Computational Interpretations of the Gricean Maxims in the Generation of Referring Expressions

TL;DR

This paper explores computational approaches to generating referring expressions that are both accurate and pragmatically appropriate, emphasizing simplicity and alignment with human conversational behavior.

Contribution

It proposes a simple, computationally efficient algorithm for generating referring expressions based on Gricean maxims, with implementation details for natural language generation systems.

Findings

The simplest interpretation of Gricean maxims aligns closely with human speaker behavior.

The proposed algorithm effectively generates appropriate referring expressions.

Implementation in the IDAS system demonstrates practical applicability.

Abstract

We examine the problem of generating definite noun phrases that are appropriate referring expressions; i.e, noun phrases that (1) successfully identify the intended referent to the hearer whilst (2) not conveying to her any false conversational implicatures (Grice, 1975). We review several possible computational interpretations of the conversational implicature maxims, with different computational costs, and argue that the simplest may be the best, because it seems to be closest to what human speakers do. We describe our recommended algorithm in detail, along with a specification of the resources a host system must provide in order to make use of the algorithm, and an implementation used in the natural language generation component of the IDAS system. This paper will appear in the the April--June 1995 issue of Cognitive Science, and is made available on cmp-lg with the permission of Ablex, the publishers of that journal.