Pronominalization in Generated Discourse and Dialogue

Pronominalization in Generated Discourse and Dialogue Charles B. Callaway Istituto per la Ricerca Scientifica e Tecnologica (ITC-irst), Italy callaway@irst.itc.it James C. Lester Department of Computer Science North Carolina State University, USA lester@adm.csc.ncsu.edu Abstract Previous approaches to pronominalization have largely been theoretical rather than applied in nature. Frequently, such meth- ods are based on Centering Theory, which deals with the resolution of anaphoric pro- nouns. But it is not clear that complex the- oretical mechanisms, while having satis- fying explanatory power, are necessary for the actual generation of pronouns. We first illustrate examples of pronouns from vari- ous domains, describe a simple method for generating pronouns in an implemented multi-page generation system, and present an evaluation of its performance. 1 Introduction Pronominalization is an important element in the au- tomatic creation of multi-paragraph and multi-page texts using natural language generation (NLG). Au- thors routinely use pronouns in texts spanning all types of genres, such as newspaper copy, science fiction and even academic papers. Indeed, without pronouns, texts quickly become confusing as readers begin to pay more attention to the writing style than to the content that makes the text informative or en- joyable (Callaway and Lester, 2001a). Even worse, incorrect pronouns can lead readers to misinterpret the text or draw unsound inferences. Furthermore, current pronominalization strategies are ill-equipped to deal with the wide variety of reasons that pronouns are used in naturally occur- ring texts. Almost without exception, they focus on anaphoric pronouns as described in Focus/Centering Theory (Webber, 1979; Sidner, 1983; Grosz and Sid- ner, 1986; Walker, 1998), ignoring the multitude of other possible types. However, it is certainly true that authors make use of pronouns which are not mo- tivated by anaphoric reference. In addition, because such approaches are oriented towards anaphora resolution during parsing, they ig- nore structures such as the discourse plan which are present during generation but not parsing. A typi- cal discourse plan can include vital information for pronominalization such as time and clause bound- aries, ordering of propositions, and semantic de- tails verbal arguments. Current approaches based on Centering algorithms thus attempt to recreate a text coherence structure that duplicates work already done by the discourse planner. Finally, there are significant obstacles to verifying the correctness of existing pronominalization algo- rithms for any pronominalization theory (Not, 1996; Yeh and Mellish, 1997; McCoy and Strube, 1999; Henschel et al., 2000; Kibble and Power, 2000): the lack of natural language generation systems that can produce large enough texts to bring discourse-level processes into play. Because of this, researchers are forced to simulate by hand how their algorithms will work on a given text. It is also not sufficient to use template generation systems to perform this task be- cause they lack the low-level discourse representa- tion needed to provide the information upon which most algorithms base their decisions. In this paper we first summarize related work in both anaphora resolution and anaphora genera- tion. We next describe the range of pronoun types Computational Linguistics (ACL), Philadelphia, …

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