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Leveraging Bilingual Terminology to Improve Machine Translation in a CAT Environment

Authors: 

Mihael Arcan, Marco Turchi, Sara Tonelli, Paul Buitelaar

Publication Type: 
Refereed Original Article
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.
Digital Object Identifer (DOI): 
10.1017/S1351324917000195
Publication Status: 
Published
Publication Date: 
30/05/2017
Journal: 
Natural Language Engineering
Research Group: 
Institution: 
NUIG
Project Acknowledges: 
Open access repository: 
No