通过问答任务研究用户对语音翻译系统的认知，发现实践和转录能提升判断能力。
Measuring User's Mental Models of Speech Translation in Human-AI Collaboration
- 用跨语言问答测试用户对翻译错误的预判能力
- 有源语言基础的用户经练习后能更好识别错误
- 提供语音转写文本有助于形成更准确的认知模型
数以百万计的人每天使用机器翻译工具，但对其功能边界认知仍不明确。本文基于跨语言问答框架，研究用户对语音翻译系统的心智模型：用户需判断机器翻译结果是否可直接使用，或需专业重译以回答外语文本中的问题。通过分析不同翻译质量下用户行为与准确率变化，发现用户可通过表面错误线索逐步建立更精准的认知模型，尤其在具备源语言知识且经过实践后表现更优。此外，提供语音转写文本显著促进心智模型发展。结果表明，跨语言问答是研究机器翻译心智模型的有效下游任务，有助于深化对人机协作的理解。
原文摘要 · Abstract (English)
Millions of people use machine translation (MT) tools daily, yet little is known about their perception of what systems can and cannot do. This paper studies users' mental models of speech translation systems through a new framework based on cross-lingual question answering, where users either accept MT output or request professional re-translation to answer questions based on the information presented in a foreign language. By analyzing user behavior and accuracy trends across varying translation qualities, we examine to what extent they can predict where the system is likely to be wrong, and how this mental model evolves. Users develop stronger mental models with practice, especially when they have some knowledge of the source language, primarily by relying on surface-level error cues. Moreover, providing speech transcriptions can help users develop better mental models. Our results show the promise of cross-lingual question answering as a downstream task for studying MT mental models and advancing our understanding of human-AI collaboration.
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