Abstract
Prompting has recently been shown as a promising approach for applying pre-trained language models to perform downstream tasks. We present Multi-Stage Prompting, a simple and automatic approach for leveraging pre-trained language models to translation tasks. To better mitigate the discrepancy between pre-training and translation, MSP divides the translation process via pre-trained language models into three separate stages: the encoding stage, the re-encoding stage, and the decoding stage. During each stage, we independently apply different continuous prompts for allowing pre-trained language models better shift to translation tasks. We conduct extensive experiments on three translation tasks. Experiments show that our method can significantly improve the translation performance of pre-trained language models.- Anthology ID:
- 2022.acl-long.424
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Smaranda Muresan, Preslav Nakov, Aline Villavicencio
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6131–6142
- Language:
- URL:
- https://aclanthology.org/2022.acl-long.424
- DOI:
- 10.18653/v1/2022.acl-long.424
- Cite (ACL):
- Zhixing Tan, Xiangwen Zhang, Shuo Wang, and Yang Liu. 2022. MSP: Multi-Stage Prompting for Making Pre-trained Language Models Better Translators. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6131–6142, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- MSP: Multi-Stage Prompting for Making Pre-trained Language Models Better Translators (Tan et al., ACL 2022)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2022.acl-long.424.pdf
- Code
- thunlp-mt/plm4mt