I would need it in German, but it is not tragic if it is in another language since it can be translated. Dataset 08/06/2020 ∙ by Patrick Lewis, et al. Questions Answering Dataset Abstract: While models have reached superhuman performance on popular question answering (QA) datasets such as SQuAD, they have yet to outperform humans on the task of question answering itself. A dataset covering 14,042 questions from NQ-open. Question Answering Dataset QASC is the first dataset to offer two desirable properties: (a) the facts to be composed are an- Question-Answer Dataset 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://... Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasets. VQA: Visual Question Answering Question Answering Question Answering Dataset (SQuAD), blending ideas from existing state-of-the-art models to achieve results that surpass the original logistic regression base-lines. It would also be okay if the format is not the same, I would only need contexts, questions and answers. Movies and TV shows, for example, benefit from professional camera movements, clean editing, crisp audio recordings, and scripted dialog between professional actors. Question Answering Dataset an Open-Domain Question Answering System SQuAD The models are implemented with Java and … The dataset contains over 760K questions with around 10M answers. The dataset now includes 10,898 articles, 17,794 tweets, and 13,757 crowdsourced question-answer pairs. 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. Actually QALD also provides hybrid questions as well as questions from the biomedical domain. In the BioASQ project (http://bioasq.org) we also cre... We also made sure to balance the dataset, tightly controlling the answer distribution for different groups of questions, in order to prevent educated guesses using … Closed 2 days ago. squad Question-Answer Dataset. VQA is a new dataset containing open-ended questions about images. to improve the performance of Question Answering (QA) system, such QA systems fail to extend its performance beyond in-domain datasets. The bAbI-Question Answering is a dataset for question noting and text understanding. Question Answering in Context (QuAC) is a dataset for modeling, understanding, and … The answer to every question is a segment of text, or span, from the corresponding reading passage. The corpus has 1 million questions … Archived Releases. CommonsenseQA is a new multiple-choice question answering dataset that requires different types of commonsense knowledge to predict the correct answers . Current datasets, and the models built upon them, have focused on questions which are answerable by direct analysis of the … Perform the following: a) Read all Update the question so it's on-topic for Data Science Stack Exchange. 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. other kinds of question answering datasets (Manju-natha et al.,2018;Kaushik and Lipton,2018;Sug-awara et al.,2018,2020), we know comparatively little about how the questions and answers are dis-tributed in these ODQA benchmarks, making it hard to understand and contextualize the results we are observing. 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. SQuAD contains 107,785 question-answer pairs on 536 articles, and Datasets are sorted by year of publication. 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. 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 … It consists of 108,442 natural language questions, each paired with a corresponding fact from Freebase knowledge base. Answering tasks, where the system tries to provide the correct answer to the query with a given context paragraph. question and answer. Question Datasets WebQuestions. A Chinese Multi-type Complex Questions Answering Dataset over Wikidata. SQuAD2.0 The Stanford Question Answering Dataset Despite the number of currently available datasets on video-question answering, there still remains a need for a dataset involving multi-step and non-factoid answers. PDF: https://www.aclweb.org/anthology/D13-1160.pdf 11/11/2021 ∙ by Jianyun Zou, et al. EmrQA is a domain-specific large-scale question answering (QA) datasets by re-purposing existing expert annotations on clinical notes for various NLP tasks from the community shared i2b2 datasets. … The dataset was generated using 38 unique templates together with 5,042 entities and 615 predicates. Visual Question Answering is a new task that can facilitate the extraction of information from images through textual queries: it aims at answering an open-ended question for-mulated in natural language about a given image. A question answering system that in addition to providing an answer provides an explanation of the reasoning that leads to that answer has potential advantages in terms of debuggability, extensibility, and trust. This is the official repository for the code and models of the paper CCQA: A New Web-Scale Question Answering Dataset for Model Pre-Training. That focus on answer extraction, the previous version of the paper CCQA: New. > Question Answering ( QA ) you need good samples, for instance, tricky examples for no! From the corresponding reading passage each paired with a corresponding fact from Freebase knowledge base dataset was using! To bAbI, MCTest is a social media-focused Question Answering dataset < /a Abstract... Generated using 38 unique templates together with 5,042 entities and 615 predicates in the context of pain management ''. A New Web-Scale Question Answering dataset the reading comprehension from QA datasets by using... 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