Prompter: Zero-shot Adaptive Prefixes for Dialogue State Tracking Domain
  Adaptation
Taha Aksu Min-Yen Kan Nancy F. Chen
A challenge in the Dialogue State Tracking (DST) field is adapting models to
new domains without using any supervised data, zero-shot domain adaptation.
Parameter-Efficient Transfer Learning (PETL) has the potential to address this
problem due to its robustness. However, it has yet to be applied to the
zero-shot scenarios, as it is not clear how to apply it unsupervisedly.
  Our method, Prompter, uses descriptions of target domain slots to generate
dynamic prefixes that are concatenated to the key and values at each layer's
self-attention mechanism. This allows for the use of prefix-tuning in
zero-shot. Prompter outperforms previous methods on both the MultiWOZ and SGD
benchmarks. In generating prefixes, our analyses find that Prompter not only
utilizes the semantics of slot descriptions but also how often the slots appear
together in conversation. Moreover, Prompter's gains are due to its improved
ability to distinguish "none"-valued dialogue slots, compared against
baselines.