Paper ID: 2304.00884

Dialog-to-Actions: Building Task-Oriented Dialogue System via Action-Level Generation

Yuncheng Hua, Xiangyu Xi, Zheng Jiang, Guanwei Zhang, Chaobo Sun, Guanglu Wan, Wei Ye

End-to-end generation-based approaches have been investigated and applied in task-oriented dialogue systems. However, in industrial scenarios, existing methods face the bottlenecks of controllability (e.g., domain-inconsistent responses, repetition problem, etc) and efficiency (e.g., long computation time, etc). In this paper, we propose a task-oriented dialogue system via action-level generation. Specifically, we first construct dialogue actions from large-scale dialogues and represent each natural language (NL) response as a sequence of dialogue actions. Further, we train a Sequence-to-Sequence model which takes the dialogue history as input and outputs sequence of dialogue actions. The generated dialogue actions are transformed into verbal responses. Experimental results show that our light-weighted method achieves competitive performance, and has the advantage of controllability and efficiency.

Submitted: Apr 3, 2023