Abstract
Domain adaptation remains a challenge in the realm of Neural Machine Translation (NMT), even in the era of large language models (LLMs). Existing non-parametric approaches like nearest neighbor machine translation have made small Autoregressive Translation (AT) models achieve efficient domain generalization and adaptation without updating parameters, but leaving the Non-Autoregressive Translation (NAT) counterparts under-explored. To fill this blank, we introduce Bi-kNN, an innovative and efficient domain adaptation approach for NAT models that tailors a k-nearest-neighbor algorithm for NAT. Specifically, we introduce an effective datastore construction and correlated updating strategies to conform the parallel nature of NAT. Additionally, we train a meta-network that seamlessly integrates the NN distribution with the NMT distribution robustly during the iterative decoding process of NAT. Our experimental results across four benchmark datasets demonstrate that our Bi-kNN not only achieves significant improvements over the Base-NAT model (7.8 BLEU on average) but also exhibits enhanced efficiency.- Anthology ID:
- 2024.findings-acl.810
- Volume:
- Findings of the Association for Computational Linguistics ACL 2024
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand and virtual meeting
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 13657–13670
- Language:
- URL:
- https://aclanthology.org/2024.findings-acl.810
- DOI:
- Cite (ACL):
- WangJie You, Pei Guo, Juntao Li, Kehai Chen, and Min Zhang. 2024. Efficient Domain Adaptation for Non-Autoregressive Machine Translation. In Findings of the Association for Computational Linguistics ACL 2024, pages 13657–13670, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.
- Cite (Informal):
- Efficient Domain Adaptation for Non-Autoregressive Machine Translation (You et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.findings-acl.810.pdf