Pre-trained Language Model Based Active Learning for Sentence Matching
Guirong Bai, Shizhu He, Kang Liu, Jun Zhao, Zaiqing Nie · 2020
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 from the pre-trained language model to measure instances and help select more effective instances for annotation.Experiments demonstrate our approach can achieve greater accuracy with fewer labeled training instances.