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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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A Richly Annotated Corpus for Different Tasks in Automated Fact-Checking

A Richly Annotated Corpus for Different Tasks in Automated Fact-Checking

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A Large-Scale Multilingual Dataset of Misspellings and Grammatical Errors

A Large-Scale Multilingual Dataset of Misspellings and Grammatical Errors

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RyanSpeech: A Corpus for Conversational Text-to-Speech Synthesis

RyanSpeech: A Corpus for Conversational Text-to-Speech Synthesis

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古滕贝格图书公司

古滕贝格图书公司

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开放式研究科珀斯

开放式研究科珀斯

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WAC：维基百科对话在线滥用检测的科珀斯

WAC：维基百科对话在线滥用检测的科珀斯

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阿拉伯语语音语料库

阿拉伯语语音语料库

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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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波兰议会党团

波兰议会党团

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