@inproceedings{guo-etal-2023-cs2w,
    title = "{CS}2{W}: A {C}hinese Spoken-to-Written Style Conversion Dataset with Multiple Conversion Types",
    author = "Guo, Zishan  and
      Yu, Linhao  and
      Xu, Minghui  and
      Jin, Renren  and
      Xiong, Deyi",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2023.emnlp-main.241/",
    doi = "10.18653/v1/2023.emnlp-main.241",
    pages = "3962--3979",
    abstract = "Spoken texts (either manual or automatic transcriptions from automatic speech recognition (ASR)) often contain disfluencies and grammatical errors, which pose tremendous challenges to downstream tasks. Converting spoken into written language is hence desirable. Unfortunately, the availability of datasets for this is limited. To address this issue, we present CS2W, a Chinese Spoken-to-Written style conversion dataset comprising 7,237 spoken sentences extracted from transcribed conversational texts. Four types of conversion problems are covered in CS2W: disfluencies, grammatical errors, ASR transcription errors, and colloquial words. Our annotation convention, data, and code are publicly available at https://github.com/guozishan/CS2W."
}