Grounding Description-Driven Dialogue State Trackers with
  Knowledge-Seeking Turns
Alexandru Coca Bo-Hsiang Tseng Jinghong Chen Weizhe Lin Weixuan Zhang Tisha Anders Bill Byrne
Schema-guided dialogue state trackers can generalise to new domains without
further training, yet they are sensitive to the writing style of the schemata.
Augmenting the training set with human or synthetic schema paraphrases improves
the model robustness to these variations but can be either costly or difficult
to control. We propose to circumvent these issues by grounding the state
tracking model in knowledge-seeking turns collected from the dialogue corpus as
well as the schema. Including these turns in prompts during finetuning and
inference leads to marked improvements in model robustness, as demonstrated by
large average joint goal accuracy and schema sensitivity improvements on SGD
and SGD-X.