DFKI-LT - Optimising Natural Language Generation Decision Making For Situated Dialogue
Optimising Natural Language Generation Decision Making For Situated Dialogue
Proceedings of the Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL),
Natural language generators are faced with a multitude of different decisions during their generation process. We address the joint optimisation of navigation strategies and referring expressions in a situated setting with respect to task success and human-likeness. To this end, we present a novel, comprehensive framework that combines supervised learning, Hierarchical Reinforcement Learning and a hierarchical Information State. A human evaluation shows that our learnt instructions are rated similar to human instructions, and significantly better than the supervised learning baseline.
Files: BibTeX, hc-sigdial2011.pdf