CUED Publications database

On-line active reward learning for policy optimisation in spoken dialogue systems

Su, PH and Gašić, M and Mrkšić, N and Rojas-Barahona, L and Ultes, S and Vandyke, D and Wen, TH and Young, S (2016) On-line active reward learning for policy optimisation in spoken dialogue systems. 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers, 4. pp. 2431-2441.

Full text not available from this repository.

Abstract

© 2016 Association for Computational Linguistics. The ability to compute an accurate reward function is essential for optimising a dialogue policy via reinforcement learning. In real-world applications, using explicit user feedback as the reward signal is often unreliable and costly to collect. This problem can be mitigated if the user's intent is known in advance or data is available to pre-train a task success predictor off-line. In practice neither of these apply for most real world applications. Here we propose an on-line learning framework whereby the dialogue policy is jointly trained alongside the reward model via active learning with a Gaussian process model. This Gaussian process operates on a continuous space dialogue representation generated in an unsupervised fashion using a recurrent neural network encoder-decoder. The experimental results demonstrate that the proposed framework is able to significantly reduce data annotation costs and mitigate noisy user feedback in dialogue policy learning.

Item Type: Article
Subjects: UNSPECIFIED
Divisions: Div F > Machine Intelligence
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:13
Last Modified: 21 Nov 2017 03:07
DOI: