@inproceedings{zheng-yu-2025-incorporating,
    title = "Incorporating Lexicon-Aligned Prompting in Large Language Model for {T}angut{--}{C}hinese Translation",
    author = "Zheng, Yuxi  and
      Yu, Jingsong",
    editor = "Anderson, Adam  and
      Gordin, Shai  and
      Li, Bin  and
      Liu, Yudong  and
      Passarotti, Marco C.  and
      Sprugnoli, Rachele",
    booktitle = "Proceedings of the Second Workshop on Ancient Language Processing",
    month = may,
    year = "2025",
    address = "The Albuquerque Convention Center, Laguna",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.alp-1.16/",
    pages = "127--136",
    ISBN = "979-8-89176-235-0",
    abstract = "This paper proposes a machine translation approach for Tangut{--}Chinese using a large language model (LLM) enhanced with lexical knowledge. We fine-tune a Qwen-based LLM using Tangut{--}Chinese parallel corpora and dictionary definitions. Experimental results demonstrate that incorporating single-character dictionary definitions leads to the best BLEU-4 score of 72.33 for literal translation. Additionally, applying a chain-of-thought prompting strategy significantly boosts free translation performance to 64.20. The model also exhibits strong few-shot learning abilities, with performance improving as the training dataset size increases. Our approach effectively translates both simple and complex Tangut sentences, offering a robust solution for low-resource language translation and contributing to the digital preservation of Tangut texts."
}