CS2W: A Chinese Spoken-to-Written Style Conversion Dataset with Multiple Conversion Types

Zishan Guo, Linhao Yu, Minghui Xu, Renren Jin, Deyi Xiong

Submission Type: Regular Long Paper

Submission Track: Resources and Evaluation

Submission Track 2: Syntax, Parsing and their Applications

Keywords: Spoken-to-Written Style Conversion, Disfluency Detection, Grammatical Error Correction, ASR

TL;DR: A Fine-grained Chinese Spoken-to-Written Style Conversion Dataset with Multiple Conversion Types

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.

Submission Number: 2803