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.
Several approaches have been adopted for learning a dialogue reward model given a corpus of annotated dialogues. used collaborative filtering to infer user preferences. The use of reward shaping has also been investigated in @cite_44 @cite_22 to enrich the reward function in order to speed up dialogue policy learning. Also, demonstrated that there is a strong correlation between expert's user satisfaction ratings and dialogue success. However, all these methods assume the availability of reliable dialogue annotations such as expert ratings, which in practice are hard to obtain.
@cite_44 @cite_22
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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. HighlightsWe integrate user appraisals in a POMDP-based dialogue manager procedure.We employ additional socially-inspired rewards in a RL setup to guide the learning.A unified framework for speeding up the policy optimisation and user adaptation.We consider a potential-based reward shaping with a sample efficient RL algorithm.Evaluated using both user simulator (information retrieval) and user trials (HRI). This paper investigates some conditions under which polarized user appraisals gathered throughout the course of a vocal interaction between a machine and a human can be integrated in a reinforcement learning-based dialogue manager. More specifically, we discuss how this information can be cast into socially-inspired rewards for speeding up the policy optimisation for both efficient task completion and user adaptation in an online learning setting. For this purpose a potential-based reward shaping method is combined with a sample efficient reinforcement learning algorithm to offer a principled framework to cope with these potentially noisy interim rewards. The proposed scheme will greatly facilitate the system's development by allowing the designer to teach his system through explicit positive negative feedbacks given as hints about task progress, in the early stage of training. At a later stage, the approach will be used as a way to ease the adaptation of the dialogue policy to specific user profiles. Experiments carried out using a state-of-the-art goal-oriented dialogue management framework, the Hidden Information State (HIS), support our claims in two configurations: firstly, with a user simulator in the tourist information domain (and thus simulated appraisals), and secondly, in the context of man-robot dialogue with real user trials. We report evaluation results for real users of a learnt dialogue management policy versus a hand-coded policy in the TALK project's "Townlnfo" tourist information system. The learnt policy, for filling and confirming information slots, was derived from COMMUNICATOR (flight-booking) data using reinforcement learning (RL) as described in [2], ported to the tourist information domain (using a general method that we propose here), and tested using 18 human users in 180 dialogues, who also used a state-of-the-art hand- coded dialogue policy embedded in an otherwise identical system. We found that users of the (ported) learned policy had an average gain in perceived task completion of 14.2 (from 67.6 to 81.8 at p < .03), that the hand-coded policy dialogues had on average 3.3 more system turns (p < .01), and that the user satisfaction results were comparable, even though the policy was learned for a different domain. Combining these in a dialogue reward score, we found a 14.4 increase for the learnt policy (a 23.8 relative increase, p < .03). These results are important because they show a) that results for real users are consistent with results for automatic evaluation [2] of learned policies using simulated users [3, 4], b) that a policy learned using linear function approximation over a very large policy space [2] is effective for real users, and c) that policies learned using data for one domain can be used successfully in other domains. We also present a qualitative discussion of the learnt policy. Reinforcement techniques have been successfully used to maximise the expected cumulative reward of statistical dialogue systems. Typically, reinforcement learning is used to estimate the parameters of a dialogue policy which selects the system's responses based on the inferred dialogue state. However, the inference of the dialogue state itself depends on a dialogue model which describes the expected behaviour of a user when interacting with the system. Ideally the parameters of this dialogue model should be also optimised to maximise the expected cumulative reward. This article presents two novel reinforcement algorithms for learning the parameters of a dialogue model. First, the Natural Belief Critic algorithm is designed to optimise the model parameters while the policy is kept fixed. This algorithm is suitable, for example, in systems using a handcrafted policy, perhaps prescribed by other design considerations. Second, the Natural Actor and Belief Critic algorithm jointly optimises both the model and the policy parameters. The algorithms are evaluated on a statistical dialogue system modelled as a Partially Observable Markov Decision Process in a tourist information domain. The evaluation is performed with a user simulator and with real users. The experiments indicate that model parameters estimated to maximise the expected reward function provide improved performance compared to the baseline handcrafted parameters.