研究化学反应提取中主动学习的局限性，发现其效果受预训练、标签稀疏等因素影响。
When Active Learning Falls Short: An Empirical Study on Chemical Reaction Extraction
- 结合六种不确定性与多样性策略，用预训练模型+CRF进行反应信息抽取。
- 部分方法用少量标注数据接近全数据性能，但学习曲线常不单调且任务依赖性强。
- 指出强预训练和标签稀疏会削弱传统主动学习稳定性，适合需高效标注的化学领域研究者。
化学文献的快速增长产生了大量非结构化数据，其中反应信息对反应预测和药物设计等应用至关重要。然而，专家标注成本高昂，导致训练数据稀缺，严重制约自动反应提取性能。本文系统研究了主动学习在化学反应提取中的应用，将六种基于不确定性和多样性的策略与预训练Transformer-CRF架构结合，在产物提取和角色标注任务上进行评估。尽管某些方法在较少标注样本下接近全数据性能，但学习曲线常呈现非单调性且任务依赖明显。分析表明，强预训练、结构化CRF解码及标签稀疏性限制了传统主动学习策略的稳定性。这些发现为化学信息抽取中有效使用主动学习提供了实践启示。
原文摘要 · Abstract (English)
The rapid growth of chemical literature has generated vast amounts of unstructured data, where reaction information is particularly valuable for applications such as reaction predictions and drug design. However, the prohibitive cost of expert annotation has led to a scarcity of training data, severely hindering the performance of automatic reaction extraction. In this work, we conduct a systematic study of active learning for chemical reaction extraction. We integrate six uncertainty- and diversity-based strategies with pretrained transformer-CRF architectures, and evaluate them on product extraction and role labeling task. While several methods approach full-data performance with fewer labeled instances, learning curves are often non-monotonic and task-dependent. Our analysis shows that strong pretraining, structured CRF decoding, and label sparsity limit the stability of conventional active learning strategies. These findings provide practical insights for the effective use of active learning in chemical information extraction.
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