arXiv cs.CL· Jen Wilson, Vipasha Bansal, William D. Lewis·· 12 小时前AI 评分26
面向全体民众沟通：紧急警报与警告的机器翻译评估
Communicating with the Entire Population: Machine Translation of Emergency Alerts and Warnings
AI 导读
研究对全美应急管理人员开展调查，了解其工作流程与操作挑战，并据此评估商用翻译工具在紧急警报与警告场景中的表现。论文揭示了这些自动化输出的实用性与局限，就何时及如何安全使用商用翻译工具提供实践指导。
正文 · 原文
Abstract:It is essential that we communicate with the entirety of an affected population during a crisis, including those who do not speak a region's dominant language(s). Commercial Translation tools offer a tempting solution to broaden access and can provide a lifeline when no other resources are available. However, Emergency response managers often operate in the dark regarding the quality and adequacy of these automated outputs. In this paper, we conduct a nationwide survey of emergency managers to understand their current workflows, perspectives, and operational challenges. We then evaluate commercial translation tools against these needs, shedding light on their utility and offering practical guidance on how and when these tools can be safely used in emergency response.
| Comments: | In Proceedings of the Language Technologies for Crisis Preparedness and Response (LT4CPR Workshop), Hengqin, Zhuhai, China. Association for Computational Linguistics. Nov 2026 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.32842 [cs.CL] |
| (or arXiv:2610.32842v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.32842 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jen Wilson [view email]
[v1]
Fri, 23 Oct 2026 16:17:37 UTC (220 KB)
来源：arXiv cs.CL · arxiv.org