Generative Zero-Shot Prompt Learning for Cross-Domain Slot Filling with Inverse Prompting

Understand

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.