A 2-LEVEL KNOWLEDGE REPRESENTATION FOR MACHINE TRANSLATION - LEXICAL SEMANTICS AND TENSE ASPECT

This paper proposes a two-level model that integrates contemporary theories of tense and aspect with lexical semantics. The model is intended to be extensible to realms outside of the temporal domain (e.g., the spatial domain). The integration of tense and aspect with lexical-semantics is especially critical in machine translation because of the lexical selection process during generation: there is often a number of lexical connective and tense/aspect possibilities that may be produced from a lexical semantic representation, which, as defined in the model presented here, is largely underspecified. Temporal/aspectual information from the source-language sentence constrains the choice of target-language terms. In turn, the target-language terms limit the possibilities for generation of tense and aspect. Thus, there is a two-way communication channel between the two processes.