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Leveraging bilingual terminology to improve machine translation in a CAT environment*

Published online by Cambridge University Press:  30 May 2017

MIHAEL ARCAN ,
MARCO TURCHI ,
SARA TONELLI  and
PAUL BUITELAAR
MIHAEL ARCAN
Affiliation:
Insight Centre for Data Analytics, National University of Ireland, Galway e-mail: [email protected], [email protected]
MARCO TURCHI
Affiliation:
FBK- Fondazione Bruno Kessler, Via Sommarive 18, 38123 Trento, Italy e-mail: [email protected], [email protected]
SARA TONELLI
Affiliation:
FBK- Fondazione Bruno Kessler, Via Sommarive 18, 38123 Trento, Italy e-mail: [email protected], [email protected]
PAUL BUITELAAR
Affiliation:
Insight Centre for Data Analytics, National University of Ireland, Galway e-mail: [email protected], [email protected]
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Abstract

This work focuses on the extraction and integration of automatically aligned bilingual terminology into a Statistical Machine Translation (SMT) system in a Computer Aided Translation scenario. We evaluate the proposed framework that, taking as input a small set of parallel documents, gathers domain-specific bilingual terms and injects them into an SMT system to enhance translation quality. Therefore, we investigate several strategies to extract and align terminology across languages and to integrate it in an SMT system. We compare two terminology injection methods that can be easily used at run-time without altering the normal activity of an SMT system: XML markup and cache-based model. We test the cache-based model on two different domains (information technology and medical) in English, Italian and German, showing significant improvements ranging from 2.23 to 6.78 BLEU points over a baseline SMT system and from 0.05 to 3.03 compared to the widely-used XML markup approach.

Type
Articles
Information
Natural Language Engineering , Volume 23 , Issue 5 , September 2017 , pp. 763 - 788
Copyright
Copyright © Cambridge University Press 2017 

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Footnotes

*

This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 (Insight).

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