EMNLP 2023short main0 citations
MultiTurnCleanup: A Benchmark for Multi-Turn Spoken Conversational Transcript Cleanup
Hua Shen, Vicky Zayats, Johann C Rocholl, Daniel David Walker, Dirk Padfield
Abstract
Current disfluency detection models focus on individual utterances each from a single speaker. However, numerous discontinuity phenomena in spoken conversational transcripts occur across multiple turns, which can not be identified by disfluency detection models. This study addresses these phenomena by proposing an innovative Multi-Turn Cleanup task for spoken conversational transcripts and collecting a new dataset, MultiTurnCleanup. We design a data labeling schema to collect the high-quality dataset and provide extensive data analysis. Furthermore, we leverage two modeling approaches for experimental evaluation as benchmarks for future research.
Multi-Turn Spoken ConversationsCrowdsourcingTranscript CleanupDisfluency Detection
BibTeX
@inproceedings{
shen2023multiturncleanup,
title={MultiTurnCleanup: A Benchmark for Multi-Turn Spoken Conversational Transcript Cleanup},
author={Hua Shen and Vicky Zayats and Johann C Rocholl and Daniel David Walker and Dirk Padfield},
booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
url={https://openreview.net/forum?id=OVLnZliSHs}
}