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Prompt-based methods with large pre-trained language models (PLMs) have shownimpressive unaided performance across many NLP tasks. These models improve evenfurther with the addition of a few labeled in-context exemplars to guide outputgeneration. However, for more complex tasks such as dialogue state tracking(DST), designing prompts that reliably convey the desired intent is nontrivial,leading to unstable results. Furthermore, building in-context exemplars fordialogue tasks is difficult because conversational contexts are long whilemodel input lengths are relatively short. To overcome these issues we firstadapt a meta-learning scheme to the dialogue domain which stabilizes theability of the model to perform well under various prompts. We additionallydesign a novel training method to improve upon vanilla retrieval mechanisms tofind ideal in-context examples. Finally, we introduce a saliency model to limitdialogue text length, allowing us to include more exemplars per query. Ineffect, we are able to achieve highly competitive results for few-shot DST onMultiWOZ.

Stabilized In-Context Learning with Pre-trained Language Models for Few Shot Dialogue State Tracking