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        <title>MultiTurnCleanup: A Benchmark for Multi-Turn Spoken Conversational Transcript Cleanup</title>
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        <namePart type="given">Hua</namePart>
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            <namePart type="given">Kalika</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T MultiTurnCleanup: A Benchmark for Multi-Turn Spoken Conversational Transcript Cleanup
%A Shen, Hua
%A Zayats, Vicky
%A Rocholl, Johann
%A Walker, Daniel
%A Padfield, Dirk
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F shen-etal-2023-multiturncleanup
%X 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.
%R 10.18653/v1/2023.emnlp-main.613
%U https://aclanthology.org/2023.emnlp-main.613
%U https://doi.org/10.18653/v1/2023.emnlp-main.613
%P 9895-9903

Markdown (Informal)

[MultiTurnCleanup: A Benchmark for Multi-Turn Spoken Conversational Transcript Cleanup](https://aclanthology.org/2023.emnlp-main.613) (Shen et al., EMNLP 2023)

• MultiTurnCleanup: A Benchmark for Multi-Turn Spoken Conversational Transcript Cleanup (Shen et al., EMNLP 2023)

ACL

• Hua Shen, Vicky Zayats, Johann Rocholl, Daniel Walker, and Dirk Padfield. 2023. MultiTurnCleanup: A Benchmark for Multi-Turn Spoken Conversational Transcript Cleanup. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 9895–9903, Singapore. Association for Computational Linguistics.