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
this https URL.

本文提出一种基于文本生成模型的协作域前缀调整方法，用于跨领域命名实体识别任务，实现知识在不同领域之间的迁移，提高模型对多源领域的处理能力。实验结果表明，该方法灵活、可靠，优于当前主流的同类方法。

为所有领域设计的一种模型：协作式领域前缀调整用于跨领域命名实体识别

One Model for All Domains: Collaborative Domain-Prefix Tuning for Cross-Domain NER

Input distribution shift is one of the vital problems in unsupervised domain
adaptation (UDA). The most popular UDA approaches focus on domain-invariant
representation learning, trying to align the features from different domains
into similar feature distributions. However, these approaches ignore the direct
alignment of input word distributions between domains, which is a vital factor
in word-level classification tasks such as cross-domain NER. In this work, we
shed new light on cross-domain NER by introducing a subword-level solution,
X-Piece, for input word-level distribution shift in NER. Specifically, we
re-tokenize the input words of the source domain to approach the target subword
distribution, which is formulated and solved as an optimal transport problem.
As this approach focuses on the input level, it can also be combined with
previous DIRL methods for further improvement. Experimental results show the
effectiveness of the proposed method based on BERT-tagger on four benchmark NER
datasets. Also, the proposed method is proved to benefit DIRL methods such as
DANN.

本研究提出了一种基于子词的解决方案（X-Piece），通过重新令牌化源域的输入单词以接近目标子词分布，来解决交叉域命名实体识别中的输入词级分布偏移问题，实验证明了这种方法基于 BERT-tagger 在四个基准 NER 数据集上的有效性。