Example-Based Machine Translation Without Saying Inferable Predicate

For natural translations, a human being does not express predicates that are inferable from the context in a target language. This paper proposes a method of machine translation which handles these predicates. First, to investigate how to translate them, we build a corpus in which predicate correspondences are annotated manually. Then, we observe the corpus, and find alignment patterns including these predicates. In our experimental results, the machine translation system using the patterns demonstrated the basic feasibility of our approach.