Abstract
Learning multiscale Transformer models has been evidenced as a viable approach to augmenting machine translation systems. Prior research has primarily focused on treating subwords as basic units in developing such systems. However, the incorporation of fine-grained character-level features into multiscale Transformer has not yet been explored. In this work, we present a Slow-Fast two-stream learning model, referred to as TranSFormer, which utilizes a “slow” branch to deal with subword sequences and a “fast” branch to deal with longer character sequences. This model is efficient since the fast branch is very lightweight by reducing the model width, and yet provides useful fine-grained features for the slow branch. Our TranSFormer shows consistent BLEU improvements (larger than 1 BLEU point) on several machine translation benchmarks.- Anthology ID:
- 2023.findings-acl.430
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6883–6896
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.430
- DOI:
- 10.18653/v1/2023.findings-acl.430
- Cite (ACL):
- Bei Li, Yi Jing, Xu Tan, Zhen Xing, Tong Xiao, and Jingbo Zhu. 2023. TranSFormer: Slow-Fast Transformer for Machine Translation. In Findings of the Association for Computational Linguistics: ACL 2023, pages 6883–6896, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- TranSFormer: Slow-Fast Transformer for Machine Translation (Li et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2023.findings-acl.430.pdf