Input Specification in the WAG Sentence Generation System

This paper describes the input specification language of the WAG Sentence Generation system. The input is described in terms of Halliday's (1978) three meaning components, ideational meaning (the propositional content to be expressed), interactional meaning (what the speaker intends the listener to do in making the utterance), and textual meaning (how the content is structured as a message, in terms of theme, reference, etc.). 1 I n t r o d u c t i o n This paper describes the input specification language of the WAG Sentence Generation system. The input is described in terms of Halliday's (1978) three meaning components, ideational meaning (the propositional content to be expressed), interactional meaning (what the speaker intends the listener to do in making the utterance), and textual meaning (how the ideational content is structured as a message, in terms of theme, reference, etc.). One motivation for this paper is the lack of descriptions of input-specifications for sentence generators. I have been asked at various times to fill this gap. Perhaps a better motivation is the need to argue for a more abstract level of input. Many of the available sentence generators require specification of syntactic information within the input specification. This means that any text-planner which uses this system as its realisation module needs to concern itself with these fiddling details. One of the aims in the WAG system has been to lift the abstractness of sentence specification to a semantic level. This paper discusses this representation. The WAG Sentence Generation System is one component of the Workbench for Analysis and Generation (WAG), a system which offers various tools for developing Systemic resources (grammars, semantics, lexicons, etc.), maintaining these resources (lexical acquisition tools, network graphers, hypertext browsers, etc.), and processing (sentence analysis O'Donnell 1993, 1994; sentence generation O'Donnell 1995b; knowledge representation O'Donnell 1994; corpus tagging and explorat i o n O'Donnell 1995a). The Sentence Generation component of this system generates single sentences from a semantic input. This semantic input could be supplied by a human user. Alternatively, the semantic input can be generated as the output of a multi-sentential text generation system, allowing such a system to use the WAG system as its realisation component. The sentence generator can thus be treated a blackbox unit. Taking this approach, the designer of the multi-sentential generation system can focus on multi-sentential concerns without worrying about sentential issues. WAG improves on earlier sentence generators in various ways. Firstly, it provides a more abstract level of input than many other systems (Mumble: McDonald 1980; Meteer et al. 1987; FUF: Elhadad 1991), as will be demonstrated throughout this paper. The abstractness improves even over the nearest comparable system, Penman (Mann 1983; Mann 8z Matthiessen 1985), in its treatment of textual information (see below). Other sentence generators, while working from abstract semantic specifications, do not represent a generalised realiser, but are somewhat domain specific in implementation, e.g., Proteus (Davey 1974/1978); Slang (Patten 1988). Other systems do not allow generation independent from user interaction, for instance, Genesys (Faw-