{"type":"ScholarlyArticle","title":"Knowledge Base Question Answering with Topic Units","authors":[{"name":"Yunshi Lan","slug":"yunshi-lan","given":"Yunshi","family":"Lan","affiliation":"School of Information System , Singapore Management University"},{"name":"Shuohang Wang","slug":"shuohang-wang","given":"Shuohang","family":"Wang","affiliation":"School of Information System , Singapore Management University"},{"name":"Jing Jiang","slug":"jing-jiang","given":"Jing","family":"Jiang","affiliation":"School of Information System , Singapore Management University"}],"abstract":"Knowledge base question answering (KBQA) is an important task in natural language processing. Existing methods for KBQA usually start with entity linking, which considers mostly named entities found in a question as the starting points in the KB to search for answers to the question. However, relying only on entity linking to look for answer candidates may not be sufficient. In this paper, we propose to perform topic unit linking where topic units cover a wider range of units of a KB. We use a generation-and-scoring approach to gradually refine the set of topic units. Furthermore, we use reinforcement learning to jointly learn the parameters for topic unit linking and answer candidate ranking in an end-to-end manner. Experiments on three commonly used benchmark datasets show that our method consistently works well and outperforms the previous state of the art on two datasets.","doi":"10.24963/ijcai.2019/701","url":"https://ijcai.org/proceedings/2019/701","pdf":"https://ijcai.org/proceedings/2019/0701.pdf","markdown":"https://ijcai.org/proceedings/2019/701.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/701","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Natural Language Processing","track":"Main track","number":701,"pages":{"first":5046,"last":5052},"keywords":["Natural Language Processing: Natural Language Processing","Machine Learning: Knowledge-based Learning","Natural Language Processing: Question Answering"],"videos":[],"references":[{"position":1,"raw":"[Berant et al., 2013] Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 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