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.
@article{arxiv.2505.02983,
title = {Logits-Constrained Framework with RoBERTa for Ancient Chinese NER},
author = {Wenjie Hua and Shenghan Xu},
journal= {arXiv preprint arXiv:2505.02983},
year = {2025}
}
Comments
5 pages, 2 figures, 6 tables. Accepted to EvaHan 2025 shared task on Ancient Chinese NLP