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

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 a...

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Bibliographic Details
Published in:The International journal of networked and distributed computing (Online) 2017-07, Vol.5 (3), p.176-191
Main Authors: Salam, Khan Md Anwarus, Yamada, Setsuo, Tetsuro, Nishino
Format: Article
Language:English
Subjects:
Example-Based Machine Translation
Knowledge Engineering
Research Article
WordNet
Online Access:Get full text
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Description
Summary: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.
ISSN:2211-7938
2211-7946
2211-7946
DOI:10.2991/ijndc.2017.5.3.6