Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning

Zhong Qian, Heng Zhang, Peifeng Li, Qiaoming Zhu, Guodong Zhou


Abstract
Document-level Event Factuality Identification (DEFI) predicts the factuality of a specific event based on a document from which the event can be derived, which is a fundamental and crucial task in Natural Language Processing (NLP). However, most previous studies only considered sentence-level task and did not adopt document-level knowledge. Moreover, they modelled DEFI as a typical text classification task depending on annotated information heavily, and limited to the task-specific corpus only, which resulted in data scarcity. To tackle these issues, we propose a new framework formulating DEFI as Machine Reading Comprehension (MRC) tasks considering both Span-Extraction (Ext) and Multiple-Choice (Mch). Our model does not employ any other explicit annotated information, and utilizes Transfer Learning (TL) to extract knowledge from universal large-scale MRC corpora for cross-domain data augmentation. The empirical results on DLEFM corpus demonstrate that the proposed model outperforms several state-of-the-arts.
Anthology ID:
2022.coling-1.231
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2622–2632
Language:
URL:
https://aclanthology.org/2022.coling-1.231
DOI:
Bibkey:
Cite (ACL):
Zhong Qian, Heng Zhang, Peifeng Li, Qiaoming Zhu, and Guodong Zhou. 2022. Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2622–2632, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning (Qian et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.231.pdf
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