Logic-Based Rhetorical Structuring for Natural Language Generation in Human-Computer Dialogue

Rhetorical structuring is field approached mostly by research in natural language (pragmatic) interpretation. However, in natural language generation (NLG) the rhetorical structure plays an important part, in monologues and dialogues as well. Hence, several approaches in this direction exist. In most of these, the rhetorical structure is calculated and built in the framework of Rhetorical Structure Theory (RST), or Centering Theory [7], [5]. In language interpretation, a more recent formal account of rhetorical structuring has emerged, namely Segmented Discourse Representation Theory (SDRT), which alleviates some of the issues and weaknesses inherent in previous theories [1]. Research has been initiated in rhetorical structuring for NLG using SDRT, mostly concerning monologues [3]. Most of the approaches in using and / or approximating SDRT in computer implementations lean on dynamic semantics, derived from Discourse Representation Theory (DRT) in order to compute rhetorical relations [9]. Some efforts exist in approximating SDRT using less expressive (and expensive) logics, such as First Order Logic (FOL) or Dynamic Predicate Logic (DPL), but these efforts concern language interpretation [10]. This paper describes a rhetorical structuring component of a natural language generator for human-computer dialogue, using SDRT, approximated via the usage of FOL, doubled by a domain-independent discourse ontology. Thus, the paper is structured as follows: the first section situates the research in context and motivates the approach; the second section describes the discourse ontology; the third section describes the approximations done on vanilla SDRT, in order for it to be used for language generation purposes; the fourth section describes an algorithm for updating the discourse structure for a current dialogue; the fifth section provides a detailed example of rhetorical relation computation. The sixth section concludes the paper and gives pointers to future research and improvements.