MultiTurnCleanup：面向多轮口语对话转录清理的基准
MultiTurnCleanup: A Benchmark for Multi-Turn Spoken Conversational Transcript Cleanup
AI总结：
本文针对口语对话转录中跨轮次不连续现象，提出Multi-Turn Cleanup任务并构建MultiTurnCleanup1数据集，设计标注模式并进行数据分析，同时以两种建模方法建立实验基准。
摘要：
当前的不流畅检测模型聚焦于来自单一说话人的独立话语。然而，口语对话转录中的大量不连续现象跨多个轮次出现，妨碍了人类可读性及下游自然语言处理任务的性能。本研究通过提出一个创新的面向口语对话转录的多轮清理（Multi-Turn Cleanup）任务，并收集新数据集 MultiTurnCleanup1 来应对这些现象。我们设计了一套数据标注模式以收集高质量数据集，并提供广泛的数据分析。此外，我们利用两种建模方法进行实验评估，作为未来研究的基准。
英文摘要：
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, hampering human readability and the performance of downstream NLP tasks. This study addresses these phenomena by proposing an innovative Multi-Turn Cleanup task for spoken conversational transcripts and collecting a new dataset, MultiTurnCleanup1. 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
