Abstract
This paper describes a translation model for ancient Chinese to modern Chinese and English for the Evahan 2023 competition, a subtask of the Ancient Language Translation 2023 challenge. During the training of our model, we applied various data augmentation techniques and used SiKu-RoBERTa as part of our model architecture. The results indicate that back translation improves the model’s performance, but double back translation introduces noise and harms the model’s performance. Fine-tuning on the original dataset can be helpful in solving the issue.- Anthology ID:
- 2023.alt-1.6
- Volume:
- Proceedings of ALT2023: Ancient Language Translation Workshop
- Month:
- September
- Year:
- 2023
- Address:
- Macau SAR, China
- Venue:
- alt
- SIG:
- Publisher:
- Asia-Pacific Association for Machine Translation
- Note:
- Pages:
- 43–47
- Language:
- URL:
- https://aclanthology.org/2023.alt-1.6
- DOI:
- Cite (ACL):
- Li Zeng, Yanzhi Tian, Yingyu Shan, and Yuhang Guo. 2023. BIT-ACT: An Ancient Chinese Translation System Using Data Augmentation. In Proceedings of ALT2023: Ancient Language Translation Workshop, pages 43–47, Macau SAR, China. Asia-Pacific Association for Machine Translation.
- Cite (Informal):
- BIT-ACT: An Ancient Chinese Translation System Using Data Augmentation (Zeng et al., alt 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2023.alt-1.6.pdf