  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.
