Paper: Application Of Analogical Modelling To Example Based Machine Translation

ACL ID C00-1075
Title Application Of Analogical Modelling To Example Based Machine Translation
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2000
Authors

This paper describes a self-modelling, incremental algorithm for learning translation rules from existing bilingual corpora. The notions of supracontext and subcontext are extended to encompass bilingual information through simultaneous analogy on both source and target sentences and juxtaposition of corresponding results. Analogical modelling is performed during the learning phase and translation patterns are projected in a multi-dimensional analogical network. The proposed fi'amework was evaluated on a small training corpus providing promising results. Suggestions to improve system performance are 1.