Abstract
Recently, fine-tuning pre-trained language models (e.g., multilingual BERT) to downstream cross-lingual tasks has shown promising results. However, the fine-tuning process inevitably changes the parameters of the pre-trained model and weakens its cross-lingual ability, which leads to sub-optimal performance. To alleviate this problem, we leverage continual learning to preserve the original cross-lingual ability of the pre-trained model when we fine-tune it to downstream tasks. The experimental result shows that our fine-tuning methods can better preserve the cross-lingual ability of the pre-trained model in a sentence retrieval task. Our methods also achieve better performance than other fine-tuning baselines on the zero-shot cross-lingual part-of-speech tagging and named entity recognition tasks.- Anthology ID:
- 2021.repl4nlp-1.8
- Volume:
- Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Anna Rogers, Iacer Calixto, Ivan Vulić, Naomi Saphra, Nora Kassner, Oana-Maria Camburu, Trapit Bansal, Vered Shwartz
- Venue:
- RepL4NLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 64–71
- Language:
- URL:
- https://aclanthology.org/2021.repl4nlp-1.8
- DOI:
- 10.18653/v1/2021.repl4nlp-1.8
- Cite (ACL):
- Zihan Liu, Genta Indra Winata, Andrea Madotto, and Pascale Fung. 2021. Preserving Cross-Linguality of Pre-trained Models via Continual Learning. In Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pages 64–71, Online. Association for Computational Linguistics.
- Cite (Informal):
- Preserving Cross-Linguality of Pre-trained Models via Continual Learning (Liu et al., RepL4NLP 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.repl4nlp-1.8.pdf