Counter-Interference Adapter for Multilingual Machine Translation
Yaoming Zhu, Jiangtao Feng, Chengqi Zhao, Mingxuan Wang, Lei Li
Abstract
Developing a unified multilingual model has been a long pursuing goal for machine translation. However, existing approaches suffer from performance degradation - a single multilingual model is inferior to separately trained bilingual ones on rich-resource languages. We conjecture that such a phenomenon is due to interference brought by joint training with multiple languages. To accommodate the issue, we propose CIAT, an adapted Transformer model with a small parameter overhead for multilingual machine translation. We evaluate CIAT on multiple benchmark datasets, including IWSLT, OPUS-100, and WMT. Experiments show that the CIAT consistently outperforms strong multilingual baselines on 64 of total 66 language directions, 42 of which have above 0.5 BLEU improvement.- Anthology ID:
- 2021.findings-emnlp.240
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2812–2823
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.240
- DOI:
- 10.18653/v1/2021.findings-emnlp.240
- Cite (ACL):
- Yaoming Zhu, Jiangtao Feng, Chengqi Zhao, Mingxuan Wang, and Lei Li. 2021. Counter-Interference Adapter for Multilingual Machine Translation. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2812–2823, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Counter-Interference Adapter for Multilingual Machine Translation (Zhu et al., Findings 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2021.findings-emnlp.240.pdf
- Code
- yaoming95/ciat
- Data
- OPUS-100