{"type":"ScholarlyArticle","title":"Event Factuality Identification via Generative Adversarial Networks with Auxiliary Classification","authors":[{"name":"Zhong Qian","slug":"zhong-qian","given":"Zhong","family":"Qian","affiliation":"School of Computer Science and Technology , Soochow University , Suzhou , China"},{"name":"Peifeng Li","slug":"peifeng-li","given":"Peifeng","family":"Li","affiliation":"School of Computer Science and Technology , Soochow University , Suzhou , China"},{"name":"Yue Zhang","slug":"yue-zhang","given":"Yue","family":"Zhang","affiliation":"Singapore University of Technology and Design , Singapore"},{"name":"Guodong Zhou","slug":"guodong-zhou","given":"Guodong","family":"Zhou","affiliation":"School of Computer Science and Technology , Soochow University , Suzhou , China"},{"name":"Qiaoming Zhu","slug":"qiaoming-zhu","given":"Qiaoming","family":"Zhu","affiliation":"School of Computer Science and Technology , Soochow University , Suzhou , China"}],"abstract":"Event factuality identification is an important semantic task in NLP. Traditional research heavily relies on annotated texts. This paper proposes a two-step framework, first extracting essential factors related with event factuality from raw texts as the input, and then identifying the factuality of events via a Generative Adversarial Network with Auxiliary Classification (AC-GAN). The use of AC-GAN allows the model to learn more syntactic information and address the imbalance among factuality values. Experimental results on FactBank show that our method significantly outperforms several state-of-the-art baselines, particularly on events with embedded sources, speculative and negative factuality values.","doi":"10.24963/ijcai.2018/597","url":"https://ijcai.org/proceedings/2018/597","pdf":"https://ijcai.org/proceedings/2018/0597.pdf","markdown":"https://ijcai.org/proceedings/2018/597.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/597","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Natural Language Processing","track":"Main track","number":597,"pages":{"first":4293,"last":4300},"keywords":["Natural Language Processing: Natural Language Processing","Natural Language Processing: Information Extraction"],"videos":[],"references":[{"position":1,"raw":"[Adel and Schütze, 2017] Heike Adel and Hinrich Schütze. 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