Machine translation: how far can it go?
Machine translation: how far can it go?
A statistical approach to machine translation
Computational Linguistics
Learning translation templates from examples
Information Systems - Special issue on selected papers from 6th annual workshop on information technologies and systems, December 1996, Cleveland, Ohio, USA
Toward memory-based translation
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 3
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In this paper I elaborate a model of competence for corpus-based machine translation (CBMT) along the lines of the representations used in the translation system. Representations in CBMT-systems can be rich or austere, molecular or holistic and they can be fine-grained or coarse-grained. The paper shows that different CBMT architectures are required dependent on whether a better translation quality or a broader coverage is preferred according to Boitet (1999)'s formula: "Coverage * Quality = K".