Empirically Estimating Order Constraints for Content Planning in Generation

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.