Towards a Model of Competence for Corpus-Based Machine Translation

A translation is a conversion from a source language into a target language preserving the meaning. A huge number of techniques and computational approaches have been experimented in order to translate natural languages automatically, yet no satisfactory solution has been found. This paper examines approaches to corpus-based machine translation (CBMT). In CBMT, a set of reference example translations is given to the MT system. These are analyzed and compiled into the system's internal representation according to the theory of meaning the system implements. The representations, then, serve as a basis to translate new sentences. This paper discusses three main approaches in the CBMT paradigm: the memory-based approach (e.g. translation memories (TM)), the example-based approach (EBMT) and the statistical-based approach (SBMT). Concrete CBMT systems are discussed in light of the theory of meaning (preservation) they implement. This discussion, then leads to a model of competence for CBMT systems. The paper concludes that CBMT systems can be designed to achieve high reliability or broad coverage, though both seem to be mutually exclusive qualities. 1 Meaning Preservation in Machine