A Model of Competence for Corpus-Based Machine Translation
نویسنده
چکیده
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 ne-grained or coarse-grained. The paper shows that di erent 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".
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