Abstract
In this paper, we address the Event Detection task under a zero-shot cross-lingual setting where a model is trained on a source language but evaluated on a distinct target language for which there is no labeled data available. Most recent efforts in this field follow a direct transfer approach in which the model is trained using language-invariant features and then directly applied to the target language. However, we argue that these methods fail to take advantage of the benefits of the data transfer approach where a cross-lingual model is trained on target-language data and is able to learn task-specific information from syntactical features or word-label relations in the target language. As such, we propose a hybrid knowledge-transfer approach that leverages a teacher-student framework where the teacher and student networks are trained following the direct and data transfer approaches, respectively. Our method is complemented by a hierarchical training-sample selection scheme designed to address the issue of noisy labels being generated by the teacher model. Our model achieves state-of-the-art results on 9 morphologically-diverse target languages across 3 distinct datasets, highlighting the importance of exploiting the benefits of hybrid transfer.- Anthology ID:
- 2023.acl-long.296
- Volume:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5414–5427
- Language:
- URL:
- https://aclanthology.org/2023.acl-long.296
- DOI:
- 10.18653/v1/2023.acl-long.296
- Cite (ACL):
- Luis Guzman Nateras, Franck Dernoncourt, and Thien Nguyen. 2023. Hybrid Knowledge Transfer for Improved Cross-Lingual Event Detection via Hierarchical Sample Selection. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5414–5427, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Hybrid Knowledge Transfer for Improved Cross-Lingual Event Detection via Hierarchical Sample Selection (Guzman Nateras et al., ACL 2023)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2023.acl-long.296.pdf