{"type":"ScholarlyArticle","title":"TransOMCS: From Linguistic Graphs to Commonsense Knowledge","authors":[{"name":"Hongming Zhang","slug":"hongming-zhang","given":"Hongming","family":"Zhang","affiliation":"The Hong Kong University of Science and Technology"},{"name":"Daniel Khashabi","slug":"daniel-khashabi","given":"Daniel","family":"Khashabi","affiliation":"Allen Institute for AI"},{"name":"Yangqiu Song","slug":"yangqiu-song","given":"Yangqiu","family":"Song","affiliation":"The Hong Kong University of Science and Technology"},{"name":"Dan Roth","slug":"dan-roth","given":"Dan","family":"Roth","affiliation":"University of Pennsylvania"}],"abstract":"Commonsense knowledge acquisition is a key problem for artificial intelligence. Conventional methods of acquiring commonsense knowledge generally require laborious and costly human annotations, which are not feasible on a large scale. In this paper, we explore a practical way of mining commonsense knowledge from linguistic graphs, with the goal of transferring cheap knowledge obtained with linguistic patterns into expensive commonsense knowledge. The result is a conversion of ASER [Zhang et al., 2020], a large-scale selectional preference knowledge resource, into TransOMCS, of the same representation as ConceptNet [Liu and Singh, 2004] but two orders of magnitude larger. Experimental results demonstrate the transferability of linguistic knowledge to commonsense knowledge and the effectiveness of the proposed approach in terms of quantity, novelty, and quality. TransOMCS is publicly available at: https://github.com/HKUST-KnowComp/TransOMCS.","doi":"10.24963/ijcai.2020/554","url":"https://ijcai.org/proceedings/2020/554","pdf":"https://ijcai.org/proceedings/2020/0554.pdf","markdown":"https://ijcai.org/proceedings/2020/554.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/554","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":"Natural Language Processing","track":"Main track","number":554,"pages":{"first":4004,"last":4010},"keywords":["Natural Language Processing: Resources and Evaluation","Natural Language Processing: Knowledge Extraction"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25170/file_25170.m3u8","length_sec":304,"provider":"hls"}],"references":[{"position":1,"raw":"[Bosselut et al., 2019] Antoine Bosselut, Hannah Rashkin, Maarten Sap, Chaitanya Malaviya, Asli Celikyilmaz, and Yejin Choi. 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