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arXiv · 1610.09996

End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension

Yang Yu·Wei Zhang·Kazi Hasan·Mo Yu·Bing Xiang·Bowen Zhou
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Abstract

This paper proposes dynamic chunk reader (DCR), an end-to-end neural reading comprehension (RC) model that is able to extract and rank a set of answer candidates from a given document to answer questions. DCR is able to predict answers of variable lengths, whereas previous neural RC models primarily focused on predicting single tokens or entities. DCR encodes a document and an input question with recurrent neural networks, and then applies a word-by-word attention mechanism to acquire question-aware representations for the document, followed by the generation of chunk representations and a ranking module to propose the top-ranked chunk as the answer. Experimental results show that DCR achieves state-of-the-art exact match and F1 scores on the SQuAD dataset.

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Yang Yu, Wei Zhang, Kazi Hasan, Mo Yu, Bing Xiang, Bowen Zhou. 2016-10-31. End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension. https://arxiv.org/abs/1610.09996

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