  Zero-shot cross-domain slot filling aims to transfer knowledge from the
labeled source domain to the unlabeled target domain. Existing models either
encode slot descriptions and examples or design handcrafted question templates
using heuristic rules, suffering from poor generalization capability or
robustness. In this paper, we propose a generative zero-shot prompt learning
framework for cross-domain slot filling, both improving generalization and
robustness than previous work. Besides, we introduce a novel inverse prompting
strategy to distinguish different slot types to avoid the multiple prediction
problem, and an efficient prompt-tuning strategy to boost higher performance by
only training fewer prompt parameters. Experiments and analysis demonstrate the
effectiveness of our proposed framework, especially huge improvements (+13.44%
F1) on the unseen slots.
