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 are available in https://github.com/zjunlp/DeepKE/tree/main/example/ner/cross.

翻译：跨领域命名实体识别是一项具有挑战性的任务，旨在解决实际场景中的资源匮乏问题。以往的典型解决方案主要通过利用大规模资源领域的数据，使用预训练语言模型获得一个NER模型，并将其适配到目标领域。由于不同领域间实体类型存在不匹配问题，先前的方法通常会调整预训练语言模型的所有参数，最终为每个领域生成一个全新的NER模型。此外，当前模型仅关注利用单个通用源领域的知识，无法成功地将多个源领域的知识迁移到目标领域。为解决这些问题，我们基于文本到文本生成式预训练语言模型，提出了面向跨领域NER的协作领域前缀调优方法（CP-NER）。具体而言，我们通过文本到文本生成的方式，关联领域相关指令，将知识迁移至新领域的NER任务，而无需修改模型结构。我们利用冻结的预训练语言模型，并通过协作领域前缀调优来激发预训练语言模型处理跨领域NER任务的潜力。在Cross-NER基准上的实验结果表明，所提方法具有灵活的知识迁移能力，在单源和多源跨领域NER任务中均表现更优。代码已开源至https://github.com/zjunlp/DeepKE/tree/main/example/ner/cross。