A Hybrid Approach to Machine Translation System Design

Abstract

It is difficult for pure statistics-based machine translation systems to process long sentences. In addition, the domain dependent problem is a key issue under such a framework. Pure rule-based machine translation systems have many human costs in formulating rules and introduce inconsistencies when the number of rules increases. Integration of these two approaches reduces the difficulties associated with both. In this paper, an integrated model for machine translation system is proposed. A partial parsing method is adopted, and the translation process is performed chunk by chunk. In the synthesis module, the word order is locally rearranged within chunks via the Markov model. Since the length of a chunk is much shorter than that of a sentence, the disadvantage of the Markov model in dealing with long distance phenomena is greatly reduced. Structural transfer is fulfilled using a set of rules; in contrast, lexical transfer is resolved using bilingual constraints. Qualitative and quantitative knowledge is employed interleavingly and cooperatively, so that the advantages of these two approaches can be retained.