Date of Original Version
6-2002
Type
Conference Proceeding
Published In
S.D. Richardson (Ed.): AMTA 2002, LNAI 2499, pp. 1–10, 2002.
Abstract or Table of Contents
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.