Combining Hierarchical Reinforcement Learning and Bayesian Networks for Natural Language Generation in Situated Dialogue
Nina Dethlefs, Heriberto Cuayáhuitl · 2011
Language generators in situated domains face a number of content selection, utterance planning and surface realisation decisions, which can be strictly interdependent. We therefore propose to optimise these processes in a joint fashion using Hierarchical Reinforcement Learning. To this end, we induce a reward function for content selection and utterance planning from data using the PARADISE framework, and suggest a novel method for inducing a reward function for surface realisation from corpora. It is based on generation spaces represented as Bayesian Networks. Results in terms of task success and humanlikeness suggest that our unified approach performs better than a baseline optimised in isolation or a greedy or random baseline. It receives human ratings close to human authors. 1