Using Textual Interface to Align External Knowledge for End-to-End
  Task-Oriented Dialogue Systems
Qingyang Wu Deema Alnuhait Derek Chen Zhou Yu
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.