  Chinese sequence labeling tasks are heavily reliant on accurate word boundary
demarcation. Although current pre-trained language models (PLMs) have achieved
substantial gains on these tasks, they rarely explicitly incorporate boundary
information into the modeling process. An exception to this is BABERT, which
incorporates unsupervised statistical boundary information into Chinese BERT's
pre-training objectives. Building upon this approach, we input supervised
high-quality boundary information to enhance BABERT's learning, developing a
semi-supervised boundary-aware PLM. To assess PLMs' ability to encode
boundaries, we introduce a novel ``Boundary Information Metric'' that is both
simple and effective. This metric allows comparison of different PLMs without
task-specific fine-tuning. Experimental results on Chinese sequence labeling
datasets demonstrate that the improved BABERT variant outperforms the vanilla
version, not only on these tasks but also more broadly across a range of
Chinese natural language understanding tasks. Additionally, our proposed metric
offers a convenient and accurate means of evaluating PLMs' boundary awareness.
