Speaker Attitudes in Text Planning

Natural language generation needs an input language whose expressive power is sufficient for generating texts with the level of quality desired by various NLP applications. In oar generator, DIOGENES(e.g., Nirenburg et al., 1989), we use the text meaning representation language TAMERLAN(Nirenburg and Defrise, 1989 and forthcoming). Expressions in this language are used as input by the DIOGENES text planner to produce text plan expressions in the text plan language, TPL, that in their turn serve as input to syntactic realization. In this paper we describe the treatment of one of the several types of knowledge encoded in TAMERLAN, namely, speaker attitudes. We aLso illustrate how these input components are used in producing text plans. I. I n t r o d u c t i o n Our reasons for introducing attitudes as an explicit part of the representation of the meaning of a natural language clause are manifold. In what follows we will review three (partially interconnected) reasons. Representing attitudes a) helps reasoning about speaker goals, b) highlights the argumentative structure of a discourse and c) provides a convenient vehicle for representing modal meanings, including negation. Almost all spoken and written discourse involves the participants' opinions, so much so that producing a perfectly 'objective' text is an almost impossible task. Within the set of possible goals relating to generating text, the introduction (explicit or implicit, lexicalized or not) of the producer's opinions and points of view serves two goals: • modifying the consumer's model of the producer by stating facts (including opinions) about self which are not in principle observable by the consumer • modifying the consumer's opinions by stating producer's opinions about facts of the world (the latter can in principle be observed by the consumer) The above distinctions only become visible if one decides to represent attitudes overtly. Once this decision is made, it becomes clear that it brings about better description possibilities for additional linguistic phenomena, such as the argumentative structure of discourse. It has been observed (e.g., Anscombre and Ducrot, 1983) that texts have a well-defined argumentative structure which reflects the producer's current goals and influences such processes as the ordering of text components and lexical selection in generation. The argumentative structure of a text is realized (or, in text understanding, detected) through linguistic means such as the use of scalar adverbs ( 'only' , 'even', 'almost', 'hardly', etc.), connectives ( 'but' , 'since'), adjectives ('unbearable', 'fascinating', etc.). Sets of such lexical items may have to be considered equivalent from a purely semantic point of view, but different in a facet of their pragmatic effect known as argumentative orientation. For example, to illustrate the interplay between semantic content and argumentative orientation (i.e. the producer's attitude towards an event), contrast (1) and (2), which have opposite truth conditions, but the same pragmatic v a lu e from both (1) and (2) the consumer will infer that the producer regards Burma as an inefficient sleuth. In this example it is sufficient to retain pragmatic information concerning the producer's judgment of Burma while the semantic differences (induced by the use of "few" versus "none at all") can be disregarded. However, in other contexts the semantics will matter much more consider, for instance, (3) for which there can be no paraphrase with "no clues at all." (1) Nestor Burma found few clues. Nobody was surprised. (2) Nestor Burma found no clues at all. Nobody was surprised. (3) Nestor Burma found few clues. But it was still better than having none at all. The difference between (4) and (5), whose truth conditions are similar, is purely argumentative (or attidudinal) (4) expresses a positive (optimistic!) attitude, (5) the opposite point of view. This example shows how crucial theextraction of the argumentative structure is, since it is the only clue for the inacceptability of (6). (4) Nestor has a little money.