Paper: SPoT: A Trainable Sentence Planner

ACL ID N01-1003
Title SPoT: A Trainable Sentence Planner
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2001
Authors

Sentence planning is a set of inter-related but distinct tasks, one of which is sentence scoping, i.e. the choice of syntactic structure for elementary speech acts and the decision of how to combine them into one or more sentences. In this paper, we present SPoT, a sentence planner, and a new methodology for automatically train- ing SPoT on the basis of feedback provided by human judges. We reconceptualize the task into two distinct phases. First, a very simple, randomized sentence-plan- generator (SPG) generates a potentially large list of pos- sible sentence plans for a given text-plan input. Second, the sentence-plan-ranker (SPR) ranks the list of output sentence plans, and then selects the top-ranked plan. The SPR uses ranking rules automatically learned from train- ing data. We show t...