Handling Pragmatic Information With A Reversible Architecture

Handling Pragmatic Information With A Reversible Architecture Masato Ishizaki NTT Communications and Information Processing Laboratones 1-2356, Take, Yokosuka, Kanagawa, 238-03 Japan E-mail: ishizaki%nttnly.ntt.jp@relay.cs.net Abstract This paper propo~s a reversible architecture to handle not orily syntactic and semantic information but also pragmatic information. • I Existing architectures cannot represent prag- matic informatioil explicitly, and lack rea- soning capability;given insufficient informa- tion. I argue that the techniques of plan rep- resentation and approximate reasoning are, in the enhanced argumentation system pro- posed here, effective for solving these prob- lems. 1. Introduction Reversibility or bi-directionality of grammars seems to play a quite important role in natural language procesSing. It reduces the cost of constructing a grammar; we need to use only one grammar instead of two for parsing and generation. Cost ~here includes not only the making of grammar rules but also verifying the rules and the algorithms for parsing and generation. Reversibility differs from bi- directionality: the former requires the same mechanism and 'grammar for parsing and generation; the latter requires just the same grammar as shown Figure l(Noord, 1990). Pragmatic in:formation is not rigidly defined; rather it is thought of as information other than syntaqtic and semantic informa- tion. It is indispetlsable for explaining many linguistic pheno/nena from cleft sentences to discourse structures. As pragmatics cannot restrain language in a strict manner like syn- tax, it must be processed differently; that is, a distinction must be made between con- straints that need to be fully satisfied and those that do not.'Plan representation seems to be appropriate for collecting different level information. A plan consists of precon ditions, constraints, plan expansion (usually termed the body), and effects. The relation- ship between preconditions and constraints parallels that between pragmatic and syntactic information. Thus, the difference between preconditions and constraints can be easily modeled. Handling pragmatic information clearly depends on assumption of belief: Generating referring expressions requires inferencing the hearer's belief (Appelt, 1985); Producing text requires the usage of a one-sided mutual beliefl(Moore et a1.,1989); the listener's inference about the speaker's belief greatly helps to resolve anaphora or to analyze the speaker's intention. In any case, belief be- comes a condition for further inference; how- ever, it is difficult if not impossible to confirm the assumed belief. Thus, a new mechanism based on a new architecture is needed. Ap- proximate reasoning (Elkan,1990) is stat- able for this purpose. Processing can con- "tinue, even if some preconditions are not fully satisfied; they are held as assump- tions 2. This approach seems to be very natural. For example, in conversations, the speaker should conceptualize the listener's 1One-sided mutual belief is one half of mutual knowledge, so to speak, namely the set of those pieces of mutual knowledge that constitute the knowledge of one speaker (Bunt, 1989: 60). 2Since approximate reasoning can fail, assumptions must be held explicitly for further in- ference. Plan representation is adequate for that reason. 119 Strings Stri0gs Analyzer Forms Figure 1. Reversible And Bi-directional Architectures. understanding. In most conversations, how- ever, the …

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