Abstract
The existing Document-Level Event Factuality Identification (DEFI) work relies on the syntactic and semantic features of event trigger and sentences. However, focusing only on the relevant features of event trigger may omit the important information for event factuality identification, while finding critical information from the whole document is still challenging. In this paper, our motivation is that DEFI can be inferred from a complete set of evidential sentences rather than the event trigger. Hence, we construct a new Evidence-Based Document-Level Event Factuality (EB-DLEF) corpus, and introduce a new evidential sentence selection task for DEFI. Moreover, we propose a pipeline approach to solve the two-step work of evidential sentence selection and event factuality identification, which outperforms various baselines.
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Acknowledgments
The authors would like to thank the three anonymous reviewers for their comments on this paper. This research was supported by the National Natural Science Foundation of China (Nos. 61836007, and 62006167), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).
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Zhang, H., Qian, Z., Li, P., Zhu, X. (2022). Evidence-Based 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_18
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