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arXiv · 2608.05167

CNM-BERT: A Drop-In Structural Embedding for Chinese Characters via Ideographic Description Sequences

Thomas Sing-wing Wu·Liqian Yan
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Abstract

Token-based encoders like BERT treat Chinese characters as atomic identifiers, ignoring their recursive orthographic structure. Consequently, models rely on contextual co-occurrence, degrading performance on rare and out-of-vocabulary (OOV) characters. We propose the Compositional Network Model (CNM), a lightweight augmentation that injects discrete compositional structure into Transformer encoders. CNM parses Ideographic Description Sequences (IDS) into trees, encodes them via a recursive Tree-MLP, and fuses the structural embeddings into BERT without modifying the backbone. Evaluated on the Wu et al. (2025) structural-probing benchmark, CNM-BERT outperforms the strongest baseline (ChineseBERT) on long-tail and OOV characters by +9.8 Structure accuracy and +7.7 Radical F1. Furthermore, CNM-BERT achieves consistent gains across CLUE, MRC, and NER tasks at both base and large scales, demonstrating that explicit structural injection delivers both robust OOV understanding and tangible downstream value.

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Thomas Sing-wing Wu, Liqian Yan. 2026-05-27. CNM-BERT: A Drop-In Structural Embedding for Chinese Characters via Ideographic Description Sequences. https://arxiv.org/abs/2608.05167

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