In this paper, we present a novel technique to learn a tree-like structure for a content planner from an aligned corpus of semantic inputs and corresponding, humanproduced, outputs. We apply a stochastic search mechanism with a two-level fitness function. As a first stage, we use high level order constraints to quickly discard unpromising planners. As a second stage, alignments between regenerated text and human output are employed. We evaluate our approach by using the existing symbolic planner in our system as a gold standard, obtaining a 66% improvement over a random baseline in just 20 generations of genetic search.
REF learn a tree-like planner from an aligned corpus of semantic inputs and corresponding human-authored outputs using evolutionary al-gorithms.
Content Planner Construction via Evolutionary Algorithms and a Corpus-Based Fitness Function
In Proceedings of the 2 nd International Natural Language Generation Conference (INLG'02
Computer Science