Viewing 11-20 of 44 datasets
- 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.
- 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.
- 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).
- 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.
- 2771 story questions about qualitative relationshipsAristo • 2018QuaRel is a crowdsourced dataset of 2771 multiple-choice story questions, including their logical forms.
- 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.
- 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.
- 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.
- Over 14K paper drafts and over 10K textual peer reviewsAristo • 2018PeerRead is a dataset of scientific peer reviews available to help researchers study this important artifact.
- 7,787 multiple choice science questions and associated corporaAristo • 2018A new dataset of 7,787 genuine grade-school level, multiple-choice science questions, assembled to encourage research in advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. We are also including a corpus of over 14 million science sentences relevant to the task, and an implementation of three neural baseline models for this dataset. We pose ARC as a challenge to the community.