Abstract
Document-level Event Factuality Identification (DEFI) refers to identifying the degree of certainty that a specific event occurs in a document. Previous studies on DEFI failed to link the document-level event factuality with various sentence-level factuality values in the same document. In this paper, we innovatively propose an event factuality inference task to bridge the sentence-level and the document-level event factuality semantically. Specifically, we present a Sentence-to-Document Inference Network (SDIN) that contains a multi-layer interaction module and a gated aggregation module to integrate the above two tasks, and employ a multi-task learning framework to improve the performance of DEFI. The experimental results on the public English and Chinese DLEF datasets show that our model outperforms the SOTA baselines significantly.- Anthology ID:
- 2023.findings-acl.879
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 13990–14002
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.879
- DOI:
- 10.18653/v1/2023.findings-acl.879
- Cite (ACL):
- Heng Zhang, Peifeng Li, Zhong Qian, and Xiaoxu Zhu. 2023. Incorporating Factuality Inference to Identify Document-level Event Factuality. In Findings of the Association for Computational Linguistics: ACL 2023, pages 13990–14002, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Incorporating Factuality Inference to Identify Document-level Event Factuality (Zhang et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2023.findings-acl.879.pdf