打造实时标注播客的工具，助力多语言事实核查研究。
Annotation Tool and Dataset for Fact-Checking Podcasts
- 支持播客播放时实时标注关键陈述与上下文错误。
- 结合Whisper转录与众包标注，构建高质量多语言数据集。
- 适合多语言事实核查、自然语言处理研究者使用。
播客是网络上流行的媒体形式，内容多样且多语种，常包含未经验证的陈述。事实核查播客极具挑战性，需完成转录、标注和陈述验证，并保留口语内容的上下文细节。我们开发的工具通过在播放过程中实时标注，使用户可边听边标记关键陈述、陈述片段及上下文错误。该方法融合OpenAI Whisper等先进转录模型与众包标注，构建高质量数据集，用于微调XLM-RoBERTa等多语言Transformer模型，以实现陈述检测与立场分类。我们还发布了标注后的播客文本及样例标注，并提供初步实验结果。
原文摘要 · Abstract (English)
Podcasts are a popular medium on the web, featuring diverse and multilingual content that often includes unverified claims. Fact-checking podcasts is a challenging task, requiring transcription, annotation, and claim verification, all while preserving the contextual details of spoken content. Our tool offers a novel approach to tackle these challenges by enabling real-time annotation of podcasts during playback. This unique capability allows users to listen to the podcast and annotate key elements, such as check-worthy claims, claim spans, and contextual errors, simultaneously. By integrating advanced transcription models like OpenAI's Whisper and leveraging crowdsourced annotations, we create high-quality datasets to fine-tune multilingual transformer models such as XLM-RoBERTa for tasks like claim detection and stance classification. Furthermore, we release the annotated podcast transcripts and sample annotations with preliminary experiments.
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