Generative Dialog Policy for Task-oriented Dialog Systems

TL;DR

This paper introduces a novel generative approach for dialogue policy learning in task-oriented systems, using attention and seq2seq models to generate multiple dialogue acts and parameters simultaneously, outperforming existing classification methods.

Contribution

The paper proposes a new generative dialogue policy method that constructs multiple dialogue acts and parameters at once, addressing limitations of classification-based approaches.

Findings

Significantly outperforms state-of-the-art baselines on benchmark datasets.

Uses attention mechanism to improve relevance in dialogue context.

Publicly released code for reproducibility.

Abstract

There is an increasing demand for task-oriented dialogue systems which can assist users in various activities such as booking tickets and restaurant reservations. In order to complete dialogues effectively, dialogue policy plays a key role in task-oriented dialogue systems. As far as we know, the existing task-oriented dialogue systems obtain the dialogue policy through classification, which can assign either a dialogue act and its corresponding parameters or multiple dialogue acts without their corresponding parameters for a dialogue action. In fact, a good dialogue policy should construct multiple dialogue acts and their corresponding parameters at the same time. However, it's hard for existing classification-based methods to achieve this goal. Thus, to address the issue above, we propose a novel generative dialogue policy learning method. Specifically, the proposed method uses attention mechanism to find relevant segments of given dialogue context and input utterance and then constructs the dialogue policy by a seq2seq way for task-oriented dialogue systems. Extensive experiments on two benchmark datasets show that the proposed model significantly outperforms the state-of-the-art baselines. In addition, we have publicly released our codes.