A Two-Level Knowledge Representation for Machine Translation: Lexical Semantics and Tense/Aspect

This paper proposes a two-level model that integrates tense and aspect information, based on theories by both Hornstein (in the spirit of Reichenbach) and Allen, with lexicai-semantic information based on an extended version of $ackendoff's theory that includes a verb classification system proposed by Dowty and Vendler. 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. The use of tense and aspect information allows the choice of target-language terms to be more finely tuned and the combination of event structures to be more carefully constrained.