n- Mean Accuracy: 68.3 vs 68.4 " ], [ "" ], [ "" ], [ "" ], [ "" ], [ "" ], [ "" ], [ "" ] ]
Short text clustering is of great importance due to its various applications, such as user profiling BIBREF0 and recommendation BIBREF1 , for nowaday's social media dataset emerged day by day. However, short text clustering has the data sparsity problem and most words only occur once in each short text BIBREF2 . As a r...	[ "What were the evaluation metrics used?", "What were their performance results?", "By how much did they outperform the other methods?", "Which popular clustering methods did they experiment with?", "What datasets did they use?" ]	[ [ "" ], [ "On SearchSnippets dataset ACC 77.01%, NMI 62.94%, on StackOverflow dataset ACC 51.14%, NMI 49.08%, on Biomedical dataset ACC 43.00%, NMI 38.18%" ], [ "on SearchSnippets dataset by 6.72% in ACC, by 6.94% in NMI; on Biomedical dataset by 5.77% in ACC, 3.91% in NMI" ], [ "" ], ...
Students are exposed to simple arithmetic word problems starting in elementary school, and most become proficient in solving them at a young age. Automatic solvers of such problems could potentially help educators, as well as become an integral part of general question answering services. However, it has been challengi...	[ "Does pre-training on general text corpus improve performance?", "What neural configurations are explored?", "Are the Transformers masked?", "How is this problem evaluated?", "What datasets do they use?" ]	[ [ "" ], [ "" ], [ "" ], [ "" ], [ "" ] ]
Voice-controlled virtual assistants (VVA) such as Siri and Alexa have experienced an exponential growth in terms of number of users and provided capabilities. They are used by millions for a variety of tasks including shopping, playing music, and even telling jokes. Arguably, their success is due in part to the emotion...	[ "What evaluation metrics were used?", "Where did the real production data come from?", "What feedback labels are used?" ]	[ [ "" ], [ "" ], [ "" ] ]
Over the past few years, the term big data has become an important key point for research into data mining and information retrieval. Through the years, the quantity of data managed across enterprises has evolved from a simple and imperceptible task to an extent to which it has become the central performance improvemen...	[ "What representations for textual documents do they use?", "Which dataset(s) do they use?", "How do they evaluate knowledge extraction performance?" ]	[ [ "" ], [ "" ], [ "" ] ]
Pretrained word representations have a long history in Natural Language Processing (NLP), from non-neural methods BIBREF0, BIBREF1, BIBREF2 to neural word embeddings BIBREF3, BIBREF4 and to contextualised representations BIBREF5, BIBREF6. Approaches shifted more recently from using these representations as an input to ...	[ "What is CamemBERT trained on?", "Which tasks does CamemBERT not improve on?", "What is the state of the art?", "How much better was results of CamemBERT than previous results on these tasks?", "Was CamemBERT compared against multilingual BERT on these tasks?", "How long was CamemBERT trained?", "What d...	[ [ "" ], [ "" ], [ "POS and DP task: CONLL 2018\nNER task: (no extensive work) Strong baselines CRF and BiLSTM-CRF\nNLI task: mBERT or XLM (not clear from text)" ], [ "" ], [ "" ], [ "" ], [ "" ] ]
Controversy is a phenomenom with a high impact at various levels. It has been broadly studied from the perspective of different disciplines, ranging from the seminal analysis of the conflicts within the members of a karate club BIBREF0 to political issues in modern times BIBREF1, BIBREF2. The irruption of digital socia...	[ "What are the state of the art measures?", "What controversial topics are experimented with?", "What datasets did they use?", "What social media platform is observed?", "How many languages do they experiment with?" ]	[ [ "" ], [ "" ], [ "" ], [ "" ], [ "" ] ]
Microblog sentiment analysis; Twitter opinion miningSentiment Analysis: This is text analysis aiming to determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a piece of text.Sentiment analysis on Twitter is the use of natural language processing techniques to i...	[ "What is the current SOTA for sentiment analysis on Twitter at the time of writing?", "What difficulties does sentiment analysis on Twitter have, compared to sentiment analysis in other domains?", "What are the metrics to evaluate sentiment analysis on Twitter?" ]	[ [ "" ], [ "Tweets noisy nature, use of creative spelling and punctuation, misspellings, slang, new words, URLs, and genre-specific terminology and abbreviations, short (length limited) text" ], [ "" ] ]
Vector representations are becoming truly essential in majority of natural language processing tasks. Word embeddings became widely popular with the introduction of word2vec BIBREF0 and GloVe BIBREF1 and their properties have been analyzed in length from various aspects.Studies of word embeddings range from word simila...	[ "How many sentence transformations on average are available per unique sentence in dataset?", "What annotations are available in the dataset?", "How are possible sentence transformations represented in dataset, as new sentences?", "What are all 15 types of modifications ilustrated in the dataset?", "Is this...	[ [ "27.41 transformation on average of single seed sentence is available in dataset." ], [ "For each source sentence, transformation sentences that are transformed according to some criteria (paraphrase, minimal change etc.)" ], [ "Yes, as new sentences." ], [ "- paraphrase 1\n- paraphras...
