Abstract
Event factuality identification (EFI) aims to assess the veracity degree to which an event mentioned in a document has happened, and both semantic and syntactic features are crucial for this task. Most of the previous studies only focused on sentence-level event factuality, which may lead to conflicts among mentions of a specific event in a document. Existing studies on document-level EFI (DEFI) are still scarce and mainly focus on semantic features. To address the above issues, we propose a novel Heterogeneous Semantics-Syntax-fused Network (HS\(^2\)N) for DEFI, which not only integrates both semantic and syntactic information in an efficient way using Biaffine Attention and differentiated alignment method, but also considers both inter-and-intra sentence interaction. Experimental results on the English and Chinese datasets show that our proposed HS\(^2\)N outperforms the state-of-the-art model.
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Acknowledgements
The authors would like to thank the two anonymous reviewers for their comments on this paper. This research was supported by the National Natural Science Foundation of China (No. 61836007 and 62006167.), and Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).
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Zhang, Z., Liu, C., Qian, Z., Zhu, X., Li, P. (2022). HS\(^2\)N: Heterogeneous Semantics-Syntax Fusion Network for Document-Level Event Factuality Identification. In: Khanna, S., Cao, J., Bai, Q., Xu, G. (eds) PRICAI 2022: Trends in Artificial Intelligence. PRICAI 2022. Lecture Notes in Computer Science, vol 13630. Springer, Cham. https://doi.org/10.1007/978-3-031-20865-2_23
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