Using Discourse Predictions for Ambiguity Resolution

Using Discourse Predictions for Ambiguity Resolution Yan Qu, Carolyn P. Ros6 and Barbara Di Eugenio Computational Linguistics Program Department of Philosophy Carnegie Mellon University Pittsburgh, PA 15213 {yqu,cprose} (6) cs.cmu.edu,dieugeni~cmu.cdu Abstract In this paper we discuss how we all- ply discourse predictions along with non context-based predictions to the prob- lem of parse disambiguation in Enthusi- ast, a Spanish-to-English translation sys- tem (Woszcyna et al., 1993; Snhm et al., 1994; Levin el; al., 1995). We discuss extensions to our plan-based discourse processor in order to make this possi- ble. We evaluate those extensions and demonstrate the advantage of exploiting context-based predictions over a purely non context-based approach. 1 Introduction A system which processes spoken language must address all of the ambiguities arising when pro- cessing written language, plus other ambiguities specitie to the speech processing task. These include ambiguities derived from speech disflu- encies, speech recognition errors, and the lack of clearly marked sentence boundaries. Because a large flexible grammar is necessary to handle these features of spoken language, as a side-effect the number of ambiguities increases. In this pa- per, we discuss how we apply discourse predic- tions along with non context-based predictions to the problem of parse disambiguation. This work has been carried out in the context of Enthusi- ast, a Spanish-to-English speech-to-speech trans- lation system (Woszcyna et al., 1993; Suhm et al., 1994; Levin et al., 1995), which currently trans- lates spontaneous dialogues between two people trying to schedule a meeting time. A key feature of our approach is that it al- lows multiple hypotheses to be processed through the system in parallel, and uses context to disam- biguate among alternatives in the linal stage of the process, where knowledge can be exploited to the fullest extent. In our system, numerical predic- tions based on the more local utterance level are generated by tile parser. The larger discourse con- text is processed and maintained by a plan-based discourse processor, which also produces context- based predictions for ambiguities. Our goal was to combine the predictions from the context-based discourse processing approach with those from the non context-based parser approach. In developing our discourse processor for disam- biguation we needed to address three major issues. First, most plan-based or finite state automaton based discourse processors (Allen and Schubert, 1991; Smith, Hipp, and Biermann, 1995; Lam- bert, 1993; Reithinger and Maim:, 1995), including tile one we initially developed (l~.osd et al., 1995), only take one semantic representation as input at a time: thus, we had to extend the discourse pro- cessor so thai; it can handle multiple hypotheses as input. Secondly, we needed to quantify the disam- biguating predictions made by the plan-based dis- course processor in order to combine these predic- tions with the non context-based ones. Finally, we needed a method for combining context-based and non context-based predictions in such a way as to reflect not only which factors are important, but also to what extent they are important, and under what circumstances. We assume that knowledge from …

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