Case Study: How Cisco Networking Academy Leveraged AI Post-Editing To Accelerate Multilingual Content Delivery
案例研究：思科网络技术学院如何利用AI译后编辑加速多语言内容管理
In today’s global marketplace, the demand for fast, affordable multilingual content delivery has never been higher. However, for many organizations, traditional translation and localization methods have caused bottlenecks and proved too costly to handle all their content, resulting in a backlog of content awaiting translation. But all that has changed.
在当今全球市场，企业对快速、低成本的多语言内容管理需求空前高涨。然而，传统的翻译与本地化方法不仅效率低下，形成瓶颈，而且成本高昂，导致大量内容积压待译。但现在，这一切都已改变。
Enter Lionbridge, the leader in language solutions, and Cisco Systems, a technology leader with a social mission. With Lionbridge’s help, Cisco Networking Academy (Cisco’s technology education initiative) leveraged the transformative power of AI post-editing to reach new levels of content delivery speed, scale, and cost-effectiveness.
进入语言解决方案领导者北京莱博智环球科技有限公司（Lionbridge）和承担社会使命的技术领导者思科（Cisco Systems, Inc.）。在北京莱博智环球科技有限公司的帮助下，思科网络学院 (思科的技术教育计划) 利用AI后期编辑的变革力量，将内容交付速度、规模和成本效益提高到新的水平。
How does Lionbridge’s automated post-editing solution work? What did Cisco Networking Academy accomplish? Can your enterprise benefit from this approach to overcome its translation challenges? Find out in our case study.
莱博智的自动化后期编辑解决方案如何工作？思科网络技术学院取得了哪些成就？您的企业能否从这种方法中受益，以克服其翻译挑战？在我们的案例研究中找出答案。
What Makes Lionbridge’s Solution Stand Out?
是什么让莱博智的解决方案脱颖而出？
While Machine Translation (MT) is a familiar tool, Lionbridge’s translation solution uses a multilayered approach, with the global content lifecycle orchestrated by its AI-first platform, Lionbridge Aurora AI™.
虽然机器翻译 (MT) 是一种熟悉的工具，但莱博智的翻译解决方案使用多层方法，其AI优先平台Lionbridge Aurora AI协调了全球内容生命周期。
The solution uses the best Neural Machine Translation (NMT) engines for initial translation and frontier Large Language Models (LLMs) for post-editing the NMT output. By incorporating robust linguistic assets — such as Translation Memories (TMs), glossaries, and terminology management — along with varying levels of human-in-the-loop oversight, you get a workflow that’s both agile and reliable.
该解决方案使用最佳的神经机器翻译 (NMT) 引擎进行初始翻译，并使用前沿大型语言模型 (LLMS) 对NMT输出进行译后编辑。通过整合强大的语言资产 (如翻译记忆 (TM) 、术语或词汇存储) 以及不同级别的人工循环监督，您可以获得既灵活又可靠的工作流程。
Quality evaluation options range from no human post-edit to full human post-edit, with additional offerings in between; the content profile, the customer’s goals, and the budget drive the ultimate decision.
质量评估选项的范围从没有人工编辑到完全人工编辑，中间还有其他产品; 内容概况、客户的目标和预算决定最终决策。
What Are the Limitations of AI Post-Editing?
AI译后编辑的局限性是什么？
Although AI post-editing is a breakthrough, it isn’t perfect. Low-resource languages, cultural details, and domain-specific terminology can challenge even the best models. Also, generative AI can produce false or hallucinated information. That’s why human validation and continuous prompting are crucial to overcoming these shortcomings and making sure content connects with all audiences.
虽然人工智能译后编辑是一个突破，但它并不完美。低资源的语言，文化细节和特定于领域的术语甚至可以挑战最好的模型。此外，生成性AI可以产生虚假或幻觉信息。这就是为什么人类验证和持续提示对于克服这些缺点并确保内容与所有受众联系至关重要的原因。
What Does Cisco Have to Say About Its Experiences With AI Post-Editing?
关于AI译后编辑的经验，思科有什么要说的？
Cisco’s GTS program manager offered insights into automated post-editing during our webinar, “Can AI Post-Edit?” Watch the recording to learn more.
思科的GTS项目经理在我们的网络研讨会 “AI可以译后编辑吗？” 期间提供了有关自动译后编辑的见解。观看录音以了解更多信息。
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