The Future of Interpretation Integrates AI with a Human…


2023-11-20 18:00 GALA


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Being able to communicate with multicultural, multilingual consumers from around the world has never been more important. Businesses serve a more diverse clientele than ever, and these consumers expect personalized experiences that are smooth, seamless, and technologically advanced. Our team of language experts believes that Artificial Intelligence (AI) and its transformative technology will play a pivotal role in creating more personalized, efficient, and culturally sensitive communication solutions. In this article we’ll examine the pros and cons of AI in interpretation and share lessons our team has learned from integrating AI with our services and other technologies such as interactive voice response (IVR) to better serve our customers. The Pros of AI in Interpretation The hype is real: AI can improve the quality of over-the-phone interpretation services in a number of ways. Here are a few positive changes we’re noticing: AI has the potential to make interpretation much more efficient. For example, improved call flows can save time for our clients and their customers. They also improve the customer experience, especially if it means they don’t have to wait as long. AI also offers the potential to scale up the services you offer or the number of customers you serve without increasing the number of staff members. This means providing a level of service that was previously impossible due to cost or personnel constraints. For example, conferences are currently able to provide simultaneous interpretation in many different languages by utilizing AI-powered real-time interpretation with AI-generated voices, something that would have been cost-prohibitive in the past. ULG’s resident MT expert Blanca Vidal described other ways AI can help us grow with our customers in a recent interview with Authority Magazine: “We can create style guides that mimic human language that can connect remote employees, provide healthcare information to multilingual patients or other applications we’re not thinking of yet, all with the touch of a button.” With AI, we can analyze many different types of data to identify customer needs and trends across cultures. These cultural insights help us to provide more personalized and efficient service, constantly adapting to meet the evolving needs of our clients and their callers. Cons of AI Integration With all the positive buzz around AI utilization today, it’s vital to acknowledge the limitations of its use cases as well. Here are some reasons our team has found to be cautious: Efficiency without quality is a recipe for failure, and when cultural nuance and context are critical, AI sometimes falls short. When an AI interpreter squared off against two professional human interpreters, the AI was better able to keep up with the pace of the content than humans. However, it struggled with word choice and clarity at times. The U.S. immigration system has tried to use AI as a substitute for human interpreters. But the cost has been unacceptable: massive human suffering as immigrants struggle to explain asylum claims from detention using AI tools that can’t process their regional dialects. Publicly available AI tools like ChatGPT feast on massive sets of data for training, including potentially sensitive client details. There’s always the potential for service providers to store, access or misuse those details, or for them to simply be regurgitated in future chat sessions. The solution is to use AI tools in a secure environment with the help of a trusted service provider. We tend to think of bias as a human failing, but AI is not immune to it. These tools adopt human biases from the data they learn from, making them far from impartial. Fixing bias in AI starts with using diverse teams to build these systems and diverse data to train them. Human supervision via post-editing and quality assurance are vital to spot and correct bias in training materials and scripts for interpreters. No matter how intelligent a large language model (LLM) like ChatGPT can sound, these systems don’t think like we do. As a result, LLMs will sometimes “hallucinate,” which means that they will provide plausible-sounding answers that are incorrect. There's also the issue of logic and reasoning: While AI can quickly sort through tons of data, it often stumbles on tasks that require complex thinking or deductive reasoning. If you're relying on AI for language support, these limitations can be more than just minor hiccups; they can lead to real misunderstandings. In translation and interpretation, where the nuances can be as important as the words themselves, our team has learned to blend the best of both worlds: human expertise with AI efficiency. We’re not looking to replace human interpreters who bring valuable skills and expertise that AI can’t replicate. Instead, we’re using AI to improve the customer experience by doing what it does best: analyzing data, spotting trends and powering process improvements. Our goal is to use AI to help callers get the support they need in their own language as quickly as possible, while freeing up human interpreters to focus on more complex and satisfying work. By integrating AI with our interactive voice response (IVR) system, we can speed up and improve call flows, ensuring that callers with limited English proficiency (LEP) have easy access to a full range of self-help services. This frees up interpreters to focus on what really counts: delivering high-quality, nuanced interpretation that truly bridges language gaps. We’re also actively designing ways to route calls to the interpreter who is best suited to handle them. AI tools can instantly connect calls to the most appropriate interpreter, based on past performance, specialization, and even caller preferences, making the whole process smooth and virtually seamless. This isn't just about speed—it's about making each interaction as helpful as possible. We’re building a system powered by AI that can use call data to identify routine questions asked by consumers. With this information, we can help our clients create efficiencies, reducing the time it takes to handle these questions, improving the customer experience, and potentially designing proactive solutions that reduce the need for support calls. One of the most powerful use cases we’ve found for AI is mapping the Cultural Drivers of Engagement (CDE). These are the factors within a culture that affect how a customer engages with a company, such as their demographics, their belief systems, and the way they research, shop or purchase. With the CDE, we can provide culturally relevant experiences for multicultural consumers that to increase engagement. AI holds enormous potential to enrich language services, making us faster, smarter, and more in tune with the needs of our customers and the communities they serve. However, AI has strengths and weaknesses. We're committed to leveraging the best of both worlds—human expertise and AI capabilities—to deliver secure solutions that drive success for our customers and make the world a more inclusive place. By focusing on creating new, viable workflows we are empowering our clients to communicate with their diverse consumers more efficiently and effectively. We’re always on the lookout for informative, useful and well-researched content relative to our industry. Write to us.
