多语言对话语料库及误解检测
Corpus of Cross-lingual Dialogues with Minutes and Detection of Misunderstandings
AI总结：
本文提出一个多语言对话语料库及自动检测误解的方法，通过手动标注和模型测试，验证了Gemini模型在识别误解方面的性能。
摘要：
语音处理和翻译技术有潜力帮助没有共同语言的个体进行交流。为了评估此类任务的自动系统，需要一个多功能且现实的评估语料库。因此，我们创建并呈现了一个由没有共同语言的个体通过自动实时语音翻译进行的多语言对话语料库。该语料库包含5小时的语音录音，包含12种原始语言的自动语音识别和黄金转录，以及自动和校正的英文翻译。为了研究多语言摘要，该语料库还包含会议的书面摘要（会议记录）。此外，我们提出了自动检测误解的方法。为了概述此任务及其复杂性，我们尝试量化多语言会议中的误解。我们手动标注误解，并测试当前大语言模型自动检测误解的能力。结果表明，Gemini模型能够识别包含误解的文本片段，召回率为77%，精确率为47%。
英文摘要：
Speech processing and translation technology have the potential to facilitate meetings of individuals who do not share any common language. To evaluate automatic systems for such a task, a versatile and realistic evaluation corpus is needed. Therefore, we create and present a corpus of cross-lingual dialogues between individuals without a common language who were facilitated by automatic simultaneous speech translation. The corpus consists of 5 hours of speech recordings with ASR and gold transcripts in 12 original languages and automatic and corrected translations into English. For the purposes of research into cross-lingual summarization, our corpus also includes written summaries (minutes) of the meetings. Moreover, we propose automatic detection of misunderstandings. For an overview of this task and its complexity, we attempt to quantify misunderstandings in cross-lingual meetings. We annotate misunderstandings manually and also test the ability of current large language models to detect them automatically. The results show that the Gemini model is able to identify text spans with misunderstandings with recall of 77% and precision of 47%.
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。
