DFKI-LT - Combining Hierarchical Reinforcement Learning and Bayesian Networks for Natural Language Generation in Situated Dialogue
Combining Hierarchical Reinforcement Learning and Bayesian Networks for Natural Language Generation in Situated Dialogue
1 European Workshop on Natural Language Generation (ENLG), Nancy, France, ACL-SIGGEN, 9/2011
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 human-likeness 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.
Files: BibTeX, hc-enlg2011.pdf