Abstract
Large scale pre-trained language models have shown groundbreaking performance improvements for transfer learning in the domain of natural language processing. In our paper, we study a pre-trained multilingual BERT model and analyze the OOV rate on downstream tasks, how it introduces information loss, and as a side-effect, obstructs the potential of the underlying model. We then propose multiple approaches for mitigation and demonstrate that it improves performance with the same parameter count when combined with fine-tuning.- Anthology ID:
- 2020.emnlp-main.631
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7846–7852
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.631
- DOI:
- 10.18653/v1/2020.emnlp-main.631
- Cite (ACL):
- Sangwhan Moon and Naoaki Okazaki. 2020. PatchBERT: Just-in-Time, Out-of-Vocabulary Patching. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7846–7852, Online. Association for Computational Linguistics.
- Cite (Informal):
- PatchBERT: Just-in-Time, Out-of-Vocabulary Patching (Moon & Okazaki, EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2020.emnlp-main.631.pdf