Despite the widespread use of automatic AI translation systems in daily language tasks, professional translation remains crucial in domain-specific and high-stakes scenarios. Yet professional translators rarely rely on these systems in their everyday practice due to a lack of detailed support for the translation process, matching professional styles, and accountability for the final outcome. To bridge the gap, we present CHORUS, a mixed-initiative translation system that supports the translation process and personal style as translators work. A formative study found that incorporating MQM theory may be beneficial for achieving professional translation, and that the system should adapt to each individual translator's idiosyncratic traits. The final within-subject study with 30 licensed English--Chinese translators found that our system reduced completion time by 33.8\%, lowered translators' cognitive effort, and improved final translation quality using the BLEU and COMET as automatic evaluation metrics. Participants' qualitative analysis also revealed that the system made translation issues easier to inspect, reduced repeated prompting compared to single-agent AI systems, and offered reflections on their habits and traits. Our findings illustrate how multi-agent AI systems can be designed to support expert workflows and their potential for professional use.

翻译：尽管自动AI翻译系统在日常语言任务中已广泛使用，但在专业领域及高风险场景中，人工翻译依然至关重要。然而，由于缺乏对翻译过程、匹配专业风格以及最终结果问责的细致支持，专业翻译人员在实际工作中很少依赖这些系统。为弥合这一差距，我们提出CHORUS——一种支持翻译过程并适应译者个人风格的混合主动式翻译系统。前期形成性研究发现，融合MQM理论可能有助于实现专业翻译，且系统应适应每位译者独特的个体特质。最终针对30名持证英汉译员的被试内实验表明，本系统将任务完成时间缩短33.8%，降低译员认知负荷，并通过BLEU和COMET自动评估指标提升最终翻译质量。参与者的定性分析也显示，与单智能体AI系统相比，本系统使翻译问题更易审查，减少重复提示次数，并为译者提供关于自身习惯与特质的反思。我们的研究结果揭示了多智能体AI系统如何被设计以支持专家工作流程及其在专业领域的应用潜力。