Application of analogical modelling to example based machine translation
Christos Malavazos, Stelios Piperidis · 2000
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 framework was evaluated on a small training corpus providing promising results. Suggestions to improve system performance are this kind of analysis unquestionably leads to more computationally expensive and difficult to obtain systems. Our approach consists in a fully modular analogical framework, which can cope with lack of resources, and will perform even better when these are available.