用约束解码提升古汉语命名实体识别效果，尤其适合标签多或数据量大的场景。
Logits-Constrained Framework with RoBERTa for Ancient Chinese NER
- 引入可微分解码机制，强制符合BMES标签转移规则
- 在EvaHan 2025上优于传统CRF与BiLSTM方法，尤其在高标签复杂度下
- 提出基于标签复杂度与数据量的模型选择准则，实用性强
本文提出一种针对古汉语命名实体识别（NER）的对数约束（Logits-Constrained, LC）框架，在EvaHan 2025基准上进行了评估。该两阶段模型结合GujiRoBERTa进行上下文编码，并采用可微分解码机制以强制合法的BMES标签转移。实验表明，LC在高标签数量或大规模数据设置下显著优于传统CRF与BiLSTM方法。此外，我们还提出了一个兼顾标签复杂度与数据集规模的模型选择准则，为实际古汉语自然语言处理任务提供可操作指导。
原文摘要 · Abstract (English)
This paper presents a Logits-Constrained (LC) framework for Ancient Chinese Named Entity Recognition (NER), evaluated on the EvaHan 2025 benchmark. Our two-stage model integrates GujiRoBERTa for contextual encoding and a differentiable decoding mechanism to enforce valid BMES label transitions. Experiments demonstrate that LC improves performance over traditional CRF and BiLSTM-based approaches, especially in high-label or large-data settings. We also propose a model selection criterion balancing label complexity and dataset size, providing practical guidance for real-world Ancient Chinese NLP tasks.
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