@inproceedings{wang-etal-2024-sentence,
    title = "Sentence Segmentation and Punctuation for Ancient Books Based on Supervised In-context Training",
    author = "Wang, Shiquan  and
      Fu, Weiwei  and
      Li, Mengxiang  and
      He, Zhongjiang  and
      Li, Yongxiang  and
      Fang, Ruiyu  and
      Guan, Li  and
      Song, Shuangyong",
    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-1.31",
    pages = "251--255",
    abstract = "This paper describes the participation of team {``}TeleAI{''} in the third International Chinese Ancient Chinese Language Information Processing Evaluation (EvalHan24). The competition comprises a joint task of sentence segmentation and punctuation, categorized into open and closed tracks based on the models and data used. In the final evaluation, our system achieved significantly better results than the baseline. Specifically, in the closed-track sentence segmentation task, we obtained an F1 score of 0.8885, while in the sentence punctuation task, we achieved an F1 score of 0.7129.",
}

This paper describes the participation of team {``}TeleAI{''} in the third International Chinese Ancient Chinese Language Information Processing Evaluation (EvalHan24). The competition comprises a joint task of sentence segmentation and punctuation, categorized into open and closed tracks based on the models and data used. In the final evaluation, our system achieved significantly better results than the baseline. Specifically, in the closed-track sentence segmentation task, we obtained an F1 score of 0.8885, while in the sentence punctuation task, we achieved an F1 score of 0.7129.
Wang, Shiquan Fu, Weiwei Li, Mengxiang He, Zhongjiang Li, Yongxiang Fang, Ruiyu Guan, Li Song, Shuangyong
Sentence Segmentation and Punctuation for Ancient Books Based on Supervised In-context Training