A General Planning-Based Framework for Goal-Driven Conversation Assistant

  • Zhuoxuan Jiang IBM Research
  • Jie Ma IBM Research
  • Jingyi Lu Rice University
  • Guangyuan Yu IBM Research
  • Yipeng Yu IBM Research
  • Shaochun Li IBM Research

Abstract

We propose a general framework for goal-driven conversation assistant based on Planning methods. It aims to rapidly build a dialogue agent with less handcrafting and make the more interpretable and efficient dialogue management in various scenarios. By employing the Planning method, dialogue actions can be efficiently defined and reusable, and the transition of the dialogue are managed by a Planner. The proposed framework consists of a pipeline of Natural Language Understanding (intent labeler), Planning of Actions (with a World Model), and Natural Language Generation (learned by an attention-based neural network). We demonstrate our approach by creating conversational agents for several independent domains.

Published
2019-07-17
Section
Demonstration Track