Using Textual Interface to Align External Knowledge for End-to-End Task-Oriented Dialogue Systems

TL;DR

This paper introduces a novel textual interface paradigm for end-to-end task-oriented dialogue systems that improves alignment with external knowledge and enhances response naturalness and task success.

Contribution

It proposes a new paradigm using a textual interface to better align external knowledge and introduces a re-processed dataset and system evaluation for this approach.

Findings

Generated more natural final responses.

Achieved higher task success rate.

Improved alignment with external knowledge.

Abstract

Traditional end-to-end task-oriented dialogue systems have been built with a modularized design. However, such design often causes misalignment between the agent response and external knowledge, due to inadequate representation of information. Furthermore, its evaluation metrics emphasize assessing the agent's pre-lexicalization response, neglecting the quality of the completed response. In this work, we propose a novel paradigm that uses a textual interface to align external knowledge and eliminate redundant processes. We demonstrate our paradigm in practice through MultiWOZ-Remake, including an interactive textual interface built for the MultiWOZ database and a correspondingly re-processed dataset. We train an end-to-end dialogue system to evaluate this new dataset. The experimental results show that our approach generates more natural final responses and achieves a greater task success rate compared to the previous models.