A Document-Level Neural Machine Translation Model with Dynamic Caching Guided by Theme-Rheme Information
Yiqi Tong, Jiangbin Zheng, Hongkang Zhu, Yidong Chen, Xiaodong Shi
Abstract
Research on document-level Neural Machine Translation (NMT) models has attracted increasing attention in recent years. Although the proposed works have proved that the inter-sentence information is helpful for improving the performance of the NMT models, what information should be regarded as context remains ambiguous. To solve this problem, we proposed a novel cache-based document-level NMT model which conducts dynamic caching guided by theme-rheme information. The experiments on NIST evaluation sets demonstrate that our proposed model achieves substantial improvements over the state-of-the-art baseline NMT models. As far as we know, we are the first to introduce theme-rheme theory into the field of machine translation.- Anthology ID:
- 2020.coling-main.388
- Volume:
- Proceedings of the 28th International Conference on Computational Linguistics
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 4385–4395
- Language:
- URL:
- https://aclanthology.org/2020.coling-main.388
- DOI:
- 10.18653/v1/2020.coling-main.388
- Cite (ACL):
- Yiqi Tong, Jiangbin Zheng, Hongkang Zhu, Yidong Chen, and Xiaodong Shi. 2020. A Document-Level Neural Machine Translation Model with Dynamic Caching Guided by Theme-Rheme Information. In Proceedings of the 28th International Conference on Computational Linguistics, pages 4385–4395, Barcelona, Spain (Online). International Committee on Computational Linguistics.
- Cite (Informal):
- A Document-Level Neural Machine Translation Model with Dynamic Caching Guided by Theme-Rheme Information (Tong et al., COLING 2020)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2020.coling-main.388.pdf
- Code
- ydc/ctrd