Bridging Ancient & Modern: AI's Annotation Approach for Classical Chinese-Japanese Translation
By Zilong Li, Jie Cao
Published on November 10, 2025| Vol. 1, Issue No. 1
Content Source
This is a curated briefing. The original article was published on cs.CL updates on arXiv.org.
Summary
This research investigates a computational approach to translating Classical Chinese into Japanese, mimicking ancient annotation practices. Facing a low-resource problem, the study frames this process as sequence tagging tasks, leveraging an LLM-based annotation pipeline and a newly constructed dataset. Key findings indicate that auxiliary Chinese NLP tasks significantly improve training for sequence tagging in low-resource settings. While Large Language Models (LLMs) excel at direct machine translation for this content, they struggle with character-level annotation, suggesting that the proposed annotation-based method can effectively complement LLM capabilities.
Why It Matters
This work isn't just about translating ancient texts; it's a blueprint for a more nuanced approach to AI-powered language processing. It challenges the prevailing "LLMs do it all" narrative by exposing their blind spots in structured annotation tasks and champions a hybrid strategy. For AI professionals, this signals a future where integrating domain-specific techniques and structured intermediate representations with powerful generative models will unlock greater accuracy, explainability, and efficiency, particularly in challenging low-resource or highly specialized domains. The study also highlights innovative methods for dataset generation in low-resource contexts and underscores the critical role of specialized NLP tasks in boosting overall system performance, moving us closer to truly intelligent and interpretable language AI.