@inproceedings{bai-etal-2020-pre,
    title = "Pre-trained Language Model Based Active Learning for Sentence Matching",
    author = "Bai, Guirong  and
      He, Shizhu  and
      Liu, Kang  and
      Zhao, Jun  and
      Nie, Zaiqing",
    editor = "Scott, Donia  and
      Bel, Nuria  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.coling-main.130/",
    doi = "10.18653/v1/2020.coling-main.130",
    pages = "1495--1504",
    abstract = "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."
}