Abstract
This paper describes DUT-NLP Lab’s submission to the WMT-21 triangular machine translation shared task. The participants are not allowed to use other data and the translation direction of this task is Russian-to-Chinese. In this task, we use the Transformer as our baseline model, and integrate several techniques to enhance the performance of the baseline, including data filtering, data selection, fine-tuning, and post-editing. Further, to make use of the English resources, such as Russian/English and Chinese/English parallel data, the relationship triangle is constructed by multilingual neural machine translation systems. As a result, our submission achieves a BLEU score of 21.9 in Russian-to-Chinese.- Anthology ID:
- 2021.wmt-1.38
- Volume:
- Proceedings of the Sixth Conference on Machine Translation
- Month:
- November
- Year:
- 2021
- Address:
- Online
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 331–335
- Language:
- URL:
- https://aclanthology.org/2021.wmt-1.38
- DOI:
- Cite (ACL):
- Huan Liu, Junpeng Liu, Kaiyu Huang, and Degen Huang. 2021. DUTNLP Machine Translation System for WMT21 Triangular Translation Task. In Proceedings of the Sixth Conference on Machine Translation, pages 331–335, Online. Association for Computational Linguistics.
- Cite (Informal):
- DUTNLP Machine Translation System for WMT21 Triangular Translation Task (Liu et al., WMT 2021)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2021.wmt-1.38.pdf