Today's increasing flood of information on the web creates a need for automated multi-document summarization systems that produce high quality summaries. However, producing summaries in a multi-document setting is difficult, as the language used to display the same information in a sentence can vary significantly, maki...	[ "How big is dataset domain-specific embedding are trained on?", "How big is unrelated corpus universal embedding is traned on?", "How better are state-of-the-art results than this model? " ]	[ [ "" ], [ "" ], [ "" ] ]
Twitter sentiment classification have intensively researched in recent years BIBREF0 BIBREF1 . Different approaches were developed for Twitter sentiment classification by using machine learning such as Support Vector Machine (SVM) with rule-based features BIBREF2 and the combination of SVMs and Naive Bayes (NB) BIBREF3...	[ "What were their results on the three datasets?", "What was the baseline?", "Which datasets did they use?", "Are results reported only on English datasets?", "Which three Twitter sentiment classification datasets are used for experiments?", "What semantic rules are proposed?" ]	[ [ "accuracy of 86.63 on STS, 85.14 on Sanders and 80.9 on HCR" ], [ "" ], [ "" ], [ "" ], [ "" ], [ "rules that compute polarity of words after POS tagging or parsing steps" ] ]
Knowledge graphs (KGs) such as Freebase BIBREF0 , DBpedia BIBREF1 , and YAGO BIBREF2 play a critical role in various NLP tasks, including question answering BIBREF3 , information retrieval BIBREF4 , and personalized recommendation BIBREF5 . A typical KG consists of numerous facts about a predefined set of entities. Eac...	[ "Which knowledge graph completion tasks do they experiment with?", "Apart from using desired properties, do they evaluate their LAN approach in some other way?", "Do they evaluate existing methods in terms of desired properties?" ]	[ [ "" ], [ "" ], [ "" ] ]
It is well known that sentiment annotation or labeling is subjective BIBREF0. Annotators often have many disagreements. This is especially so for crowd-workers who are not well trained. That is why one always feels that there are many errors in an annotated dataset. In this paper, we study whether it is possible to bui...	[ "How does the model differ from Generative Adversarial Networks?", "What is the dataset used to train the model?", "What is the performance of the model?", "Is the model evaluated against a CNN baseline?" ]	[ [ "" ], [ "" ], [ "Experiment 1: ACC around 0.5 with 50% noise rate in worst case - clearly higher than baselines for all noise rates\nExperiment 2: ACC on real noisy datasets: 0.7 on Movie, 0.79 on Laptop, 0.86 on Restaurant (clearly higher than baselines in almost all cases)" ], [ "" ...
There has been significant research on style transfer, with the goal of changing the style of text while preserving its semantic content. The alternative where semantics are adjusted while keeping style intact, which we call semantic text exchange (STE), has not been investigated to the best of our knowledge. Consider ...	[ "Does the model proposed beat the baseline models for all the values of the masking parameter tested?", "Has STES been previously used in the literature to evaluate similar tasks?", "What are the baseline models mentioned in the paper?" ]	[ [ "" ], [ "" ], [ "" ] ]
Speaker recognition including identification and verification, aims to recognize claimed identities of speakers. After decades of research, performance of speaker recognition systems has been vastly improved, and the technique has been deployed to a wide range of practical applications. Nevertheless, the present speake...	[ "What was the performance of both approaches on their dataset?", "What kind of settings do the utterances come from?", "What genres are covered?", "Do they experiment with cross-genre setups?", "Which of the two speech recognition models works better overall on CN-Celeb?", "By how much is performance on C...	[ [ "ERR of 19.05 with i-vectors and 15.52 with x-vectors" ], [ "" ], [ "genre, entertainment, interview, singing, play, movie, vlog, live broadcast, speech, drama, recitation and advertisement" ], [ "" ], [ "x-vector" ], [ "For i-vector system, performances are 11.75% infe...
Deep neural network-based models are easy to overfit and result in losing their generalization due to limited size of training data. In order to address the issue, data augmentation methods are often applied to generate more training samples. Recent years have witnessed great success in applying data augmentation in th...	[ "On what datasets is the new model evaluated on?", "How do the authors measure performance?", "Does the new objective perform better than the original objective bert is trained on?", "Are other pretrained language models also evaluated for contextual augmentation? ", "Do the authors report performance of co...	[ [ "" ], [ "Accuracy across six datasets" ], [ "" ], [ "" ], [ "" ] ]

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