Hybrid model for Chinese-English machine translation

This paper presents a hybrid model integrating the example-based (EB) approach with the knowledge base methodology in support of Chinese-English machine translation (MT). The EB approach normally performs translation by referring to similar source sentences in the example base. It can be employed at different stages of MT including source language analysis, source to target transformation and target language generation. The proposed MT model brings together knowledge in two forms: rules and examples. This system is intended for domains that are understood well but not completely perfect. Expert and heuristic knowledge in the form of rules are incorporated to provide a skeletal method for solving problems. Examples are then used to flesh out the method by covering idiosyncrasies and special cases that are not anticipated by the rules. In addition to a reasonably accurate and efficient set of rules to serve as a starting point for problem solving, the system is provided with some specific similarity measurement for critical analysis of Chinese sentences. This is based on word grammatical features and integrated with some dynamic mechanism to improve flexibility and robustness during translation process.