Publikation
Optimising Natural Language Generation Decision Making For Situated Dialogue
Nina Dethlefs; Dr. Heriberto Cuayáhuitl
In: Proceedings of the Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL). Annual SIGdial Meeting on Discourse and Dialogue (SIGdial-2011), 12th, June 17-18, Portland, Oregon, USA, Pages 78-87, ISBN 978-1-937284-10-7, ACL, 7/2011.
Zusammenfassung
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.