{"type":"ScholarlyArticle","title":"Document-level Event Factuality Identification via Reinforced Multi-Granularity Hierarchical Attention Networks","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; AI Research Institute , 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; AI Research Institute , Soochow University , Suzhou , China"},{"name":"Guodong Zhou","slug":"guodong-zhou","given":"Guodong","family":"Zhou","affiliation":"School of Computer Science and Technology , Soochow University , Suzhou , China; AI Research Institute , Soochow University , Suzhou , China"}],"abstract":"Document-level Event Factuality Identification (DEFI) predicts the event factuality according to the current document, and mainly depends on event-related tokens and sentences. However, previous studies relied on annotated information and did not filter irrelevant and noisy texts. Therefore, this paper proposes a novel end-to-end model, i.e., Reinforced Multi-Granularity Hierarchical Attention Network (RMHAN), which can learn information at different levels of granularity from tokens and sentences hierarchically. Moreover, with hierarchical reinforcement learning, RMHAN first selects relevant and meaningful tokens, and then selects useful sentences for document-level encoding. Experimental results on DLEF-v2 corpus show that RMHAN model outperforms several state-of-the-art baselines and achieves the best performance.","doi":"10.24963/ijcai.2022/602","url":"https://ijcai.org/proceedings/2022/602","pdf":"https://ijcai.org/proceedings/2022/0602.pdf","markdown":"https://ijcai.org/proceedings/2022/602.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/602","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Natural Language Processing","track":"Main Track","number":602,"pages":{"first":4338,"last":4345},"keywords":["Natural Language Processing: Information Extraction","Natural Language Processing: Text Classification","Natural Language Processing: Natural Language Semantics","Natural Language Processing: Information Retrieval and Text Mining"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986494","length_sec":79,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984637","length_sec":358,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Cao et al., 2020] Jinjuan Cao, Zhong Qian, Peifeng Li, Xi- aoxu Zhu, and Qiaoming Zhu. 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