Meaning Representation For Knowledge Sharing In Practical Machine Translation

Knowledge-based machine translation can be viewed as the problem of extracting and representing the meaning of a text and generating a translation in a target language using the meaning representation. Meaning extraction requires the integration of information present explicitly in a text with common sense and domain knowledge given to the system. Thus, integrating linguistic knowledge of each language with general world knowledge is a central problem in machine translation, especially when more than two languages are involved. In this article we consider the design of a meaning representation that enables language-specific lexicons to share knowledge with a language-independent world model. We illustrate how the underlying core meaning representation can be enhanced in three different ways to arrive at lexical, ontological, and text meaning representations. The meaning representations presented here have been implemented in the Mikrokosmos machine translation system and used to repres..