Abstract
Combining Translation Memory (TM) with Statistical Machine Translation (SMT) together has been demonstrated to be beneficial. In this paper, we present a discriminative framework which can integrate TM into SMT by incorporating TM-related feature functions. Experiments on English–Chinese and English–French tasks show that our system using TM feature functions only from the best fuzzy match performs significantly better than the baseline phrase- based system on both tasks, and our discriminative model achieves comparable results to those of an effective generative model which uses similar features. Furthermore, with the capacity of handling a large amount of features in the discriminative framework, we propose a method to efficiently use multiple fuzzy matches which brings more feature functions and further significantly improves our system.- Anthology ID:
- 2014.amta-researchers.19
- Volume:
- Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track
- Month:
- October 22-26
- Year:
- 2014
- Address:
- Vancouver, Canada
- Editors:
- Yaser Al-Onaizan, Michel Simard
- Venue:
- AMTA
- SIG:
- Publisher:
- Association for Machine Translation in the Americas
- Note:
- Pages:
- 249–260
- Language:
- URL:
- https://aclanthology.org/2014.amta-researchers.19
- DOI:
- Cite (ACL):
- Liangyou Li, Andy Way, and Qun Liu. 2014. A discriminative framework of integrating translation memory features into SMT. In Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track, pages 249–260, Vancouver, Canada. Association for Machine Translation in the Americas.
- Cite (Informal):
- A discriminative framework of integrating translation memory features into SMT (Li et al., AMTA 2014)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2014.amta-researchers.19.pdf