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.
Dialogue evaluation has been an active research area since late 90s. proposed the PARADISE framework, where a linear function of task completion and various dialogue features such as dialogue duration were used to infer user satisfaction. This measure was later used as a reward function for learning a dialogue policy @cite_10 . However, as noted, task completion is rarely available when the system is interacting with real users and also concerns have been raised regarding the theoretical validity of the model @cite_23 .
@cite_10 @cite_23
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This paper presents PARADISE (PARAdigm for DIalogue System Evaluation), a general framework for evaluating spoken dialogue agents. The framework decouples task requirements from an agent's dialogue behaviors, supports comparisons among dialogue strategies, enables the calculation of performance over subdialogues and whole dialogues, specifies the relative contribution of various factors to performance, and makes it possible to compare agents performing different tasks by normalizing for task complexity. The paper presents results and conclusions about the current evaluation methodologies for spoken dialogue systems (SDS). The PARADISE paradigm, used for evaluation in the DARPA Communicator project, is briefly introduced and discussed through the application to the OVID home banking dialogue system. It is shown to provide results consistent with those obtained by the DARPA community, but a number of problems and limitations are pointed out. The issue of user attitude measures obtained through questionnaires is discussed. This is an area that has not received much attention from the speech technology community, but is important in order to obtain valid results and conclusions about usability. A general presentation of the issues that must be addressed when developing and employing questionnaires is given with a focus on how to ensure the reliability and validity of the results. Examples of results obtained from the OVID project are used to illustrate this. 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. This paper describes a novel method by which a spoken dialogue system can learn to choose an optimal dialogue strategy from its experience interacting with human users. The method is based on a combination of reinforcement learning and performance modeling of spoken dialogue systems. The reinforcement learning component applies Q-learning (Watkins, 1989), while the performance modeling component applies the PARADISE evaluation framework (, 1997) to learn the performance function (reward) used in reinforcement learning. We illustrate the method with a spoken dialogue system named elvis (EmaiL Voice Interactive System), that supports access to email over the phone. We conduct a set of experiments for training an optimal dialogue strategy on a corpus of 219 dialogues in which human users interact with elvis over the phone. We then test that strategy on a corpus of 18 dialogues. We show that elvis can learn to optimize its strategy selection for agent initiative, for reading messages, and for summarizing email folders.