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解释共同接地的参考决议的注释语料库

解释共同接地的参考决议的注释语料库

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兴奋数据集s

兴奋数据集s

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自动语音识别 （ASR） 错误稳健性

自动语音识别 （ASR） 错误稳健性

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康奈尔电影对话语料库（Cornell Movie Dialog Corpus）

康奈尔电影对话语料库（Cornell Movie Dialog Corpus）

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NIPS2015 [Kaggle]

NIPS2015 [Kaggle]

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Fashion-MNIST

Fashion-MNIST

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意图分类和范围外预测的数据集

意图分类和范围外预测的数据集

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奥苏姆德数据集

奥苏姆德数据集

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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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Reasoning Over Paragraph Effects in Situations (ROPES)

Reasoning Over Paragraph Effects in Situations (ROPES)

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