Enhancing Named Entity Translation from Classical C hinese to V ietnamese in Traditional V ietnamese Medicine Domain: A Hybrid Masking and Dictionary-Augmented Approach (2025)

Abstract: Vietnam’s traditional medical texts were historically written in Classical Chinese using Sino-Vietnamese pronunciations. As the Vietnamese language transitioned to a Latin-based national script and interest in integrating traditional medicine with modern healthcare grows, accurate translation of these texts has become increasingly important. However, the diversity of terminology and the complexity of translating medical entities into modern contexts pose significant challenges. To address this, we propose a method that fine-tunes large language models (LLMs) using augmented data and a Hybrid Entity Masking and Replacement (HEMR) strategy to improve named entity translation. We also introduce a parallel named entity translation dataset specifically curated for traditional Vietnamese medicine. Our evaluation across multiple LLMs shows that the proposed approach achieves a translation accuracy of 71.91%, demonstrating its effectiveness. These results underscore the importance of incorporating named entity awareness into translation systems, particularly in low-resource and domain-specific settings like traditional Vietnamese medicine.

## introduction classical chinese script, pronounced through sino-vietnamese pronunciations, was the primary writing system for vietnamese texts until the 20th century. it played a central role in documenting traditional vietnamese medicine. with the shift to the latin-based vietnamese national script, the ability to access and interpret these historical texts has steadily declined. as the vietnamese government increasingly looks to incorporate traditional medicine into modern healthcare systems, the preservation and digitization of these historical documents has become more urgent than ever. a critical aspect of translating traditional vietnamese medicine is the accurate translation of named entities (nes), particularly medicinal materials. these entities often possess culturally and linguistically specific names in vietnamese that do not directly correspond to their chinese equivalents. mistranslations can occur due to variations in naming conventions. for example,"黄斤" (kudzu powder) can be translated as "cát căn", "hoàng cân", or "sắn dây". among these, "sắn dây" is the most widely understood in contemporary usage, but many traditional medical texts still prefer the earlier terms. the challenge stems not only from the diversity of names but also from the difficulty of accurately translating these terms in modern contexts. given this challenge, it is essential to develop machine translation systems for classical chinese-vietnamese texts with a strong focus on ne accuracy. in recent years, the emergence of large language models (llms) has significantly improved multilingual translation capabilities, including for chinese and vietnamese. llms have shown great potential in the field of traditional chinese medicine by analyzing classical texts, supporting clinical decision-making, and bridging traditional and modern medical paradigms @xcite . @xcite fine-tuned t5 models using spacy for entity recognition combined with xml-based tagging, while @xcite proposed a data augmentation framework based on translation difficulty and context diversity scores. these advancements highlight the potential of llms as a promising approach to address the complexities of ne translation in traditional vietnamese medicine. in this paper, we aim to tackle the challenge of ne translation from classical chinese to vietnamese within the domain of traditional vietnamese medicine. our approach uses fine-tuned llms, enhanced through data augmentation tech-niques and an entity masking and replacement strategy based on a specialized dictionary. to achieve these goals, we present the following contributions: ## background chinese script, sino-vietnamese, and modern vietnamese translation: literary chinese, written in chinese script and read with sino-vietnamese pronunciations, was once a major writing system in vietnam @xcite . most texts, including those on traditional medicine, were composed this way. with the adoption of the latinbased vietnamese national script, vietnam shifted to a phonographic system that preserves about 60% of sino-vietnamese pronunciations @xcite . however, this transition reduced the population's ability to read chinese script, limiting access to historical texts. although we can transliterate classical chinese into the national script, the text often stays largely incomprehensible because much of the vocabulary is obsolete or has been replaced. ## related work traditional approaches to ne handling in neural machine translation (nmt) typically involve explicitly introducing ne information into the translation pipeline to improve translation accuracy. early research focused on integrated neural models and attention mechanisms. @xcite , and @xcite , enhanced nmt using linguistic annotations and ne embeddings. @xcite extended token embeddings with entity-specific vectors, while modrzejewski et al. (2020) incorporated source-side linguistic factors into transformer networks. these methods improved contextual representation but relied heavily on large amounts of labeled data, which poses scalability challenges. to address these limitations, xie et al. (2022) proposed an end-to-end nmt architecture with entity classifiers integrated into both encoder and decoder. an adaptive loss function emphasized ne translation accuracy. this built-in ne awareness lessens dependence on external systems and reduces error propagation, leading to better performance on six benchmark tasks. more recently, the field has shifted towards datacentric and llm-based approaches. @xcite fine-tuned t5 models for ne-aware translation using spacy for entity recognition and xml-based entity tagging. this method increased ne preservation and improved bleu scores, especially for rare entities. @xcite introduced a data augmentation framework based on translation difficulty and context diversity scores. their strategy improved overall translation and ne accuracy across wmt news and terminology datasets. @xcite further explored instruction tuning for ne-aware tasks using customized prompts on pre-annotated data. this instruction-based approach offers flexibility for domain-specific scenarios and aligns with recent trends in large language model fine-tuning. ## methodology to address the challenge of ne-aware translation from classical chinese script to vietnamese in the domain of traditional vietnamese medicine, we conducted a series of experiments guided by the following research questions. ## results and analysis in this section, we present the experimental results and analysis of three approaches: standard finetuning, fine-tuning with augmented data, and the hybrid entity masking and replacement method. ## conclusion this work explores the challenge of ne translation from classical chinese script to vietnamese within the domain of traditional medicine. we investigate a dictionary-based data augmentation method and propose a hybrid entity masking and replacement (hemr) approach. to support our evaluation, we introduce a parallel ne translation dataset and a curated dictionary of medicinal material terminologies. experimental results show that our hemr process leads to a significant im-provement in translation accuracy. these findings highlight the potential of combining llms with external dictionaries to enhance translation quality in domain-specific settings such as traditional vietnamese medicine. for future work, we intend to enhance overall translation performance and further expand the medicinal material dictionary to improve coverage and accuracy in domain-specific entity translation.