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。