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arXiv · 1704.05572

Answering Complex Questions Using Open Information Extraction

Tushar Khot·Ashish Sabharwal·Peter Clark
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Abstract

While there has been substantial progress in factoid question-answering (QA), answering complex questions remains challenging, typically requiring both a large body of knowledge and inference techniques. Open Information Extraction (Open IE) provides a way to generate semi-structured knowledge for QA, but to date such knowledge has only been used to answer simple questions with retrieval-based methods. We overcome this limitation by presenting a method for reasoning with Open IE knowledge, allowing more complex questions to be handled. Using a recently proposed support graph optimization framework for QA, we develop a new inference model for Open IE, in particular one that can work effectively with multiple short facts, noise, and the relational structure of tuples. Our model significantly outperforms a state-of-the-art structured solver on complex questions of varying difficulty, while also removing the reliance on manually curated knowledge.

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Tushar Khot, Ashish Sabharwal, Peter Clark. 2017-04-19. Answering Complex Questions Using Open Information Extraction. https://arxiv.org/abs/1704.05572

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