@inproceedings{bohus2006online,
author = {Bohus, Dan and Langner, Brian and Raux, Antoine and Black, Alan and Eskenazi, Maxine and Rudnicky, Alex},
title = {Online Supervised Learning of Non-understanding Recovery Policies},
booktitle = {IEEE/ACL 2006 Workshop on Spoken Language Technology},
year = {2006},
month = {December},
abstract = {Spoken dialog systems typically use a limited number of nonunderstanding recovery strategies and simple heuristic policies to engage them (e.g. first ask user to repeat, then give help, then transfer to an operator). We propose a supervised, online method for learning a non-understanding recovery policy over a large set of recovery strategies. The approach consists of two steps: first, we construct runtime estimates for the likelihood of success of each recovery strategy, and then we use these estimates to construct a policy. An experiment with a publicly available spoken dialog system shows that the learned policy produced a 12.5% relative improvement in the non-understanding recovery rate.},
url = {http://approjects.co.za/?big=en-us/research/publication/online-supervised-learning-non-understanding-recovery-policies/},
edition = {IEEE/ACL 2006 Workshop on Spoken Language Technology},
}