Enhancing statistical machine translation with bilingual terminology in a CAT environment

Mihael Arcan, Marco Turchi, Sara Topelli, Paul Buitelaar


Abstract
In this paper, we address the problem of extracting and integrating bilingual terminology into a Statistical Machine Translation (SMT) system for a Computer Aided Translation (CAT) tool scenario. We develop a framework that, taking as input a small amount of parallel in-domain data, gathers domain-specific bilingual terms and injects them in an SMT system to enhance the translation productivity. Therefore, we investigate several strategies to extract and align bilingual terminology, and to embed it into the SMT. We compare two embedding methods that can be easily used at run-time without altering the normal activity of an SMT system: XML markup and the cache-based model. We tested our framework on two different domains showing improvements up to 15% BLEU score points.
Anthology ID:
2014.amta-researchers.5
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
Venue:
AMTA
SIG:
Publisher:
Association for Machine Translation in the Americas
Note:
Pages:
54–68
Language:
URL:
https://aclanthology.org/2014.amta-researchers.5
DOI:
Bibkey:
Cite (ACL):
Mihael Arcan, Marco Turchi, Sara Topelli, and Paul Buitelaar. 2014. Enhancing statistical machine translation with bilingual terminology in a CAT environment. In Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track, pages 54–68, Vancouver, Canada. Association for Machine Translation in the Americas.
Cite (Informal):
Enhancing statistical machine translation with bilingual terminology in a CAT environment (Arcan et al., AMTA 2014)
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PDF:
https://aclanthology.org/2014.amta-researchers.5.pdf