{"type":"ScholarlyArticle","title":"AILA: A Question Answering System in the Legal Domain","authors":[{"name":"Weiyi Huang","slug":"weiyi-huang","given":"Weiyi","family":"Huang","affiliation":"Shenzhen Institutes of Advanced Technology , Chinese Academy of Sciences; University of Chinese Academy of Sciences; SIAT-DELI AI and Law Joint Lab"},{"name":"Jiahao Jiang","slug":"jiahao-jiang","given":"Jiahao","family":"Jiang","affiliation":"Shenzhen Institutes of Advanced Technology , Chinese Academy of Sciences"},{"name":"Qiang Qu","slug":"qiang-qu","given":"Qiang","family":"Qu","affiliation":"Shenzhen Institutes of Advanced Technology , Chinese Academy of Sciences"},{"name":"Min Yang","slug":"min-yang","given":"Min","family":"Yang","affiliation":"Shenzhen Institutes of Advanced Technology , Chinese Academy of Sciences; SIAT-DELI AI and Law Joint Lab"}],"abstract":"Question answering (QA) in the legal domain has gained increasing popularity for people to seek legal advice. However, existing QA systems struggle to comprehend the legal context and provide jurisdictionally relevant answers due to the lack of domain expertise. In this paper, we develop an Artificial Intelligence Law Assistant (AILA) for question answering in the domain of Chinese laws. AILA system automatically comprehends users' natural language queries with the help of the legal knowledge graph (KG) and provides the best matching answers for given queries. In addition, AILA provides visual cues to interpret the input queries and candidate answers based on the legal KG. Experimental results on a large-scale legal QA corpus show the effectiveness of AILA. To the best of our knowledge, AILA is the first Chinese legal QA system which integrates the domain knowledge from legal KG to comprehend the questions and answers for ranking QA pairs. AILA is available at http://bmilab.ticp.io:48478/.","doi":"10.24963/ijcai.2020/762","url":"https://ijcai.org/proceedings/2020/762","pdf":"https://ijcai.org/proceedings/2020/0762.pdf","markdown":"https://ijcai.org/proceedings/2020/762.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/762","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Demos","track":"Demos","number":762,"pages":{"first":5258,"last":5260},"keywords":["Natural Language Processing: general"],"videos":[],"references":[{"position":1,"raw":"[Feng et al., 2015] Minwei Feng, Bing Xiang, Michael R Glass, Lidan Wang, and Bowen Zhou. 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