The Value of Semantic Parse Labeling for Knowledge Base Question Answering

Scott Wen-tau Yih, Matthew Richardson, Chris Meek, Ming-Wei Chang, Jina Suh

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics |

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We demonstrate the value of collecting semantic parse labels for knowledge base question answering. In particular, (1) unlike previous studies on small-scale datasets, we show that learning from labeled semantic parses significantly improves overall performance, resulting in absolute 5 point gain compared to learning from answers, (2) we show that with an appropriate user interface, one can obtain semantic parses with high accuracy and at a cost comparable or lower than obtaining just answers, and (3) we have created and shared the largest semantic-parse labeled dataset to date in order to advance research in question answering.