Publications
Publication details [#3081]
Carbonell, Jaime, Katharina Probst, Erik Peterson, Christina Monson, Alon Lavie, Ralf Brown and Lori Levin. 2002. Automatic rule learning for resource-limited MT. In Richardson, Stephen D., ed. Machine Translation: from research to real users (Lecture Notes in Computer Science 2499). Cham: Springer. pp. 1–10.
Publication type
Article in jnl/bk
Publication language
English
Abstract
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.
Source : Bitra