{"type":"ScholarlyArticle","title":"A Document-grounded Matching Network for Response Selection in Retrieval-based Chatbots","authors":[{"name":"Xueliang Zhao","slug":"xueliang-zhao","given":"Xueliang","family":"Zhao","affiliation":"Institute of Computer Science and Technology , Peking University , Beijing , China"},{"name":"Chongyang Tao","slug":"chongyang-tao","given":"Chongyang","family":"Tao","affiliation":"Institute of Computer Science and Technology , Peking University , Beijing , China"},{"name":"Wei Wu","slug":"wei-wu","given":"Wei","family":"Wu","affiliation":"Microsoft Corporation , Beijing , China"},{"name":"Can Xu","slug":"can-xu","given":"Can","family":"Xu","affiliation":"Microsoft Corporation , Beijing , China"},{"name":"Dongyan Zhao","slug":"dongyan-zhao","given":"Dongyan","family":"Zhao","affiliation":"Institute of Computer Science and Technology , Peking University , Beijing , China; Center for Data Science , Peking University , Beijing , China"},{"name":"Rui Yan","slug":"rui-yan","given":"Rui","family":"Yan","affiliation":"Institute of Computer Science and Technology , Peking University , Beijing , China; Center for Data Science , Peking University , Beijing , China"}],"abstract":"We present a document-grounded matching network (DGMN) for response selection that can power a knowledge-aware retrieval-based chatbot system. The challenges of building such a model lie in how to ground conversation contexts with background documents and how to recognize important information in the documents for matching. To overcome the challenges, DGMN fuses information in a document and a context into representations of each other, and dynamically determines if grounding is necessary and importance of different parts of the document and the context through hierarchical interaction with a response at the matching step. Empirical studies on two public data sets indicate that DGMN can significantly improve upon state-of-the-art methods and at the same time enjoys good interpretability.","doi":"10.24963/ijcai.2019/756","url":"https://ijcai.org/proceedings/2019/756","pdf":"https://ijcai.org/proceedings/2019/0756.pdf","markdown":"https://ijcai.org/proceedings/2019/756.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/756","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":756,"pages":{"first":5443,"last":5449},"keywords":["Natural Language Processing: Dialogue","Natural Language Processing: Information Retrieval"],"videos":[],"references":[{"position":1,"raw":"[Ba et al., 2016] Jimmy Lei Ba, Jamie Ryan Kiros, and Ge- offrey E Hinton. 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