In sequence labeling, previous domain adaptation methods focus on the
adaptation from the source domain to the entire target domain without
considering the diversity of individual target domain samples, which may lead
to negative transfer results for certain samples. Besides, an important
characteristic of sequence labeling tasks is that different elements within a
given sample may also have diverse domain relevance, which requires further
consideration. To take the multi-level domain relevance discrepancy into
account, in this paper, we propose a fine-grained knowledge fusion model with
the domain relevance modeling scheme to control the balance between learning
from the target domain data and learning from the source domain model.
Experiments on three sequence labeling tasks show that our fine-grained
knowledge fusion model outperforms strong baselines and other state-of-the-art
sequence labeling domain adaptation methods.

本研究提出一种细粒度知识融合模型，通过领域关联建模来控制从目标域数据学习和从源域模型学习之间的平衡，以解决序列标注中多级领域相关性差异的问题，实验结果表明，这种模型在三种序列标注任务中表现优异。