CS2W: A Chinese Spoken-to-Written Style Conversion Dataset with Multiple Conversion Types
Zishan Guo, Linhao Yu, Minghui Xu, Renren Jin, Deyi Xiong
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.
BibTeX
@inproceedings{
guo2023csw,
title={{CS}2W: A Chinese Spoken-to-Written Style Conversion Dataset with Multiple Conversion Types},
author={Zishan Guo and Linhao Yu and Minghui Xu and Renren Jin and Deyi Xiong},
booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
url={https://openreview.net/forum?id=kOhxudaIEj}
}