Adversarial Training for Weakly Supervised Event Detection

Xiaozhi Wang, Xu Han, Zhiyuan Liu, Maosong Sun, Peng Li


Abstract
Modern weakly supervised methods for event detection (ED) avoid time-consuming human annotation and achieve promising results by learning from auto-labeled data. However, these methods typically rely on sophisticated pre-defined rules as well as existing instances in knowledge bases for automatic annotation and thus suffer from low coverage, topic bias, and data noise. To address these issues, we build a large event-related candidate set with good coverage and then apply an adversarial training mechanism to iteratively identify those informative instances from the candidate set and filter out those noisy ones. The experiments on two real-world datasets show that our candidate selection and adversarial training can cooperate together to obtain more diverse and accurate training data for ED, and significantly outperform the state-of-the-art methods in various weakly supervised scenarios. The datasets and source code can be obtained from https://github.com/thunlp/Adv-ED.
Anthology ID:
N19-1105
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
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
998–1008
Language:
URL:
https://aclanthology.org/N19-1105
DOI:
10.18653/v1/N19-1105
Bibkey:
Cite (ACL):
Xiaozhi Wang, Xu Han, Zhiyuan Liu, Maosong Sun, and Peng Li. 2019. Adversarial Training for Weakly Supervised Event Detection. 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 998–1008, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Adversarial Training for Weakly Supervised Event Detection (Wang et al., NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-1105.pdf
Video:
 https://vimeo.com/353485593
Code
 thunlp/Adv-ED