  Active learning is able to significantly reduce the annotation cost for
data-driven techniques. However, previous active learning approaches for
natural language processing mainly depend on the entropy-based uncertainty
criterion, and ignore the characteristics of natural language. In this paper,
we propose a pre-trained language model based active learning approach for
sentence matching. Differing from previous active learning, it can provide
linguistic criteria to measure instances and help select more efficient
instances for annotation. Experiments demonstrate our approach can achieve
greater accuracy with fewer labeled training instances.
