[1] released the the Stanford Question Answering Dataset(SQuAD 1.0) which consists of 100K question-answer pairs each with a given context paragraph and it soon Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasets. Strongly Generalizable Question Answering Dataset (GrailQA) is a new large-scale, high-quality dataset for question answering on knowledge bases (KBQA) on Freebase with 64,331 questions annotated with both answers and corresponding logical forms in different syntax (i.e., SPARQL, S-expression, etc.). : just 1% in Natural Questions (Kwiatkowski et al.,2019) and 6% in HotpotQA (Yang et al., 2018). In an open-book exam, students are allowed to refer to external resources like notes and books while answering test questions. duce GQA, a new dataset for visual reasoning and compo-sitional question answering. 10 ground truth answers per question. Our dataset is based on the Largescale Complex Question Answering Dataset (LC-QuAD), which is a complex question answering dataset over DBpedia containing 5,000 pairs of questions and their SPARQL queries. Clinical question answering (QA) (or reading comprehension) aims to automatically answer questions from medical professionals based on clinical texts. Closed 2 days ago. Given a factoid question, if a language model has no context or is not big enough to memorize the context which exists in the training dataset, it is unlikely to guess the correct answer. These data were collected by Noah Smith, Michael Heilman, Rebecca Hwa, Shay Cohen,Kevin Gimpel, and many students at Carnegie Mellon … Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. There are 100,000+ question-answer pairs on 500+ articles. SQuAD and 30M Factoid questions are the recent ones. If you are looking for a limited set of benchmark questions, I suggest you to look at https://... Answer is the answer. Question-Answer Datasets for Chatbot Training. In this paper, we investigate if models are learning reading comprehension from QA datasets by evaluating BERT-based models across five datasets. Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. AmbigQA, a new open-domain question answering task that consists of predicting a set of question and answer pairs, where each plausible answer is associated with a disambiguated rewriting of the original question. The dataset now includes 10,898 articles, 17,794 tweets, and 13,757 crowdsourced question-answer pairs. A question-answer pair is a very short conversation which can be also used to train chatbots. Datasets are sorted by year of publication. Question Answering datasets. SQuAD is probably one of the most popular question answering datasets (it’s been cited over 2,000 times) because it’s well-created and improves on many aspects that other datasets fail to address. Shr. This project aims to improve the performance of DistiIBERT-based QA model trained on in-domain datasets in out-of-domain datasets by only using provided datasets. A dataset covering 14,042 questions from NQ-open. It consists of 108,442 natural language questions, each paired with a corresponding fact from Freebase knowledge base. A collection of large datasets containing questions and their answers for use in Natural Language Processing tasks like question answering (QA). SQuAD contains 107,785 question-answer pairs on 536 articles, and SQuAD is the Stanford Question Answering Dataset. This dataset is created by the researchers at IBM and the University of California and can be viewed as the first large-scale dataset for QA over social media data. Current video question answering datasets consist of movies and TV shows. We present WIKIQA, a dataset for open-domain question answering.2 The dataset con-tains 3,047 questions originally sampled from Bing query logs. Content Visual Question Answering (VQA) has attracted much attention in both computer vision and natural language processing communities, not least because it offers insight into the relationships between two important sources of information. ∙ Facebook ∙ 14 ∙ share . The dataset was generated using 38 unique templates together with 5,042 entities and 615 predicates. It would also be okay if the format is not the same, I would only need contexts, questions and answers. It contains 12,102 questions with one correct answer and four distractor answers. VQA is a new dataset containing open-ended questions about images. In this work, we introduce a new dataset to tackle the task of visual question answering on remote sensing images: this large- The dataset is collected from crowd-workers supply questions and answers based on a set of over 10,000 news articles from CNN, with answers consisting of spans of text from the corresponding articles. The dataset contains 119,633 natural language questions posed by crowd-workers on 12,744 news articles from CNN. Question Answering on SQuAD dataset is a task to find an answer on question in a given context (e.g, paragraph from Wikipedia), where the answer to each question is a segment of the context: Context: In meteorology, precipitation is any product of the condensation of atmospheric water vapor that falls under gravity. the reasoning aspect of question answering. Collection of Question Answering Dataset Published in ArXiv 1 minute read Question Answering (QA) Systems is an automated approach to retrieve correct responses to the questions asked by human in natural language Dwivedi & Singh, 2013.I have tried to collect and curate some publications form Arxiv that related to question answering dataset, and the … The "questionanswerpairs.txt" files contain both the questions and answers. The dataset we will use is The Stanford Question Answering Dataset, it references over 100,000 answers associated with their question. This attention is mainly motivated by the long-sought transformation in information retrieval (IR) … Archived Releases. If you use our dataset, code or any parts thereof, please cite this paper: The dataset is split into 29808 train questions, 6894 dev questions and 3003 test questions. In other document-based question answering datasets that focus on answer extraction, the answer to a given question occurs in multiple documents. In SQuAD, however, the model only has access to a single passage, presenting a much more difficult task since it isn’t as forgiving to miss the answer. It contains both English and Hindi content. There are 10 empty fields/boxes below the chart. We developed 55 medical question-answer pairs across five different types of pain management: each question includes a detailed patient-specific medical scenario ("vignette") designed to enable the substitution of multiple different racial and gender … See also our curated list of datasets https://github.com/dice-group/NLIWOD/tree/master/qa.datasets These questions require an understanding of vision, language and commonsense knowledge to answer. This talk advocates for a user-centric perspective on how to approach multilingual question answering systems. Answering tasks, where the system tries to provide the correct answer to the query with a given context paragraph. Span of the Wikipedia article from which questions and 3003 test questions: //docs.deeppavlov.ai/en/master/features/models/squad.html '' > reasoning... Answer to every Question is a segment of text, or span, the. 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