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MIMIC-III（

MIMIC-III（"重症监护医疗信息市场"）

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QASC: A Dataset for Question Answering via Sentence CompositionComposing knowledge from multiple pieces of texts is a key challenge in multi-hop question answering. We present a multi-hop reasoning dataset, Question Answering via Sentence Composition(QASC), that requires retrieving facts from a large corpus and composing them to answer a multiple-choice question. QASC is the first dataset to offer two desirable properties: (a) the facts to be composed are annotated in a large corpus, and (b) the decomposition into these facts is not evident from the question itself. The latter makes retrieval challenging as the system must introduce new concepts or relations in order to discover potential decompositions. Further, the reasoning model must then learn to identify valid compositions of these retrieved facts using common-sense reasoning. To help address these challenges, we provide annotation for supporting facts as well as their composition. Guided by these annotations, we present a two-step approach to mitigate the retrieval challenges. We use other multiple-choice datasets as additional training data to strengthen the reasoning model. Our proposed approach improves over current state-of-the-art language models by 11% (absolute). The reasoning and retrieval problems, however, remain unsolved as this model still lags by 20% behind human performance.

QASC: A Dataset for Question Answering via Sentence CompositionComposing knowledge from multiple pieces of texts is a key challenge in multi-hop question answering. We present a multi-hop reasoning dataset, Question Answering via Sentence Composition(QASC), that requires retrieving facts from a large corpus and composing them to answer a multiple-choice question. QASC is the first dataset to offer two desirable properties: (a) the facts to be composed are annotated in a large corpus, and (b) the decomposition into these facts is not evident from the question itself. The latter makes retrieval challenging as the system must introduce new concepts or relations in order to discover potential decompositions. Further, the reasoning model must then learn to identify valid compositions of these retrieved facts using common-sense reasoning. To help address these challenges, we provide annotation for supporting facts as well as their composition. Guided by these annotations, we present a two-step approach to mitigate the retrieval challenges. We use other multiple-choice datasets as additional training data to strengthen the reasoning model. Our proposed approach improves over current state-of-the-art language models by 11% (absolute). The reasoning and retrieval problems, however, remain unsolved as this model still lags by 20% behind human performance.

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IAM 50个最常见的作家手写数据集

IAM 50个最常见的作家手写数据集

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The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain

The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain

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Chinese handwritten digits MNIST dataset

Chinese handwritten digits MNIST dataset

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NUBES：西班牙临床文本中否定和不确定性的语料库

NUBES：西班牙临床文本中否定和不确定性的语料库

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Yahoo! N-Grams 2.0

Yahoo! N-Grams 2.0

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句子/概念对的真实含义

句子/概念对的真实含义

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Reddit评论

Reddit评论

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通过安装残余物来在自然语言推理中未学习数据集偏差

通过安装残余物来在自然语言推理中未学习数据集偏差

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