Diverse Retrieval-Augmented In-Context Learning for Dialogue State
  Tracking
Brendan King Jeffrey Flanigan
There has been significant interest in zero and few-shot learning for
dialogue state tracking (DST) due to the high cost of collecting and annotating
task-oriented dialogues. Recent work has demonstrated that in-context learning
requires very little data and zero parameter updates, and even outperforms
trained methods in the few-shot setting (Hu et al. 2022). We propose RefPyDST,
which advances the state of the art with three advancements to in-context
learning for DST. First, we formulate DST as a Python programming task,
explicitly modeling language coreference as variable reference in Python.
Second, since in-context learning depends highly on the context examples, we
propose a method to retrieve a diverse set of relevant examples to improve
performance. Finally, we introduce a novel re-weighting method during decoding
that takes into account probabilities of competing surface forms, and produces
a more accurate dialogue state prediction. We evaluate our approach using
MultiWOZ and achieve state-of-the-art multi-domain joint-goal accuracy in zero
and few-shot settings.