Explanation-based Learning for Machine Translation
Janine Toole, Fred Popowich, Devlan Nicholson, Davide Turcato, Paul, McFetridge
TL;DR
This paper applies explanation-based learning to improve the parsing component of a real-time English-Spanish machine translation system, balancing efficiency and coverage to enhance translation performance.
Contribution
It introduces EBL techniques tailored for machine translation parsing, optimizing coverage without sacrificing space and time efficiency.
Findings
EBL improves parsing efficiency in machine translation
Coverage can be increased while maintaining efficiency
Performance results show the approach is effective
Abstract
In this paper we present an application of explanation-based learning (EBL) in the parsing module of a real-time English-Spanish machine translation system designed to translate closed captions. We discuss the efficiency/coverage trade-offs available in EBL and introduce the techniques we use to increase coverage while maintaining a high level of space and time efficiency. Our performance results indicate that this approach is effective.
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Taxonomy
TopicsNatural Language Processing Techniques · Topic Modeling · Multimodal Machine Learning Applications