Improve Example-Based Machine Translation Quality for Low-Resource Language Using Ontology

DOI

10.2991/ijndc.2017.5.3.6How to use a DOI?

Keywords

Knowledge Engineering, WordNet, Example-Based Machine Translation;

Abstract

In this research we propose to use ontology to improve the performance of an EBMT system for low-resource language pair. The EBMT architecture use chunk-string templates (CSTs) and unknown word translation mechanism. CSTs consist of a chunk in source-language, a string in target-language, and word alignment in-formation. For unknown word translation, we used WordNet hypernym tree and English-Bengali dictionary. CSTs improved the wide-coverage by 57 points and quality by 48.81 points in human evaluation. Currently 64.29% of the test-set translations by the system were acceptable. The combined solutions of CSTs and unknown words generated 67.85% acceptable translations from the test-set. Un-known words mechanism improved translation quality by 3.56 points in human evaluation.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Khan Md Anwarus Salam
AU  - Setsuo Yamada
AU  - Nishino Tetsuro
PY  - 2017
DA  - 2017/07/03
TI  - Improve Example-Based Machine Translation Quality for Low-Resource Language Using Ontology
JO  - International Journal of Networked and Distributed Computing
SP  - 176
EP  - 191
VL  - 5
IS  - 3
SN  - 2211-7946
UR  - https://doi.org/10.2991/ijndc.2017.5.3.6
DO  - 10.2991/ijndc.2017.5.3.6
ID  - Salam2017
ER  -