Document-Level Event Factuality Identification via Adversarial Neural Network

Zhong Qian, Peifeng Li, Qiaoming Zhu, Guodong Zhou


Abstract
Document-level event factuality identification is an important subtask in event factuality and is crucial for discourse understanding in Natural Language Processing (NLP). Previous studies mainly suffer from the scarcity of suitable corpus and effective methods. To solve these two issues, we first construct a corpus annotated with both document- and sentence-level event factuality information on both English and Chinese texts. Then we present an LSTM neural network based on adversarial training with both intra- and inter-sequence attentions to identify document-level event factuality. Experimental results show that our neural network model can outperform various baselines on the constructed corpus.
Anthology ID:
N19-1287
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Jill Burstein, Christy Doran, Thamar Solorio
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2799–2809
Language:
URL:
https://aclanthology.org/N19-1287
DOI:
10.18653/v1/N19-1287
Bibkey:
Cite (ACL):
Zhong Qian, Peifeng Li, Qiaoming Zhu, and Guodong Zhou. 2019. Document-Level Event Factuality Identification via Adversarial Neural Network. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2799–2809, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Document-Level Event Factuality Identification via Adversarial Neural Network (Qian et al., NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-1287.pdf
Software:
 N19-1287.Software.zip