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Natural Language Generation as Planning under Uncertainty Using Reinforcement Learning
[article]
2016
arXiv
pre-print
We present and evaluate a new model for Natural Language Generation (NLG) in Spoken Dialogue Systems, based on statistical planning, given noisy feedback from the current generation context (e.g. a user and a surface realiser). We study its use in a standard NLG problem: how to present information (in this case a set of search results) to users, given the complex trade- offs between utterance length, amount of information conveyed, and cognitive load. We set these trade-offs by analysing
arXiv:1606.04686v1
fatcat:ujcx27wfn5eotfloksqmwvxt3q