让翻译团队的修改变成可共享、可追溯的知识，提升协作效率。
DeepTrans Studio: Turning Expert Interventions into Shared Team Knowledge in Agentic Translation Workflows
- 在智能翻译流程中插入干预节点，捕获专家决策。
- 将修正结果存入团队共享记忆，自动传播至后续段落。
- 适合翻译团队、审校人员及AI协作流程设计者使用。
专业翻译通常是团队协作过程：译员、审校和项目管理者需在文档间协调术语、法律效力与责任归属。然而，许多基于大模型的翻译工具将人工修正视为孤立编辑，专家在一个片段或由某成员做出的决策很少被转化为团队可复用的知识。我们提出DeepTrans Studio，一个协同翻译工作空间，使专业人士能够介入智能翻译流程中的特定节点，审查依据，修改AI输出，并将经批准的决策保存至共享团队记忆。演示中，参与者将扮演译员与审校，解决预设的术语与法律模态风险，观察其决策如何传播至下游段落，并在队友的工作区中以可复用范例形式浮现。演示展示了人工智能辅助工作中的人类干预如何转变为共享且可追溯的知识，而非一次性修正。
原文摘要 · Abstract (English)
Professional translation is often a team-based process: translators, reviewers, and project managers must coordinate terminology, legal force, and accountability across documents. Yet many LLM-based translation tools treat human corrections as isolated edits. Expert decisions made in one segment or by one member are rarely captured as reusable knowledge for the rest of the team. We present DeepTrans Studio, a collaborative translation workspace that lets professionals intercept selected nodes in an agentic translation workflow, review evidence, revise AI outputs, and save approved decisions to a shared team memory. During the demo, attendees will role-play translators and reviewers, resolve preset terminology and legal-modal risks, and see how their decisions are propagated to downstream segments and surfaced in a teammate's workspace as reusable precedents. The demo illustrates how human interventions in AI-mediated work can become shared, traceable knowledge rather than one-off corrections.
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