{"type":"ScholarlyArticle","title":"Deep Semantic Compliance Advisor for Unstructured Document Compliance Checking","authors":[{"name":"Honglei Guo","slug":"honglei-guo","given":"Honglei","family":"Guo","affiliation":"IBM Research - China"},{"name":"Bang An","slug":"bang-an","given":"Bang","family":"An","affiliation":"IBM Research - China"},{"name":"Zhili Guo","slug":"zhili-guo","given":"Zhili","family":"Guo","affiliation":"IBM Research - China"},{"name":"Zhong Su","slug":"zhong-su","given":"Zhong","family":"Su","affiliation":"IBM Research - China"}],"abstract":"Unstructured document compliance  checking is  always a big challenge for banks since huge amounts of contracts and regulations written in natural language \r\n  require professionals' interpretation and judgment. Traditional rule-based or keyword-based methods cannot precisely characterize the deep semantic distribution in the unstructured document semantic  compliance checking due to the semantic complexity of contracts and regulations. Deep Semantic Compliance Advisor (DSCA) is an unstructured document compliance checking platform which  provides multi-level semantic comparison by  deep learning algorithms. In the statement-level semantic comparison,  a  Graph Neural Network (GNN) based  syntactic sentence encoder  is proposed to capture the  complicate syntactic   and semantic clues of the statement sentences. This GNN-based encoder outperforms existing syntactic sentence encoders in deep semantic comparison and is more beneficial for long sentences. In the clause-level semantic comparison, an attention-based semantic relatedness detection model is applied to find the most relevant legal clauses. DSCA significantly enhances the productivity of legal professionals in the unstructured document compliance checking for banks.","doi":"10.24963/ijcai.2020/613","url":"https://ijcai.org/proceedings/2020/613","pdf":"https://ijcai.org/proceedings/2020/0613.pdf","markdown":"https://ijcai.org/proceedings/2020/613.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/613","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":"Special Track on AI in FinTech","track":"Special Track on AI in FinTech","number":613,"pages":{"first":4446,"last":4452},"keywords":["AI for regulation: AI for international regulation","AI for regulation: General","AI for regulation: AI for corporate governance and regulation"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24370/file_24370.m3u8","length_sec":236,"provider":"hls"}],"references":[{"position":1,"raw":"[Alemi et al., 2017] Alex Alemi, Ian Fischer, Josh Dillon, and Kevin Murphy. 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