Machine Translation of minority languages presents unique challenges, including the paucity of bilingual training data and the unavailability of linguistically-trained speakers. This paper focuses on a machine learning approach to transfer-based MT, where data in the form of translations and lexical alignments are elicited from bilingual speakers, and a seeded version-space learning algorithm formulates and refines transfer rules. A rule-generalization lattice is defined based on LFG-style f-structures, permitting generalization operators in the search for the most general rules consistent with the elicited data. The paper presents these methods and illustrates examples.
- Machine Translation
- Relative Clause
- Target Language
- Minority Language
- Source Language
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Carbonell, J. et al. (2002). Automatic Rule Learning for Resource-Limited MT. In: Richardson, S.D. (eds) Machine Translation: From Research to Real Users. AMTA 2002. Lecture Notes in Computer Science(), vol 2499. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45820-4_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44282-0
Online ISBN: 978-3-540-45820-3
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