Dial HEALTHDIAL for Advice: 一个用于知识驱动信息检索的多语言多平行口语对话数据集
Dial HEALTHDIAL for Advice: A Multilingual and Multi-Parallel Spoken Dialogue Dataset for Knowledge-Grounded Information Seeking
AI总结：
本文构建了HEALTHDIAL，一个大规模多语言多平行口语对话数据集，用于开发基于检索增强生成的口语对话系统，并揭示了不同语言间的性能差异。
摘要：
创建口语对话数据集在方法论上具有挑战性，当目标是构建大规模多语言多平行数据集时，这些挑战更加突出。本文介绍了HEALTHDIAL，一个用于开发和评估基于检索增强生成（RAG）的口语对话系统的大规模多语言多平行数据集。该数据集包含6,000个信息寻求对话（每种语言1,500个），这些对话基于世界卫生组织（WHO）的可信内容，以及来自四种WHO官方语言（阿拉伯语、中文、英语和西班牙语）的母语者录制的163小时用户语音。每个说话者都标注了人口统计学（如性别、年龄）和社会语言学（如主要语言、原籍地区）变量。我们报告了关键对话任务的基准结果，揭示了不同语言之间（即使是高资源语言）持续存在的性能差异。为支持未来研究，我们发布了该数据集、一个原型系统以及一个用于数据收集和系统评估的工具包。
英文摘要：
Creating spoken dialogue datasets is methodologically challenging, and these challenges are amplified when the goal is to build multilingual, multi-parallel datasets at scale. This work introduces HEALTHDIAL, a large-scale, multilingual, and multi-parallel dataset for developing and evaluating retrieval-augmented generation (RAG)-based spoken dialogue systems. The dataset comprises 6,000 information-seeking dialogues (1,500 per language) grounded in trusted content from the World Health Organization (WHO) and 163 hours of user speech recorded from native speakers of diverse dialects across four official WHO languages: Arabic, Chinese, English, and Spanish. Each speaker is annotated with demographic (e.g., gender, age) and sociolinguistic (e.g., primary language, region of origin) variables. We report benchmark results across key dialogue tasks, which reveal consistent performance disparities across languages, even among high-resource ones. To support future research, we release the dataset, a prototype system, and a toolkit for data collection and system evaluation.
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。
