Experiences with planning for natural language generation

Natural language generation (NLG) is a major subfield of computational linguistics with a long tradition as an application area of automated planning systems. While things were relatively quiet with the planning approach to NLG for a while, several recent publications have sparked a renewed interest in this area. In this paper, we investigate the extent to which these new NLG approaches profit from the advances in planner expressiveness and efficiency. Our findings are mixed. While modern planners can readily handle the search problems that arise in our NLG experiments, their overall runtime is often dominated by the grounding step they perform as preprocessing. Furthermore, small changes in the structure of a domain can significantly shift the balance between search and preprocessing. Overall, our experiments show that the off-the-shelf planners we tested are unusably slow for nontrivial NLG problem instances. As a result, we offer our domains and experiences as challenges for the planning community.