Improving Multilingual Neural Machine Translation with Auxiliary Source Languages
Weijia Xu, Yuwei Yin, Shuming Ma, Dongdong Zhang, Haoyang Huang
Abstract
Multilingual neural machine translation models typically handle one source language at a time. However, prior work has shown that translating from multiple source languages improves translation quality. Different from existing approaches on multi-source translation that are limited to the test scenario where parallel source sentences from multiple languages are available at inference time, we propose to improve multilingual translation in a more common scenario by exploiting synthetic source sentences from auxiliary languages. We train our model on synthetic multi-source corpora and apply random masking to enable flexible inference with single-source or bi-source inputs. Extensive experiments on Chinese/English-Japanese and a large-scale multilingual translation benchmark show that our model outperforms the multilingual baseline significantly by up to +4.0 BLEU with the largest improvements on low-resource or distant language pairs.- Anthology ID:
- 2021.findings-emnlp.260
- 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:
- 3029–3041
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.260
- DOI:
- 10.18653/v1/2021.findings-emnlp.260
- Cite (ACL):
- Weijia Xu, Yuwei Yin, Shuming Ma, Dongdong Zhang, and Haoyang Huang. 2021. Improving Multilingual Neural Machine Translation with Auxiliary Source Languages. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3029–3041, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Improving Multilingual Neural Machine Translation with Auxiliary Source Languages (Xu et al., Findings 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.findings-emnlp.260.pdf
- Code
- YuweiYin/MNMT_Aux_Src_Lang
- Data
- ASPEC