MULTITURNCLEANUP: A Benchmark for Multi-Turn Spoken Conversational Transcript Cleanup

被引:0
|
作者
Shen, Hua [1 ,2 ]
Zayats, Vicky [2 ]
Rocholl, Johann C. [2 ]
Walker, Daniel D. [2 ]
Padfield, Dirk [2 ]
机构
[1] Univ Michigan, Ann Arbor, MI 48109 USA
[2] Google Res, Mountain View, CA 94043 USA
来源
2023 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP 2023) | 2023年
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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(1). 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.
引用
收藏
页码:9895 / 9903
页数:9
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