Exploiting Conversational Implicature for Generating Concise Explanations

EXPLOITING CONVERSATIONAL IMPLICATURE FOR GENERATING CONCISE EXPLANATIONS HELMUT HORACEK Universit~t Bielefeld Fakultlit f'dr Linguistik und Literaturwissenschaft Postfach 8640, D-4800 Bielefeld 1, Deutschland ABSTRACT This paper presents an approach for achieving conciseness in generating explanations, which is clone by exploiting formal reconstructions of aspects of the Gricean principle of relevance to simulate conversational implicature. By apply- ing contextually motivated inference rules in an anticipation feed-back loop, a set of propo- sitions explicitly representing an explanation's content is reduced to a subset which, in the actual context, can still be considered to convey the message adequately. 1. INTRODUCTION The task of providing informative natural language explanations for illustrating the results produced by decision support systems has been gtven increased attention recently. The pro- posed methods preferably address tailoring of explanations to the needs of their addressees, including, for instance, object descriptions [8] and presentation of taxonomic knowledge [7]. In addition, particular emphasis has been put on reactive explanation techniques for selecting an appropriate content according to contextual interpretation [6], and on the way of presenting explanations by taking the information Seeking person's knowledge into account [1]. Whereas these approaches attack various issues important for the generation of natural language explanations, none of them has focussed on the conciseness of explanations in a broader con- text. Aiming at the production of natural and concise texts, we have concentrated our efforts on presenting different types of knowledge and their interrelations because this kind of infor- mation is typically relevant for explanations. We formally reconstruct aspects of the Gricean principle of relevance [3] and exploit the results obtained for creating concise explanations to questions about solutions proposed by the ex- pert system OFFICE-PLAN [5]. This system is able to appropriately assign a set of employees to a set of rooms in offices, which is guided by a number of constraints expressing various kinds of the persons" requirements. 2. REPRESENTING DOMAIN AND INFERENCE KNOWLEDGE Terminological knowledge is represented in a sorted type hierarchy, which identifies classes of entities and their relevant subsorts, as well as relations that may hold between two types of entities. Moreover, assertions which refer to the referential level must be consistent with the on- tology provided by these taxonomic definitions. Inferential knowledge is represented in terms of rules which express constraints to be satisfied in the problem solving process. Rules are represented according to the syntax of IRS [2], which is loosely based on predicate logic. The quantifiers used in our system are all, some, and unique. The predications contained are re- stricted to be one- or two-place predications corresponding to class and relation definitions introduced in the taxonomic hierarchy. In addi- tion, the recta-predicate implies is contained in the innermost predication of a rule, which con- stitutes the rule's conclusion (see Figure 1). The original representation of an explanation to a certain question consists of a set of propo- sitions (created by the preceeding component in the generation process [4]) which includes inference rules and individual facts that comple- tely identify …

Content truncated to 500 words