Logits-Constrained Framework with RoBERTa for Ancient Chinese NER

Wenjie Hua; Shenghan Xu

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arXiv · 2505.02983

Logits-Constrained Framework with RoBERTa for Ancient Chinese NER

Wenjie Hua·Shenghan Xu

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Abstract

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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BibTeX RIS

Wenjie Hua, Shenghan Xu. 2025-05-05. Logits-Constrained Framework with RoBERTa for Ancient Chinese NER. https://arxiv.org/abs/2505.02983

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