Machine translation using recursive chain-link-type learning based on translation examples

Rule-based machine translation analyzes source-language sentences using large-scale linguistic knowledge that is given by the developer beforehand. However, it is difficult to give complete linguistic knowledge to the system ex ante because natural language has various linguistic phenomena. Therefore, we worked to develop learning-based machine translation. In learning-based machine translation, a system acquires translation rules automatically from translation examples that are pairs of source and target language sentences. However, existing learning-based machine translation presents the problem that it requires a large number of similar translation examples. Consequently, it cannot acquire enough useful translation rules from sparse translation examples. This paper proposes a method of machine translation using Recursive Chain-Link-type Learning, which can acquire many useful translation rules from sparse translation examples. Our system, based on this method, efficiently acquires translation rules from each translation example without requiring two similar translation examples. Translation rules are acquired by extracting corresponding parts between source and target language sentences in translation examples. Our system determines those corresponding parts using previously acquired translation rules. Therefore, the system engenders a chain reaction in acquisition of new translation rules. Evaluation experiments using our system demonstrated an effective translation rate of 61.1%. Moreover, the effective translation rate was 85.0% when sufficient learning data were given to our system. © 2004 Wiley Periodicals, Inc. Syst Comp Jpn, 35(2): 1–15, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10511