Pre-trained Language Model Based Active Learning for Sentence Matching | |
Bai GR(白桂荣)1,2; He SZ(何世柱)1,2; Liu K(刘康)1,2; Zhao J(赵军)1,2 | |
2020 | |
会议日期 | 2020 |
会议地点 | 线上 |
页码 | 1495-1504 |
英文摘要 | 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. |
会议录出版者 | International Committee on Computational Linguistics |
会议录出版地 | Barcelona |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/48869] |
专题 | 模式识别国家重点实验室_自然语言处理 |
作者单位 | 1.中国科学院大学人工智能学院 2.中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Bai GR,He SZ,Liu K,et al. Pre-trained Language Model Based Active Learning for Sentence Matching[C]. 见:. 线上. 2020. |
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