Datasets

AI2 Irvine
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Viewing 1-4 of 4 datasets
  • ZEST: ZEroShot learning from Task descriptions

    ZEST is a benchmark for zero-shot generalization to unseen NLP tasks, with 25K labeled instances across 1,251 different tasks.AI2 Irvine, Mosaic, AllenNLP • 2020ZEST tests whether NLP systems can perform unseen tasks in a zero-shot way, given a natural language description of the task. It is an instantiation of our proposed framework "learning from task descriptions". The tasks include classification, typed entity… more
  • Quoref

    24K Question/Answer (QA) pairs over 4.7K paragraphs, split between train (19K QAs), development (2.4K QAs) and a hidden test partition (2.5K QAs).AllenNLP, AI2 Irvine • 2019Quoref is a QA dataset which tests the coreferential reasoning capability of reading comprehension systems. In this span-selection benchmark containing 24K questions over 4.7K paragraphs from Wikipedia, a system must resolve hard coreferences before selecting… more
  • Reasoning Over Paragraph Effects in Situations (ROPES)

    14k QA pairs over 1.7K paragraphs, split between train (10k QAs), development (1.6k QAs) and a hidden test partition (1.7k QAs).AllenNLP, AI2 Irvine • 2019ROPES is a QA dataset which tests a system's ability to apply knowledge from a passage of text to a new situation. A system is presented a background passage containing a causal or qualitative relation(s), a novel situation that uses this background, and… more
  • Discrete Reasoning Over the content of Paragraphs (DROP)

    The DROP dataset contains 96k Question and Answering pairs (QAs) 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… more
AI2 Irvine
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