Cross-domain NER is a challenging task to address the low-resource problem in practical scenarios. Previous typical solutions mainly obtain a NER model by pre-trained language models (PLMs) with data from a rich-resource domain and adapt it to the target domain. Owing to the mismatch issue among entity types in different domains, previous approaches normally tune all parameters of PLMs, ending up with an entirely new NER model for each domain. Moreover, current models only focus on leveraging knowledge in one general source domain while failing to successfully transfer knowledge from multiple sources to the target. To address these issues, we introduce Collaborative Domain-Prefix Tuning for cross-domain NER (CP-NER) based on text-to-text generative PLMs. Specifically, we present text-to-text generation grounding domain-related instructors to transfer knowledge to new domain NER tasks without structural modifications. We utilize frozen PLMs and conduct collaborative domain-prefix tuning to stimulate the potential of PLMs to handle NER tasks across various domains. Experimental results on the Cross-NER benchmark show that the proposed approach has flexible transfer ability and performs better on both one-source and multiple-source cross-domain NER tasks. Codes will be available in https://github.com/zjunlp/DeepKE/tree/main/example/ner/cross.

翻译：跨领域命名实体识别（NER）是解决实际场景中低资源问题的挑战性任务。先前典型方案主要通过富资源领域数据预训练语言模型（PLMs）获得NER模型，再将其适配至目标领域。由于不同领域实体类型存在不匹配问题，现有方法通常调整PLMs所有参数，最终为每个领域生成全新NER模型。此外，当前模型仅关注利用单一通用源领域的知识，未能成功将多源知识迁移至目标领域。针对这些问题，我们基于文本到文本生成式PLMs提出协作性领域前缀微调方法（CP-NER）用于跨领域NER。具体而言，我们通过文本到文本生成方式锚定领域相关指令，在不修改模型结构的情况下将知识迁移至新领域NER任务。采用冻结PLMs策略并进行协作性领域前缀微调，以激发PLMs处理跨领域NER任务的潜力。在Cross-NER基准上的实验结果表明，所提方法具备灵活迁移能力，在单源和多源跨领域NER任务中均表现更优。代码将发布于https://github.com/zjunlp/DeepKE/tree/main/example/ner/cross。