DFKI-LT - Hierarchical Reinforcement Learning and Hidden Markov Models for Task-Oriented Natural Language Generation
Hierarchical Reinforcement Learning and Hidden Markov Models for Task-Oriented Natural Language Generation
1 Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-HLT),
Surface realisation decisions in language generation can be sensitive to a language model, but also to decisions of content selection. We therefore propose the joint optimisation of content selection and surface realisation using Hierarchical Reinforcement Learning (HRL). To this end, we suggest a novel reward function that is induced from human data and is especially suited for surface realisation. It is based on a generation space in the form of a Hidden Markov Model (HMM). Results in terms of task success and human-likeness sug- gest that our unified approach performs better than greedy or random baselines.
Files: BibTeX, hc-acl-hlt2011.pdf