UNO-DST: Leveraging Unlabelled Data in Zero-Shot Dialogue State Tracking

Anonymous

TL;DR: This paper aim to utilise unlabelled data for self-training in order to improve the accuracy of zero-shot DST on PLMs and LLMs.

Abstract:

Previous zero-shot dialogue state tracking (DST) methods only apply transfer learning, but ignore unlabelled data in the target domain.
We transform zero-shot DST into few-shot DST by utilising such unlabelled data via joint and self-training methods. Our method incorporates auxiliary tasks that generate slot types as inverse prompts for main tasks, creating slot values during joint training. Cycle consistency between these two tasks enables the generation and selection of quality samples in unknown target domains for subsequent fine-tuning. This approach also facilitates automatic label creation, thereby optimizing the training and fine-tuning of DST models. We demonstrate this method's effectiveness on large language models in zero-shot scenarios, improving average joint goal accuracy by 8% across all domains in MultiWOZ.

Paper Type: long

Research Area: Dialogue and Interactive Systems

Contribution Types: Approaches to low-resource settings

Languages Studied: English