Unified Open-Domain Question Answering with Structured and Unstructured Knowledge

• URL: http://arxiv.org/abs/2012.14610v1
• Date: Tue, 29 Dec 2020 05:14:08 GMT
• Title: Unified Open-Domain Question Answering with Structured and Unstructured Knowledge
• Authors: Barlas Oguz, Xilun Chen, Vladimir Karpukhin, Stan Peshterliev, Dmytro Okhonko, Michael Schlichtkrull, Sonal Gupta, Yashar Mehdad, Scott Yih
• Abstract summary: We study open-domain question answering (ODQA) with structured, unstructured and semi-structured knowledge sources. Our approach homogenizes all sources by reducing them to text, and applies recent, powerful retriever-reader models. As a result, our unified model produces state-of-the-art results on 3 popular ODQA benchmarks.
• Score: 7.7429684536437104
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study open-domain question answering (ODQA) with structured, unstructured and semi-structured knowledge sources, including text, tables, lists, and knowledge bases. Our approach homogenizes all sources by reducing them to text, and applies recent, powerful retriever-reader models which have so far been limited to text sources only. We show that knowledge-base QA can be greatly improved when reformulated in this way. Contrary to previous work, we find that combining sources always helps, even for datasets which target a single source by construction. As a result, our unified model produces state-of-the-art results on 3 popular ODQA benchmarks.

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