Tool MKSF for acquiring super-function in Java

Abstract

We have paid attention to machine translation (MT) users for a few years, and found that most users prefer that the system is fast, inexpensive, easy to control and easy to update. This means that sometimes we do not need to aim the MT system at highly fluent translations. In fact, usually we cannot build an MT system which can achieve highly fluent translations. We propose a new MT paradigm called super-function based machine translation (SFBMT) to try to address the MT users' requests. SFBMT uses a super function (SF) to translate without thorough syntactic and semantic analysis as most MT systems usually do. The SF is a function that shows the correspondence between the original language sentence patterns and target language sentence patterns. One key point for building an SFBMT system is how to acquire the SF from the practical natural language phenomenon. We present a method for aiding the acquisition of SF from the parallel corpus, and describe the tool called MKSF. MKSF has been constructed in Java. An experimental MT system based on the proposed method has been built using Java and some translation experiments have been carried out.