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arXiv · 2305.13710

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

Qingyang Wu·Deema Alnuhait·Derek Chen·Zhou Yu
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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.

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Qingyang Wu, Deema Alnuhait, Derek Chen, Zhou Yu. 2023-05-23. Using Textual Interface to Align External Knowledge for End-to-End Task-Oriented Dialogue Systems. https://arxiv.org/abs/2305.13710

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