生成式AI在翻译方面的表现如何？

11月8日至10日，语言服务创新发展国际（厦门）论坛暨中国翻译协会翻译服务委员会2024年会在厦门举办。Nimdzi Insights公司CEO Josef Kubovsky在参会后发表了三篇长文，展示了其对语言服务行业的深度思考与见解。本文为第一篇，聚焦时下大热的生成式人工智能在本地化中的应用。

▲ 语言服务创新发展国际（厦门）论坛暨中国翻译协会翻译服务委员会2024年会会场

Generative AI in Localization:

Bridging Innovation and Cultural Sensitivity 

生成式人工智能在本地化中的应用：

在创新与文化敏感之间架起桥梁

Over the past two weeks, I’ve had the privilege of traveling across China, culminating in attending the TAC LSC 2024 in Xiamen. Organized under the leadership of Frank Zhonghe Wei and his dedicated team, the event was a masterclass in bringing together innovation, culture, and collaboration in the language service industry. From owners of some of China’s largest translation companies to specialized boutique firms, the conference drew an impressive array of professionals.

在过去的两周里，我有幸走访中国，并参加了在厦门举办的“语言服务创新发展国际（厦门）论坛暨中国翻译协会翻译服务委员会2024年会”（TAC LSC 2024）会议。由韦忠和及其精艺达翻译团队承办的此次会议，堪称一场在语言服务行业中融合创新、文化和协作的精彩盛会。从中国大型翻译公司到专注于细分领域的精品公司，此次会议吸引了众多专业人士。

The presentations and panels were nothing short of enlightening. They delves deep into the technologies shaping our industry and the challenges that lie ahead. I was particularly intrigued by discussions surrounding generative AI, a theme that reverberated across the three panels exploring current trends and challenges. These panels tackled topics ranging from technological disruption to the nuanced interplay of culture in machine-generated translations.

会议中的演讲和讨论让人深受启发，深入探讨了塑造我们行业的技术以及未来的挑战。让我尤为感兴趣的是围绕生成式人工智能的讨论，这一主题在探索当前趋势和挑战的三个专题讨论中多次被提及。这些讨论涵盖了从技术变革到文化与机器翻译的微妙平衡等议题。

One of the standout themes at TAC LSC 2024 was the role of generative AI in localization. Companies like iFlytek and Huawei showcased how their large language models (LLMs) are advancing localization workflows, particularly for Chinese-to-English and English-to-Chinese translations. These tools excel at:

TAC LSC 2024的一大主题是生成式人工智能在本地化中的作用。像科大讯飞和华为这样的公司展示了他们的大语言模型（LLMs）如何推动中英互译流程的发展。这些工具在以下方面表现出色：

- Speed and Scalability: Processing vast datasets and generating initial drafts faster than ever before.

- 速度与规模化：处理海量数据集并生成初稿的速度前所未有地快。

- Consistency in Terminology: Ensuring that complex technical terms are accurately and consistently translated across projects.

- 术语一致性：确保复杂技术术语在项目中得到准确且一致的翻译。

Yet, the limitations of AI were evident in discussions. As highlighted in the “Harnessing Generative AI for Translation” panel, idiomatic expressions and cultural nuances remain significant challenges. For example, translating “不入虎穴焉得虎子” (“Without entering the tiger’s den, how can one get the tiger’s cub?”) literally into English risks losing its metaphorical richness about bravery and risk-taking.

然而，人工智能的局限性也在讨论中显露无疑。正如“利用生成式人工智能进行翻译”的专题中提到的那样，成语表达和文化细微差异仍然是重大挑战。例如，将“不到虎穴焉得虎子”字面翻译成英文会失去其关于勇气和冒险的隐喻意义。

Cultural adaptation remains a critical weakness in AI-driven localization. At TAC LSC 2024, a recurring concern was AI’s inability to grasp deeper cultural meanings, leading to mechanical or inappropriate translations for target audiences.

文化适应性仍然是人工智能驱动的本地化的一大短板。在TAC LSC 2024会议中，人工智能无法掌握深层次文化意义的能力限制，导致翻译内容机械化或不适合目标受众的担忧多次被提及。

One session illustrated this vividly with a case study of a Chinese drama localized for Western streaming platforms. While AI efficiently handled the bulk of the translations, scenes depicting traditional Chinese customs were mistranslated or stripped of their emotional depth. The machine’s literal interpretations failed to convey the symbolic resonance of concepts like “家” (home) in the Chinese cultural context, which encompasses familial bonds and heritage.

其中一个案例研究生动地展示了这一问题。一部中国电视剧在为西方流媒体平台进行本地化时，虽然人工智能有效地完成了大部分翻译工作，但涉及中国传统习俗的场景却被误译或失去了情感深度。机器的字面解释无法传达像“家”这样的概念在中国文化中所包含的家庭纽带与传统内涵。

Speakers emphasized the growing need for hybrid workflows—integrating AI efficiency with human expertise—to bridge this gap.

