@inproceedings{li-etal-2024-overview,
    title = "Overview of {E}va{H}an2024: The First International Evaluation on {A}ncient {C}hinese Sentence Segmentation and Punctuation",
    author = "Li, Bin  and
      Chang, Bolin  and
      Xu, Zhixing  and
      Feng, Minxuan  and
      Xu, Chao  and
      Qu, Weiguang  and
      Shen, Si  and
      Wang, Dongbo",
    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-1.27",
    pages = "229--236",
    abstract = "Ancient Chinese texts have no sentence boundaries and punctuation. Adding modern Chinese punctuation to theses texts requires expertise, time and efforts. Automatic sentence segmentation and punctuation is considered as a basic task for Ancient Chinese processing, but there is no shared task to evaluate the performances of different systems. This paper presents the results of the first ancient Chinese sentence segmentation and punctuation bakeoff, which is held at the Third Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) 2024. The contest uses metrics for detailed evaluations of 4 genres of unpublished texts with 11 punctuation types. Six teams submitted 32 running results. In the closed modality, the participants are only allowed to use the training data, the highest obtained F1 scores are respectively 88.47{\%} and 75.29{\%} in sentence segmentation and sentence punctuation. The perfermances on the unseen data is 10 percent lower than the published common data, which means there is still space for further improvement. The large language models outperform the traditional models, but LLM changes the original characters around 1-2{\%}, due to over-generation. Thus, post-processing is needed to keep the text consistancy.",
}

Ancient Chinese texts have no sentence boundaries and punctuation. Adding modern Chinese punctuation to theses texts requires expertise, time and efforts. Automatic sentence segmentation and punctuation is considered as a basic task for Ancient Chinese processing, but there is no shared task to evaluate the performances of different systems. This paper presents the results of the first ancient Chinese sentence segmentation and punctuation bakeoff, which is held at the Third Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) 2024. The contest uses metrics for detailed evaluations of 4 genres of unpublished texts with 11 punctuation types. Six teams submitted 32 running results. In the closed modality, the participants are only allowed to use the training data, the highest obtained F1 scores are respectively 88.47{\%} and 75.29{\%} in sentence segmentation and sentence punctuation. The perfermances on the unseen data is 10 percent lower than the published common data, which means there is still space for further improvement. The large language models outperform the traditional models, but LLM changes the original characters around 1-2{\%}, due to over-generation. Thus, post-processing is needed to keep the text consistancy.
Li, Bin Chang, Bolin Xu, Zhixing Feng, Minxuan Xu, Chao Qu, Weiguang Shen, Si Wang, Dongbo
Overview of EvaHan2024: The First International Evaluation on Ancient Chinese Sentence Segmentation and Punctuation