On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems
This addresses the challenge of optimizing dialogue policies in real-world applications where user intent is unknown and pre-training data is unavailable, though it is incremental as it builds on existing active learning and reinforcement learning methods.
The paper tackled the problem of unreliable and costly user feedback for reward learning in spoken dialogue systems by proposing an on-line active learning framework that jointly trains the dialogue policy and reward model using a Gaussian process on unsupervised dialogue representations, resulting in significantly reduced data annotation costs and mitigated noisy feedback.
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.