Enhancing Performance on Seen and Unseen Dialogue Scenarios using
  Retrieval-Augmented End-to-End Task-Oriented System
Jianguo Zhang Stephen Roller Kun Qian Zhiwei Liu Rui Meng Shelby Heinecke Huan Wang Silvio Savarese Caiming Xiong
End-to-end task-oriented dialogue (TOD) systems have achieved promising
performance by leveraging sophisticated natural language understanding and
natural language generation capabilities of pre-trained models. This work
enables the TOD systems with more flexibility through a simple cache. The cache
provides the flexibility to dynamically update the TOD systems and handle both
existing and unseen dialogue scenarios. Towards this end, we first fine-tune a
retrieval module to effectively retrieve the most relevant information entries
from the cache. We then train end-to-end TOD models that can refer to and
ground on both dialogue history and retrieved information during TOD
generation. The cache is straightforward to construct, and the backbone models
of TOD systems are compatible with existing pre-trained generative models.
Extensive experiments demonstrate the superior performance of our framework,
with a notable improvement in non-empty joint goal accuracy by 6.7% compared to
strong baselines.