构建会议话题相关性数据集，评估对话是否跑题。
Topic-Conversation Relevance (TCR) Dataset and Benchmarks
- 构建覆盖多领域、多风格的会议数据集
- 含1500场会议、2200万字文本、超1.5万话题
- 适合研究会议智能分析与自动评估的团队
工作场合会议对组织协作至关重要，但大量会议被认为效率低下。为提升会议效果，通过判断对话是否紧扣主题，我们构建了涵盖多种领域和会议风格的专题-对话相关性（TCR）数据集。TCR包含1500场独立会议、2200万字转录文本及超过15000个会议主题，数据来源包括新采集的语音打断会议（SIM）数据与现有公开数据集。同时，开源脚本用于生成合成会议或对TCR数据进行增强，以提升数据多样性。针对每个数据源，使用GPT-4创建基准，评估模型在理解转录内容与话题相关性方面的准确率。
原文摘要 · Abstract (English)
Workplace meetings are vital to organizational collaboration, yet a large percentage of meetings are rated as ineffective. To help improve meeting effectiveness by understanding if the conversation is on topic, we create a comprehensive Topic-Conversation Relevance (TCR) dataset that covers a variety of domains and meeting styles. The TCR dataset includes 1,500 unique meetings, 22 million words in transcripts, and over 15,000 meeting topics, sourced from both newly collected Speech Interruption Meeting (SIM) data and existing public datasets. Along with the text data, we also open source scripts to generate synthetic meetings or create augmented meetings from the TCR dataset to enhance data diversity. For each data source, benchmarks are created using GPT-4 to evaluate the model accuracy in understanding transcription-topic relevance.
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