面向推理阶段的隐私保护机器翻译：一个新任务和基准
Towards Privacy-Preserving Machine Translation at the Inference Stage: A New Task and Benchmark
AI总结：
本文提出隐私保护机器翻译任务，设计了三个基准数据集和评估指标，旨在保护文本中隐私信息，填补翻译领域隐私保护研究的空白。
摘要：
当前在线翻译服务需要将用户文本发送至云端服务器，当文本包含敏感信息时，存在隐私泄露的风险，这限制了隐私敏感场景下的应用。为缓解此风险，引入针对翻译模型推理阶段的隐私保护机制是可行的。然而，与自然语言处理其他子领域如文本分类和摘要相比，机器翻译研究社区对推理阶段的隐私保护探索有限。目前缺乏明确的隐私保护任务、专用评估数据集和指标，以及参考基准方法。本文提出新的
英文摘要：
Current online translation services require sending user text to cloud servers, posing a risk of privacy leakage when the text contains sensitive information. This risk hinders the application of online translation services in privacy-sensitive scenarios. One way to mitigate this risk for online translation services is introducing privacy protection mechanisms targeting the inference stage of translation models. However, compared to subfields of NLP like text classification and summarization, the machine translation research community has limited exploration of privacy protection during the inference stage. There is no clearly defined privacy protection task for the inference stage, dedicated evaluation datasets and metrics, and reference benchmark methods. The absence of these elements has seriously constrained researchers' in-depth exploration of this direction. To bridge this gap, this paper proposes a novel "Privacy-Preserving Machine Translation" (PPMT) task, aiming to protect the private information in text during the model inference stage. For this task, we constructed three benchmark test datasets, designed corresponding evaluation metrics, and proposed a series of benchmark methods as a starting point for this task. The definition of privacy is complex and diverse. Considering that named entities often contain a large amount of personal privacy and commercial secrets, we have focused our research on protecting only the named entity's privacy in the text. We expect this research work will provide a new perspective and a solid foundation for the privacy protection problem in machine translation.
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。
