by how much did nus outperform abus?
Average success rate is higher by 2.6 percent points.
User Simulators are one of the major tools that enable offline training of task-oriented dialogue systems. For this task the Agenda-Based User Simulator (ABUS) is often used. The ABUS is based on hand-crafted rules and its output is in semantic form. Issues arise from both properties such as limited diversity and the inability to interface a text-level belief tracker. This paper introduces the Neural User Simulator (NUS) whose behaviour is learned from a corpus and which generates natural language, hence needing a less labelled dataset than simulators generating a semantic output. In comparison to much of the past work on this topic, which evaluates user simulators on corpus-based metrics, we use the NUS to train the policy of a reinforcement learning based Spoken Dialogue System. The NUS is compared to the ABUS by evaluating the policies that were trained using the simulators. Cross-model evaluation is performed i.e. training on one simulator and testing on the other. Furthermore, the trained policies are tested on real users. In both evaluation tasks the NUS outperformed the ABUS.
Spoken Dialogue Systems (SDS) allow human-computer interaction using natural speech. Task-oriented dialogue systems, the focus of this work, help users achieve goals such as finding restaurants or booking flights BIBREF0 . Teaching a system how to respond appropriately in a task-oriented setting is non-trivial. In state-of-the-art systems this dialogue management task is often formulated as a reinforcement learning (RL) problem BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . In this framework, the system learns by a trial and error process governed by a reward function. User Simulators can be used to train the policy of a dialogue manager (DM) without real user interactions. Furthermore, they allow an unlimited number of dialogues to be created with each dialogue being faster than a dialogue with a human. In this paper the Neural User Simulator (NUS) is introduced which outputs natural language and whose behaviour is learned from a corpus. The main component, inspired by BIBREF4 , consists of a feature extractor and a neural network based sequence-to-sequence model BIBREF5 . The sequence-to-sequence model consists of a recurrent neural network (RNN) encoder that encodes the dialogue history and a decoder RNN which outputs natural language. Furthermore, the NUS generates its own goal and possibly changes it during a dialogue. This allows the model to be deployed for training more sophisticated DM policies. To achieve this, a method is proposed that transforms the goal-labels of the used dataset (DSTC2) into labels whose behaviour can be replicated during deployment. The NUS is trained on dialogues between real users and an SDS in a restaurant recommendation domain. Compared to much of the related work on user simulation, we use the trained NUS to train the policy of a reinforcement learning based SDS. In order to evaluate the NUS, an Agenda-Based User-Simulator (ABUS) BIBREF6 is used to train another policy. The two policies are compared against each other by using cross-model evaluation BIBREF7 . This means to train on one model and to test on the other. Furthermore, both trained policies are tested on real users. On both evaluation tasks the NUS outperforms the ABUS, which is currently one of the most popular off-line training tools for reinforcement learning based Spoken Dialogue Systems BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 . The remainder of this paper is organised as follows. Section 2 briefly describes task-oriented dialogue. Section 3 describes the motivation for the NUS and discusses related work. Section 4 explains the structure of the NUS, how it is trained and how it is deployed for training a DM's policy. Sections 5 and 6 present the experimental setup and results. Finally, Section 7 gives conclusions.