@inproceedings{xia-etal-2024-speado,
    title = "{SPEADO}: Segmentation and Punctuation for {A}ncient {C}hinese Texts via Example Augmentation and Decoding Optimization",
    author = "Xia, Tian  and
      Yu, Kai  and
      Yu, Qianrong  and
      Peng, Xinran",
    editor = "Sprugnoli, Rachele  and
      Passarotti, Marco",
    booktitle = "Proceedings of the Third Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) @ LREC-COLING-2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lt4hala-1.32",
    pages = "256--260",
    abstract = "The SPEADO model for sentence segmentation and punctuation tasks in ancient Chinese texts is proposed, which incorporates text chunking and MinHash indexing techniques to realise example argumentation. Additionally, decoding optimization strategies are introduced to direct the attention of the LLM model towards punctuation errors and address the issue of uncontrollable output. Experimental results show that the F1 score of the proposed method exceeds the baseline model by 14.18{\%}, indicating a significant improvement in performance.",
}

The SPEADO model for sentence segmentation and punctuation tasks in ancient Chinese texts is proposed, which incorporates text chunking and MinHash indexing techniques to realise example argumentation. Additionally, decoding optimization strategies are introduced to direct the attention of the LLM model towards punctuation errors and address the issue of uncontrollable output. Experimental results show that the F1 score of the proposed method exceeds the baseline model by 14.18{\%}, indicating a significant improvement in performance.
Xia, Tian Yu, Kai Yu, Qianrong Peng, Xinran
SPEADO: Segmentation and Punctuation for Ancient Chinese Texts via Example Augmentation and Decoding Optimization