Logits-Constrained Framework with RoBERTa for Ancient Chinese NER (2505.02983v1)

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.

• The paper presents a novel two-stage logits-constrained framework leveraging GujiRoBERTa for enhanced Named Entity Recognition in ancient Chinese texts.
• The Logits-Constrained framework demonstrates superior performance over traditional methods, achieving up to 2.95% F1 improvement on complex ancient Chinese NER tasks.
• Empirical results suggest avoiding BiLSTM components and combining CRF and LC mechanisms can optimize model performance based on dataset characteristics.

Overview of Logits-Constrained Framework with RoBERTa for Ancient Chinese NER

The paper "Logits-Constrained Framework with RoBERTa for Ancient Chinese NER" presents a novel approach to tackle the complexities inherent in the Named Entity Recognition (NER) of ancient Chinese texts. The proposed two-stage framework utilizes GujiRoBERTa pre-trained embeddings combined with a Logits-Constrained (LC) mechanism, addressing significant challenges posed by traditional Conditional Random Fields (CRFs) and BiLSTM modules in high-label scenarios.

Methodological Contributions

The authors introduce a distinctive two-stage architecture designed to enhance adherence to structural constraints, specifically within the BMES tagging scheme. The framework consists of:

1. Contextual Encoding with GujiRoBERTa: This stage leverages the GujiRoBERTa model to generate robust contextual embeddings for tokens. The embeddings are transformed into preliminary label logits, optimizing through a standard cross-entropy loss function without explicit transition modeling.
2. Logits-Constrained Decoding: A constraint matrix encodes valid transitions under the BMES tagging framework. During inference, the logits undergo a masked autoregressive refinement process, ensuring output sequence validity.

Experimental Results

Empirical validation was conducted across multiple datasets annotated with varying NER categories and sentence counts, demonstrating significant performance enhancements compared to traditional methods. Notably, experiments reveal several key findings:

• LC Framework Superiority: On datasets with a richer label structure, the LC framework notably surpasses CRF performance, with an average F1 score improvement of up to 2.95% on complex NER tasks.
• BiLSTM Modules Impact: The integration of BiLSTM components leads to marked performance degradation across all datasets, suggesting interference with pre-trained attention patterns.

Dataset Expansion and Model Selection

The paper also explores dataset expansion techniques and optimal model configurations based on label complexity and dataset size. A hybrid dataset approach nearly tripled sample availability and further optimized model performance within the sequence labeling discipline. Optimal configurations are delineated based on empirical assessments of label cardinality $L$ and sentence count $N$.

Practical and Theoretical Implications

The Logits-Constrained approach propounded in this paper offers substantial advancements in ancient Chinese NER tasks. Practically, it informs empirical model selection guidelines, emphasizing avoidance of BiLSTM usage and suggesting the combined application of CRF and LC mechanisms under certain conditions. Theoretically, it refines understanding of dynamic masking's efficacy in BMES constraints, steering future developments in sequence labeling frameworks.

Limitations and Future Directions

While the framework demonstrates improved accuracy and simplicity, it bears limitations concerning reliance on manual BMES rules for constraint matrices and potential inference efficiency overheads from the two-stage design. Future research could consider adaptive constraint learning and streamline architectures to mitigate these shortcomings, exploring broader applications within diverse historical text corpora and further refining the LC mechanism's adaptability to erroneous or unpunctuated text formats.

In conclusion, the paper delivers a methodologically sophisticated solution to the intricate task of ancient Chinese NER, providing substantial grounds for future exploration in constrained decoding methodologies and enhancing understanding of effective sequence labeling in high-dimension, label-rich environments.