{"type":"ScholarlyArticle","title":"Guided Generation of Cause and Effect","authors":[{"name":"Zhongyang Li","slug":"zhongyang-li","given":"Zhongyang","family":"Li","affiliation":"Harbin Institute of Technology , China; Johns Hopkins University , USA; visiting Johns Hopkins University."},{"name":"Xiao Ding","slug":"xiao-ding","given":"Xiao","family":"Ding","affiliation":"Harbin Institute of Technology , China"},{"name":"Ting Liu","slug":"ting-liu","given":"Ting","family":"Liu","affiliation":"Harbin Institute of Technology , China"},{"name":"J. Edward Hu","slug":"j-edward-hu","given":"J. Edward","family":"Hu","affiliation":"Johns Hopkins University , USA"},{"name":"Benjamin Van Durme","slug":"benjamin-van-durme","given":"Benjamin","family":"Van Durme","affiliation":"Johns Hopkins University , USA"}],"abstract":"We present a conditional text generation framework that posits sentential expressions of possible causes and effects. This framework depends on two novel resources we develop in the course of this work: a very large-scale collection of English sentences expressing causal patterns (CausalBank); and a refinement over previous work on constructing large lexical causal knowledge graphs (Cause Effect Graph). Further, we extend prior work in lexically-constrained decoding to support disjunctive positive constraints. Human assessment confirms that our approach gives high-quality and diverse outputs. Finally, we use CausalBank to perform continued training of an encoder supporting a recent state-of-the-art model for causal reasoning, leading to a 3-point improvement on the COPA challenge set, with no change in model architecture.","doi":"10.24963/ijcai.2020/502","url":"https://ijcai.org/proceedings/2020/502","pdf":"https://ijcai.org/proceedings/2020/0502.pdf","markdown":"https://ijcai.org/proceedings/2020/502.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/502","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":502,"pages":{"first":3629,"last":3636},"keywords":["Knowledge Representation and Reasoning: Action, Change and Causality","Natural Language Processing: Natural Language Processing","Natural Language Processing: Knowledge Extraction","Natural Language Processing: Natural Language Generation"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24610/file_24610.m3u8","length_sec":291,"provider":"hls"}],"references":[{"position":1,"raw":"[Ammanabrolu et al., 2019] P. 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