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

Anonymous

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 and lack of interaction. 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.

Paper Type: short

Research Area: Dialogue and Interactive Systems

Contribution Types: NLP engineering experiment, Reproduction study, Data resources

Languages Studied: English

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