A Parameterized Approach to Integrating Aspect With Lexical-Semantics for Machine Translation

A PARAMETERIZED APPROACH TO INTEGRATING ASPECT WITH LEXICAL-SEMANTICS FOR MACHINE TRANSLATION Bonnie J. Dorr* Institute for Advanced Computer Studies A.V. Williams Building University of Maryland College Park, MD 20742 bonnie@umiacs.umd.edu ABSTRACT This paper discusses how a two-level knowledge rep- resentation model for machine translation integrates as- pectual information with lexical-semantic information by means of parameterization. The integration of aspect with lexical-semantics is especially critical in machine translation because of the lexical selection and aspec- tual realization processes that operate during the pro- duction of the target-language sentence: there are of- ten a large number of lexical and aspectual possibili- ties to choose from in the production of a sentence from a lexical semantic representation. Aspectual informa- tion from the source-language sentence constrains the choice of target-language terms. In turn, the target- language terms limit the possibilities for generation of aspect. Thus, there is a two-way communication chan- nel between the two processes. This paper will show that the selection/realization processes may be parame- terized so that they operate uniformly across more than one language and it will describe how the parameter- based approach is currently being used as the basis for extraction of aspectual information from corpora. INTRODUCTION This paper discusses how the two-level knowledge representation model for machine translation presented by Dorr (1991) integrates aspectual information with lexical-semantic information by means of parameteriza- tion. The parameter-based approach borrows certain ideas from previous work such as the lexical-semantic model of Jackendoff (1983, 1990) and models of as- pectual representation including Bach (1986), Comrie (1976), Dowty (1979), Mourelatos (1981), Passonneau (1988), Pustejovsky (1988, 1989, 1991), and Vendler (1967). However, unlike previous work, the current approach examines aspectual considerations within the context of machine translation. More recently, Bennett *This paper describes research done in the Institute for Advanced Computer Studies at the University of Maryland. A special thanks goes to Terry Gaasterland and Ki Lee for helping to close the gap between properties of aspectual in- formation and properties of lexical-semantic structure. In addition, useful guidance and commentary during this re- search were provided by Bruce Dawson, Michael Herweg, Jorge Lobo, Paola Merlo, Norbert Hornstein, Patrick Saint- Dizier, Clare Voss, and Amy Weinberg. (1) Syntactic: (a) Null Subject divergence: E: I have seen Mary 4. S: He vlsto a Marls (Have seen (to) Mary) (b) Constituent Order divergence, E: I have seen Mary 4. G: Ich habe Marie gesehen (I have Mar~" seen) (2) Lexicel-Semantic: (a) Thematic divergence: E: I like Mary 4. $: Marls me gusts a mf (Mary pleases me) (b) Structural divergence: E: John entered the house 4. S: Juan entr6 en la cas& (John entered in the house) (c) Cat esorlal divergence: E: Yo ten~o hambre 4* S: Ich habe Hun~er (I have hun~er) (3) Aepectuah (a) lterative Divergence: E: John stabbed Mary 4. S: Juan le dio una puflaJada a Marls (John gave a knife-wound to Mary) S: Juan le dio pufialadas a Marls (John gave knife-wounds to Mary) (b) Duratlve Divergence, E: John met/knew Mary 4* …

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