Abstract
Reinforcement learning has been proved to be effective in handling low resource machine trans-lation tasks and different sampling methods of reinforcement learning affect the performance ofthe model. The reward for generating translation is determined by the scalability and iteration ofthe sampling strategy so it is difficult for the model to achieve bias-variance trade-off. Therefore according to the poor ability of the model to analyze the structure of the sequence in low-resourcetasks this paper proposes a neural machine translation model parameter optimization method for asynchronous dynamic programming training strategies. In view of the experience priority situa-tion under the current strategy each selective sampling experience not only improves the value ofthe experience state but also avoids the high computational resource consumption inherent in tra-ditional valuation methods (such as dynamic programming). We verify the Mongolian-Chineseand Uyghur-Chinese tasks on CCMT2019. The result shows that our method has improved the quality of low-resource neural machine translation model compared with general reinforcement learning methods which fully demonstrates the effectiveness of our method.- Anthology ID:
- 2021.ccl-1.79
- Volume:
- Proceedings of the 20th Chinese National Conference on Computational Linguistics
- Month:
- August
- Year:
- 2021
- Address:
- Huhhot, China
- Venue:
- CCL
- SIG:
- Publisher:
- Chinese Information Processing Society of China
- Note:
- Pages:
- 886–894
- Language:
- English
- URL:
- https://aclanthology.org/2021.ccl-1.79
- DOI:
- Cite (ACL):
- Jia Xiaoning, Hou Hongxu, Wu Nier, Li Haoran, and Chang Xin. 2021. Low-Resource Machine Translation based on Asynchronous Dynamic Programming. In Proceedings of the 20th Chinese National Conference on Computational Linguistics, pages 886–894, Huhhot, China. Chinese Information Processing Society of China.
- Cite (Informal):
- Low-Resource Machine Translation based on Asynchronous Dynamic Programming (Xiaoning et al., CCL 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.ccl-1.79.pdf