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Meaning preservation in Example-based Machine Translation with structural semantics
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DOI：10.1016/j.eswa.2017.02.021.png)
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Abstract
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The main tasks in Example-based Machine Translation (EBMT) comprise of source text decomposition, following with translation examples matching and selection, and finally adaptation and recombination of the target translation. As the natural language is ambiguous in nature, the preservation of source text's meaning throughout these processes is complex and challenging. A structural semantics is introduced, as an attempt towards meaning-based approach to improve the EBMT system. The structural semantics is used to support deeper semantic similarity measurement and impose structural constraints in translation examples selection. A semantic compositional structure is derived from the structural semantics of the selected translation examples. This semantic compositional structure serves as a representation structure to preserve the consistency and integrity of the input sentence's meaning structure throughout the recombination process. In this paper, an English to Malay EBMT system is presented to demonstrate the practical application of this structural semantics. Evaluation of the translation test results shows that the new translation framework based on the structural semantics has outperformed the previous EBMT framework. (C) 2017 Elsevier Ltd. All rights reserved.
Keywords:
Example-based Machine Translation
Structured String-Tree Correspondence
Synchronous Structured String-Tree
Correspondence
Structural semantics
Semantic roles
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