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In a language generation system, a content planner embodies one or more “plans” that are usually hand–crafted, sometimes through manual analysis of target text. In this paper, we present a system that we developed to automatically learn elements of a plan and the ordering constraints among them. As training data, we use semantically annotated transcripts of domain experts performing the task our system is designed to mimic. Given the large degree of variation in the spoken language of the transcripts, we developed a novel algorithm to find parallels between transcripts based on techniques used in computational genomics. . | Empirically Estimating Order Constraints for Content Planning in Generation Pablo A. Duboue and Kathleen R. McKeown Computer Science Department Columbia University 10027 New York NY UsA pablo kathy @cs.columbia.edu Abstract In a language generation system a content planner embodies one or more plans that are usually hand-crafted sometimes through manual analysis of target text. In this paper we present a system that we developed to automatically learn elements of a plan and the ordering constraints among them. As training data we use semantically annotated transcripts of domain experts performing the task our system is designed to mimic. Given the large degree of variation in the spoken language of the transcripts we developed a novel algorithm to find parallels between transcripts based on techniques used in computational genomics. Our proposed methodology was evaluated two-fold the learning and generalization capabilities were quantitatively evaluated using cross validation obtaining a level of accuracy of 89 . A qualitative evaluation is also provided. 1 Introduction In a language generation system a content planner typically uses one or more plans to represent the content to be included in the output and the ordering between content elements. Some researchers rely on generic planners e.g. Dale 1988 for this task while others use plans based on Rhetorical Structure Theory RST e.g. Bouayad-Aga et al. 2000 Moore and Paris 1993 Hovy 1993 or schemas e.g. McKeown 1985 McKeown et al. 1997 . In all cases constraints on application of rules e.g. plan operators which determine content and order are usually hand-crafted sometimes through manual analysis of target text. In this paper we present a method for learning the basic patterns contained within a plan and the ordering among them. As training data we use semantically tagged transcripts of domain experts performing the task our system is designed to mimic an oral briefing of patient status after undergoing coronary .
