DFKI-LT - Optimising Natural Language Generation Decision Making For Situated Dialogue

Nina Dethlefs, Heriberto Cuayahuitl
Optimising Natural Language Generation Decision Making For Situated Dialogue
1 Proceedings of the Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL), Pages 78-87, Portland, Oregon, USA, ACL, 7/2011
 
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