# Choice Fusion as Knowledge for Zero-Shot Dialogue State Tracking

## TL;DR

This paper introduces CoFunDST, a zero-shot dialogue state tracking model that leverages domain-agnostic QA data and choice fusion to improve accuracy in new domains without domain-specific training.

## Contribution

The paper proposes a novel approach that explicitly models knowledge transfer and fusion using candidate choices, enhancing zero-shot DST performance with a T5-based model.

## Key findings

- Outperforms existing zero-shot DST methods in most domains on MultiWOZ 2.1
- Effectively uses QA data for knowledge transfer in dialogue state tracking
- Demonstrates the importance of choice fusion in zero-shot learning

## Abstract

With the demanding need for deploying dialogue systems in new domains with less cost, zero-shot dialogue state tracking (DST), which tracks user's requirements in task-oriented dialogues without training on desired domains, draws attention increasingly. Although prior works have leveraged question-answering (QA) data to reduce the need for in-domain training in DST, they fail to explicitly model knowledge transfer and fusion for tracking dialogue states. To address this issue, we propose CoFunDST, which is trained on domain-agnostic QA datasets and directly uses candidate choices of slot-values as knowledge for zero-shot dialogue-state generation, based on a T5 pre-trained language model. Specifically, CoFunDST selects highly-relevant choices to the reference context and fuses them to initialize the decoder to constrain the model outputs. Our experimental results show that our proposed model achieves outperformed joint goal accuracy compared to existing zero-shot DST approaches in most domains on the MultiWOZ 2.1. Extensive analyses demonstrate the effectiveness of our proposed approach for improving zero-shot DST learning from QA.