Viewing 11-20 of 47 datasets
  • QuaRTz Dataset

    3864 questions about open domain qualitative relationshipsAristo • 2019QuaRTz is a crowdsourced dataset of 3864 multiple-choice questions about open domain qualitative relationships. Each question is paired with one of 405 different background sentences (sometimes short paragraphs).
  • ARC Question Classification Dataset

    7,787 multiple choice questions annotated with question classification labelsAristo • 2019A dataset of detailed problem domain classification labels for each of the 7,787 multiple-choice science questions found in the AI2 Reasoning Challenge (ARC) dataset, to enable targeted pairing of questions with problem-specific solvers. Also included is a taxonomy of 462 detailed problem domains for grade-school science, organized into 6 levels of specificity.
  • What-If Question Answering

    Large-scale dataset of 39705 "What if..." questions over procedural textAristo • 2019The WIQA dataset V1 has 39705 questions containing a perturbation and a possible effect in the context of a paragraph. The dataset is split into 29808 train questions, 6894 dev questions and 3003 test questions.
  • CommonsenseQA

    12,102 multiple-choice questions with one correct answer and four distractor answersAI2 Israel, Question Understanding • 2019CommonsenseQA is a new multiple-choice question answering dataset that requires different types of commonsense knowledge to predict the correct answers. It contains 12,102 questions with one correct answer and four distractor answers.
  • DROP

    The DROP dataset contains 96k QA pairs over 6.7K paragraphs, split between train (77k QAs), development (9.5k QAs) and a hidden test partition (9.5k QAs).AllenNLP, AI2 Irvine • 2019DROP is a QA dataset that tests the comprehensive understanding of paragraphs. In this crowdsourced, adversarially-created, 96k question-answering benchmark, a system must resolve multiple references in a question, map them onto a paragraph, and perform discrete operations over them (such as addition, counting, or sorting).
  • SciCite: Citation intenent classification dataset

    A large dataset of citation intent classification based on citation textSemantic Scholar • 2019Citations play a unique role in scientific discourse and are crucial for understanding and analyzing scientific work. However not all citations are equal. Some citations refer to use of a method from another work, some discuss results or findings of other work, while others are merely background or acknowledgement citations. SciCite is a dataset of 11K manually annotated citation intents based on citation context in the computer science and biomedical domains.
  • QuaRel Dataset

    2771 story questions about qualitative relationshipsAristo • 2018QuaRel is a crowdsourced dataset of 2771 multiple-choice story questions, including their logical forms.
  • OpenBookQA Dataset

    5,957 multiple-choice questions probing a book of 1,326 science factsAristo • 2018OpenBookQA aims to promote research in advanced question-answering, probing a deeper understanding of both the topic (with salient facts summarized as an open book, also provided with the dataset) and the language it is expressed in. In particular, it contains questions that require multi-step reasoning, use of additional common and commonsense knowledge, and rich text comprehension.
  • Open Research Corpus

    Over 39 million published research papers in Computer Science, Neuroscience, and BiomedicalSemantic Scholar • 2018Over 39 million published research papers in Computer Science, Neuroscience, and Biomedical. This is a subset of the full Semantic Scholar corpus which represents papers crawled from the Web and subjected to a number of filters.
  • ProPara Dataset

    488 richly annotated paragraphs about processes (containing 3,300 sentences)Aristo • 2018The ProPara dataset is designed to train and test comprehension of simple paragraphs describing processes (e.g., photosynthesis), designed for the task of predicting, tracking, and answering questions about how entities change during the process.