Cross-Lingual Event Detection via Optimized Adversarial Training

Luis Guzman-Nateras, Minh Van Nguyen, Thien Nguyen


Abstract
In this work, we focus on Cross-Lingual Event Detection where a model is trained on data from a \textit{source} language but its performance is evaluated on data from a second, \textit{target}, language. Most recent works in this area have harnessed the language-invariant qualities displayed by pre-trained Multi-lingual Language Models. Their performance, however, reveals there is room for improvement as the cross-lingual setting entails particular challenges. We employ Adversarial Language Adaptation to train a Language Discriminator to discern between the source and target languages using unlabeled data. The discriminator is trained in an adversarial manner so that the encoder learns to produce refined, language-invariant representations that lead to improved performance. More importantly, we optimize the adversarial training process by only presenting the discriminator with the most informative samples. We base our intuition about what makes a sample informative on two disparate metrics: sample similarity and event presence. Thus, we propose leveraging Optimal Transport as a solution to naturally combine these two distinct information sources into the selection process. Extensive experiments on 8 different language pairs, using 4 languages from unrelated families, show the flexibility and effectiveness of our model that achieves state-of-the-art results.
Anthology ID:
2022.naacl-main.409
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5588–5599
Language:
URL:
https://aclanthology.org/2022.naacl-main.409
DOI:
10.18653/v1/2022.naacl-main.409
Bibkey:
Cite (ACL):
Luis Guzman-Nateras, Minh Van Nguyen, and Thien Nguyen. 2022. Cross-Lingual Event Detection via Optimized Adversarial Training. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5588–5599, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Cross-Lingual Event Detection via Optimized Adversarial Training (Guzman-Nateras et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.409.pdf