Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning
Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning
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DOI：
10.48550/arxiv.2203.09735
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
Rongzhi Zhang;Yue Yu;Pranav Shetty;Le Song;Chao Zhang
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文献类型：
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作者：
Rongzhi Zhang;Yue Yu;Pranav Shetty;Le Song;Chao Zhang
弱监督的学习（WSL）在解决许多NLP任务上的标签稀缺方面表现出了希望，但是手动设计全面的，高质量的标签规则集是乏味的，我们研究了互动的弱点学习问题 - 迭代和迭代的学习问题。从数据中自动发现新颖的标签规则，以改善WSL模型，名为Prboost，通过基于迭代的及时规则实现此目标发现和模型提升。并加强目前的模型。型号。
Weakly-supervised learning (WSL) has shown promising results in addressing label scarcity on many NLP tasks, but manually designing a comprehensive, high-quality labeling rule set is tedious and difficult. We study interactive weakly-supervised learning—the problem of iteratively and automatically discovering novel labeling rules from data to improve the WSL model. Our proposed model, named PRBoost, achieves this goal via iterative prompt-based rule discovery and model boosting. It uses boosting to identify large-error instances and discovers candidate rules from them by prompting pre-trained LMs with rule templates. The candidate rules are judged by human experts, and the accepted rules are used to generate complementary weak labels and strengthen the current model. Experiments on four tasks show PRBoost outperforms state-of-the-art WSL baselines up to 7.1%, and bridges the gaps with fully supervised models.