Sequence labeling models often benefit from incorporating external knowledge. However, this practice introduces data heterogeneity and complicates the model with additional modules, leading to increased expenses for training a high-performing model. To address this challenge, we propose a two-stage curriculum learning (TCL) framework specifically designed for sequence labeling tasks. The TCL framework enhances training by gradually introducing data instances from easy to hard, aiming to improve both performance and training speed. Furthermore, we explore different metrics for assessing the difficulty levels of sequence labeling tasks. Through extensive experimentation on six Chinese word segmentation (CWS) and Part-of-speech tagging (POS) datasets, we demonstrate the effectiveness of our model in enhancing the performance of sequence labeling models. Additionally, our analysis indicates that TCL accelerates training and alleviates the slow training problem associated with complex models.

翻译：序列标注模型通常受益于引入外部知识。然而，这种做法引入了数据异质性，并通过额外模块使模型复杂化，导致训练高性能模型的成本增加。为应对这一挑战，我们提出了一种专门针对序列标注任务的两阶段课程学习（TCL）框架。该框架通过从简单到困难逐步引入数据实例来优化训练过程，旨在提升模型性能与训练速度。此外，我们探索了评估序列标注任务难度等级的不同指标。通过在六个中文分词（CWS）与词性标注（POS）数据集上的广泛实验，我们验证了所提模型在增强序列标注模型性能方面的有效性。进一步分析表明，TCL加速了训练过程，缓解了复杂模型训练缓慢的问题。