Towards meaning-based machine translation: using abstractions from text generation for preserving meaning

Abstract

One of the primary motivations of text generation is the achievement of a very wide range of linguistic abilities coupled with functional control of that range. This control rests on the appropriate construction of abstract specifications of meaning that can guide the generation process to produce language that is textually, grammatically, and lexically appropriate. Such abstract semantic specifications, when constructed in the right way, preserve much of the meaning required in a translation without unduly constraining syntactic form. This is potentially of great value for machine translation since it opens up the possibility of domain-independent, constrained, meaning-based translation. This paper describes how the upper model of the PENMAN text generation system provides a level of semantic abstraction of this kind. It offers examples of the motivation of broader sets of likely translational equivalents than that possible with transfers at lower-levels of abstraction and sets out types of constraints by which the set of likely translational equivalents may be reduced to high-quality renderings of the source text.