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.

翻译：零样本跨领域槽填充旨在将标注源领域的知识迁移至未标注的目标领域。现有模型或编码槽描述与示例，或基于启发式规则设计手工问题模板，存在泛化能力不足或鲁棒性较差的缺陷。本文提出一种面向跨领域槽填充的生成式零样本提示学习框架，相较前人工作同步提升了泛化性能与鲁棒性。此外，我们引入新颖的反向提示策略以区分不同槽类型，从而避免多预测问题；并设计高效的提示微调策略，仅训练少量提示参数即可显著提升性能。实验与分析验证了所提框架的有效性，特别是在未见槽上取得了+13.44% F1值的巨大提升。