能够与来自世界各地的多元文化、多语言消费者沟通从未如此重要。企业服务于比以往更加多样化的客户,这些消费者期望流畅、无缝和技术先进的个性化体验。 我们的语言专家团队认为,人工智能(AI)及其变革性技术将在创建更加个性化、高效和文化敏感的通信解决方案方面发挥关键作用。在本文中,我们将研究人工智能在口译中的利弊,并分享我们的团队从将人工智能与我们的服务和其他技术(如交互式语音应答(IVR))集成以更好地为我们的客户服务中获得的经验教训。 人工智能在口译中的优势 炒作是真实的:人工智能可以在许多方面提高电话口译服务的质量。以下是我们注意到的一些积极变化: 人工智能有潜力让口译更加高效。例如,改进的呼叫流程可以为我们的客户及其顾客节省时间。他们还改善了客户体验,特别是如果这意味着他们不必等待太久。 人工智能还提供了在不增加员工数量的情况下扩大你提供的服务或你服务的客户数量的潜力。这意味着提供以前由于成本或人员限制而无法提供的服务水平。例如,会议目前能够通过利用人工智能驱动的实时口译和人工智能生成的声音来提供许多不同语言的同声传译,这在过去是非常昂贵的。 ULG的常驻MT专家布兰卡·维达尔(Blanca Vidal)在最近接受权威杂志(Authority Magazine)采访时描述了人工智能可以帮助我们与客户一起成长的其他方式:“我们可以创建模仿人类语言的风格指南,可以连接远程员工,为多语言患者提供医疗保健信息或其他我们尚未想到的应用程序,所有这些都只需轻触一个按钮。” 通过人工智能,我们可以分析许多不同类型的数据,以确定跨文化的客户需求和趋势。这些文化见解有助于我们提供更加个性化和高效的服务,不断适应客户及其来电者不断变化的需求。 人工智能集成的缺点 随着今天围绕人工智能应用的所有积极讨论,承认其用例的局限性也是至关重要的。以下是我们团队发现的一些谨慎的原因: 没有质量的效率是失败的原因,当文化差异和背景至关重要时,人工智能有时会功亏一篑。当一个人工智能口译员与两个专业的人类口译员对抗时,人工智能比人类更能跟上内容的节奏。然而,它有时会在用词和清晰度上挣扎。 美国移民系统试图用人工智能代替人工翻译。但代价是不可接受的:当移民使用无法处理其地区方言的人工智能工具难以解释拘留的庇护申请时,人类遭受了巨大的痛苦。 像ChatGPT这样公开可用的人工智能工具利用大量数据进行训练,包括潜在的敏感客户细节。服务提供商总是有可能存储、访问或滥用这些详细信息,或者在未来的聊天会话中重复这些信息。 解决方案是在可信服务提供商的帮助下,在安全的环境中使用人工智能工具。 我们倾向于认为偏见是人类的失败,但人工智能也不能幸免。这些工具从他们学习的数据中采纳了人类的偏见,这使得它们远非公正。修复人工智能中的偏见始于使用不同的团队来构建这些系统,并使用不同的数据来训练它们。通过后期编辑和质量保证进行人工监督对于发现和纠正口译员培训材料和脚本中的偏见至关重要。 无论像ChatGPT这样的大型语言模型(LLM)听起来有多智能,这些系统都不会像我们一样思考。因此,LLMs有时会产生“幻觉”,这意味着它们会提供听起来合理但不正确的答案。 还有逻辑和推理的问题:虽然人工智能可以快速整理大量数据,但它经常会遇到需要复杂思维或演绎推理的任务。 如果你依赖人工智能来提供语言支持,这些限制可能不仅仅是小问题;它们会导致真正的误解。 在翻译和口译中,细微差别可能与文字本身一样重要,我们的团队已经学会了融合两个世界的精华:人类的专业知识与人工智能的效率。我们不希望取代人类口译员,他们带来了人工智能无法复制的宝贵技能和专业知识。相反,我们正在使用人工智能通过做它最擅长的事情来改善客户体验:分析数据,发现趋势和推动流程改进。 我们的目标是使用人工智能来帮助呼叫者尽快获得他们自己语言的支持,同时解放人类口译员,让他们专注于更复杂和令人满意的工作。通过将人工智能与我们的交互式语音应答(IVR)系统相结合,我们可以加快和改善呼叫流程,确保英语水平有限的呼叫者能够轻松获得全方位的自助服务。这让口译员可以专注于真正重要的事情:提供高质量、细致入微的口译,真正弥合语言差距。 我们还在积极设计将调用路由到最适合处理它们的解释器的方法。人工智能工具可以根据过去的表现、专业化甚至呼叫者的偏好,立即将呼叫连接到最合适的翻译,使整个过程顺利进行,几乎无缝。这不仅仅是速度的问题,而是让每一次互动尽可能的有帮助。 我们正在建立一个由人工智能驱动的系统,可以使用通话数据来识别消费者提出的常规问题。有了这些信息,我们可以帮助客户提高效率,减少处理这些问题所需的时间,改善客户体验,并有可能设计主动解决方案来减少对支持电话的需求。 我们发现的人工智能最强大的用例之一是绘制参与的文化驱动因素(CDE)。这些是文化中影响客户如何与公司互动的因素,如他们的人口统计数据、信仰体系以及他们研究、购物或购买的方式。通过CDE,我们可以为多元文化的消费者提供与文化相关的体验,从而提高参与度。 人工智能拥有丰富语言服务的巨大潜力,使我们更快、更智能,更符合我们的客户和他们所服务的社区的需求。然而,AI有优点也有缺点。 我们致力于利用人类专业知识和人工智能能力这两个世界的精华来提供安全的解决方案,为我们的客户带来成功,并使世界变得更加包容。通过专注于创建新的、可行的工作流程,我们使我们的客户能够更高效、更有效地与不同的消费者沟通。 我们一直在寻找与我们的行业相关的信息丰富、有用和经过充分研究的内容。 给我们写信。