Using Reinforcement Learning to Model Incrementality in a Fast-Paced Dialogue Game (bibtex)
by Ramesh Manuvinakurike, David DeVault, Kallirroi Georgila
Abstract:
We apply Reinforcement Learning (RL) to the problem of incremental dialogue policy learning in the context of a fast-paced dialogue game. We compare the policy learned by RL with a high performance baseline policy which has been shown to perform very efficiently (nearly as well as humans) in this dialogue game. The RL policy outperforms the baseline policy in offline simulations (based on real user data). We provide a detailed comparison of the RL policy and the baseline policy, including information about how much effort and time it took to develop each one of them. We also highlight the cases where the RL policy performs better, and show that understanding the RL policy can provide valuable insights which can inform the creation of an even better rule-based policy.
Reference:
Using Reinforcement Learning to Model Incrementality in a Fast-Paced Dialogue Game (Ramesh Manuvinakurike, David DeVault, Kallirroi Georgila), In Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue, SIGDIAL, 2017.
Bibtex Entry:
@inproceedings{manuvinakurike_using_2017,
	address = {Saarbruecken Germany},
	title = {Using {Reinforcement} {Learning} to {Model} {Incrementality} in a {Fast}-{Paced} {Dialogue} {Game}},
	url = {http://www.manuvinakurike.com/papers/eve-2017.pdf},
	abstract = {We apply Reinforcement Learning (RL) to the problem of incremental dialogue policy learning in the context of a fast-paced dialogue game. We compare the policy learned by RL with a high performance baseline policy which has been shown to perform very efficiently (nearly as well as humans) in this dialogue game. The RL policy outperforms the baseline policy in offline simulations (based on real user data). We provide a detailed comparison of the RL policy and the baseline policy, including information about how much effort and time it took to develop each one of them. We also highlight the cases where the RL policy performs better, and show that understanding the RL policy can provide valuable insights which can inform the creation of an even better rule-based policy.},
	booktitle = {Proceedings of the 18th {Annual} {SIGdial} {Meeting} on {Discourse} and {Dialogue}},
	publisher = {SIGDIAL},
	author = {Manuvinakurike, Ramesh and DeVault, David and Georgila, Kallirroi},
	month = aug,
	year = {2017},
	keywords = {UARC, Virtual Humans}
}
Powered by bibtexbrowser