演讲者强调，需要越来越多的混合工作流来弥合这一差距，即整合人工智能的高效性与人类专家的专业性。

The concept of hybrid workflows was central to discussions, particularly during the “Emerging Workflow Models in Translation” panel. These workflows involve:

混合工作流的概念在讨论中占据了核心地位，尤其是在“翻译中的新型工作流模型”专题中。这些工作流包括：

- AI for First Drafts: Leveraging generative AI to produce preliminary translations quickly and at scale.

- 人工智能生成初稿：利用生成式人工智能快速大规模地产生初稿。

- Human Post-Editing: Involving linguists and cultural consultants to refine AI outputs, ensuring contextual and cultural fidelity.

- 人工后期编辑：让语言学家和文化顾问对人工智能的输出进行优化，确保语境和文化的准确性。

- Collaborative Platforms: Using advanced tools to streamline communication and revisions between AI and human teams.

- 协作平台：利用先进工具优化人工智能与人类团队之间的沟通与修订。

A multinational gaming company shared how they localized a Chinese role-playing game for European audiences using a hybrid approach. Generative AI completed over 100,000 lines of dialogue in just days. Human editors then tailored cultural elements, replacing references to Chinese mythology with Western equivalents that felt authentic to the new audience. The result was a seamless localization that retained the game’s core essence while resonating with European players.

一家跨国游戏公司分享了如何使用混合方法为欧洲观众本地化一款中国角色扮演游戏的案例。生成式人工智能在几天内完成了超过10万行的对话翻译。人类编辑随后对文化元素进行了调整，用西方神话替代了中国神话的引用，使其更贴近目标受众。最终的成果是一个无缝的本地化版本，既保留了游戏的核心精髓，又能引起欧洲玩家的共鸣。

It was inspiring to hear perspectives from industry leaders like Arancha Caballero , President of ANETI, and Hélène Pielmeier , Senior Analyst and Director of LSP Services at CSA, who brought a global lens to the discussions. Arancha’s presentation on the State of the Language Industry in Spain and Europe highlighted parallels between European and Chinese localization challenges, particularly around pricing pressures and adapting to AI technologies.

聆听来自行业领袖的观点令人振奋，例如ANETI主席阿兰查·卡瓦列罗和CSA  Research高级分析师兼LSP服务总监海琳·皮尔迈耶。他们为讨论带来了全球化的视角。阿兰查的演讲探讨了西班牙和欧洲语言行业的现状，并突出了欧洲与中国在本地化挑战上的相似之处，特别是在价格压力和适应人工智能技术方面。

Hélène’s talk on LSP Transformations in the Post-Localization Era offered actionable insights into how companies can evolve their business models, emphasizing agility and client-centric innovation. These presentations underscored the interconnected nature of our global industry.

海琳关于“后本地化时代LSP转型”的演讲提供了切实可行的见解，阐述了公司如何通过灵活性和以客户为中心的创新调整其业务模式。这些演讲强调了我们全球化行业的紧密关联。

From the presentations and panels at TAC LSC 2024, several key strategies emerged for tackling the challenges of generative AI in localization:

从TAC LSC 2024的演讲和讨论中，可以总结出几条应对生成式人工智能在本地化中挑战的关键策略：

- Invest in Domain-Specific Training Data: LSPs must develop datasets tailored to their target industries, improving AI accuracy for specialized content.

- 投资领域特定的训练数据：LSP需要开发针对目标行业的专用数据集，以提高人工智能对专业内容的准确性。

- Enhance Translator Training: Equip linguists with machine translation post-editing (MTPE) skills to maximize the potential of hybrid workflows.

- 加强译员培训：为语言学家提供机器翻译后编辑（MTPE）技能培训，以最大化混合工作流的潜力。

- Foster Collaboration: Build partnerships across the ecosystem, from AI developers to cultural consultants, to ensure robust and scalable workflows.

- 促进协作：与人工智能开发者和文化顾问建立伙伴关系，以确保工作流的健全性和可扩展性。

TAC LSC 2024 was a powerful reminder of the balance we must strike between technological innovation and cultural preservation. Generative AI has undeniably transformed localization workflows, but its limitations in cultural sensitivity highlight the enduring value of human expertise.

TAC LSC 2024提醒我们在技术创新与文化传承之间找到平衡的重要性。生成式人工智能无疑已经改变了本地化工作流，但其在文化敏感性上的局限性凸显了人类专业知识的持久价值。

I left Xiamen inspired by the conversations, case studies, and connections. The language service industry is entering a transformative phase, and events like TAC LSC 2024 show that collaboration, curiosity, and cultural awareness will guide us through this evolution.

离开厦门时，我为这些对话、案例研究和建立的联系感到振奋。语言服务行业正在进入一个变革阶段，而TAC LSC 2024表明，协作、好奇心和文化意识将引领我们迈向这一演变。