A discriminative framework of integrating translation memory features into SMT

Liangyou Li, Andy Way, Qun Liu


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:
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2014.amta-researchers.19.pdf