The Value of Semantic Parse Labeling for Knowledge Base Question Answering - Yih, Scott Wen-tau; Richardson, Matthew; Meek, Chris; Chang, Ming-Wei; Suh, Jina (Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2016)
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.