A Study of Performance Evaluation for GA-ILMT Using Travel English

Recently, many machine translation systems have been developed. However, for translation of conversation, correct translation rates and quality of translation are particularly low. This is due to machine translation systems not being able to generate translation results which fit the context of the conversation . We previously proposed a method of Machine Translation Using Inductive Learning with Genetic Algorithms(GA-ILMT). We compare this system's results to two others that use rule-based translation method, and evaluate the results of experiments done with GA-ILMT, measuring it's performance when applied to travel English. As a result of the evaluation experiments, we confirmed that GA-ILMT can generate translation results which are more appropriate to the context of the conversation.