Exploiting Phonetics and Glyph Representation at Radical-level for Classical C hinese Understanding (2025)

Abstract: The diachronic gap between classical and modern Chinese arises from century-scale language evolution through cumulative changes in phonological, syntactic, and lexical systems, resulting in substantial semantic variation that poses significant challenges for the computational modeling of historical texts. Current methods always enhance classical Chinese understanding of pre-trained language models through corpus pre-training or semantic integration. However, they overlook the synergistic relationship between phonetic and glyph features within Chinese characters, which is a critical factor in deciphering characters’ semantics. In this paper, we propose RPGCM, a radical-level phonetics and glyph representation enhanced Chinese model, with powerful fine-grained semantic modeling capabilities. Our model establishes robust contextualized representations through: (1) rules-based radical decomposition and bype pair encoder (BPE) based radical aggregated for structural pattern recognition, (2) phonetic-glyph semantic mapping, and (3) dynamic semantic fusion. Experimental results on CCMRC, WYWEB, and C³Bench benchmarks demonstrate the RPGCM’s superiority and validate that explicit radical-level modeling effectively mitigates semantic variations.

## introduction the natural language processing (nlp) community has witnessed a growing interest in exploiting classical chinese heritage @xcite @xcite , driven by its unique value in preserving three millennia of philosophical discourse and scholarly knowledge. they exhibit a diachronic gap between classical and modern chinese @xcite . over time, phonetic shifts, morphological changes, and diversities in 1 ⋆ corresponding author 2 the source code will be available at: figure 1 : the illustration of a phono-semantic compound character, which is commonly used by its inherent meaning in classical chinese. derivative cognate characters refer to chinese characters with similar meanings, and thus similar glyph and phonetics. existing approaches in classical chinese understanding have progressively integrated heterogeneous linguistic knowledge, spanning lexicalsemantic relationships @xcite , sememe-based representations @xcite , interpretive dictionary resources @xcite , syntactic pattern analysis @xcite , and discrete glyph processing @xcite . however, they remain constrained by tokenized feature extraction paradigms that fail to account for the synergistic relationship between phonetic and glyph components, a fundamental principle in chinese character formation. this oversight contradicts the structural logic of historical linguistics evolution, where semantic rad-icals systematically combine with phonetic and glyph elements to generate new characters with compositional meanings. crucially, such inherent glyph-phonetic interdependencies create selfcontained semantic patterns that enable contextual understanding without external knowledge augmentation. phono-semantic compounds, constituting over 80% of chinese characters @xcite , combine phonetic determinants (indicating pronunciation for disambiguating homographs) with semantic radicals (conveying conceptual associations among cognates). these compounds, known as radicals representing character constituents, constitute the minimal functional units in chinese orthography. these radical combinations create systematic glyph patterns through their structural arrangements. crucially, combinatorial radical arrangements encode inherent meanings, while derivative cognates exhibit semantic kinship through shared phonetic and glyph features. as shown in figure 1 , the character "賊" evolved from meaning "harm" to "thief", exemplifying diachronic semantic shifts that necessitate modeling glyph-phonetic interactions for semantic recovery during modern model adaptation. these approaches rely solely on classical corpus pre-training to achieve suboptimal performance due to data scarcity, while character-level modeling in modern chinese frameworks results in systematic misinterpretations of low-frequency yet semantically nuanced characters. phonetic and glyph features have proven instrumental for modern chinese nlp tasks, including reading comprehension @xcite , sememe prediction @xcite , and entity recognition @xcite . yet its application to classical chinese remains nascent, where current approaches rely on shallow feature concatenation rather than modeling the compositional relationship between semantic radicals and their phonetic counterparts. in this paper, we propose a radical-level phonetics and glyph representation enhanced chinese model (rpgcm). the model first decomposes each chinese character into semantic-independent radicals, then captures implicit relationships between glyph and phonetics to alleviate the negative effect of semantic variation. specifically, rpgcm utilizes the byte pair encoding (bpe) algorithm to transform the stroke sequence of chinese characters into radicals. two independent networks are employed to obtain phonetic and glyph em-beddings from the pinyin sequence and character glyph. the glyph image highlights the stroke subsequence corresponding to the current radical, allowing the model to reconstruct the whole chinese character. then, a dynamic fusion mechanism is adopted to acquire integrated embeddings that dynamically prioritize valuable features. the pretraining tasks include three-level masked language modeling, phonetic loan character discrimination, and phonetic-glyph cross-prediction. they enhance the connections between glyphs, phonetics, and semantics to deconstruct the original meanings implied in individual chinese characters. the experimental results on classical chinese machine reading comprehension (ccmrc), comprehensive classical chinese understanding benchmark (c 3 bench), and wen yan wen evaluation benchmark (wyweb) indicate that our proposed model outperforms the baselines and achieves significant progress. the contributions of our work can be summarized in threefolds: ## methodology our proposed rpgcm aims to bridge the diachronic gap between classical and modern chinese caused by semantic variation. first, it transforms chinese characters into stroke sequences and employs the bpe algorithm to break chinese characters into smaller subwords. then, it leverages several pre-training tasks to capture linguistic patterns of classical chinese. the overall framework of rpgcm is depicted in fig. 2 . ## conclusions and future works we proposed rpgcm, a phonetics and glyph enhanced chinese model for classical chinese understanding. by integrating phonetic and glyph embeddings, rpgcm effectively mitigates semantic variation. experiments on ccmrc, c 3 bench, and wyweb confirm its superiority, highlighting the importance of phonetics and glyphs in classical chinese nlp. future work will explore multimodal learning by integrating phonetics, glyphs, and contextual knowledge to further enhance classical chinese understanding.