Abstract
This paper introduces the Dtranx AI translation system, developed for the WMT 2023 Universal Translation Shared Task. Our team participated in two language directions: English to Chinese and Chinese to English. Our primary focus was on enhancing the effectiveness of the Chinese-to-English model through the implementation of bilingual models. Our approach involved various techniques such as data corpus filtering, model size scaling, sparse expert models (especially the Transformer model with adapters), large-scale back-translation, and language model reordering. According to automatic evaluation, our system secured the first place in the English-to-Chinese category and the second place in the Chinese-to-English category.- Anthology ID:
- 2023.wmt-1.11
- Original:
- 2023.wmt-1.11v1
- Version 2:
- 2023.wmt-1.11v2
- Volume:
- Proceedings of the Eighth Conference on Machine Translation
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Philipp Koehn, Barry Haddow, Tom Kocmi, Christof Monz
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 143–149
- Language:
- URL:
- https://aclanthology.org/2023.wmt-1.11
- DOI:
- 10.18653/v1/2023.wmt-1.11
- Cite (ACL):
- Luo Min, Yixin Tan, and Qiulin Chen. 2023. Yishu: Yishu at WMT2023 Translation Task. In Proceedings of the Eighth Conference on Machine Translation, pages 143–149, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Yishu: Yishu at WMT2023 Translation Task (Min et al., WMT 2023)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2023.wmt-1.11.pdf