Expert Perspectives: The Human Role in Language Quality Management
AI is reshaping how we think about language quality management. But are machines alone the future? Or do human beings still have a major role in assessing and improving the quality of translation?
In search of answers, Beyont asked a panel of experts to share their views on the evolving relationship between humans and AI. Keep reading for key takeaways, or watch the video to explore in depth.
Meet the Experts
Our panel discussion brought together three language quality professionals with varied experiences and perspectives:
- Marta Nieto Cayuela, localization experience program manager at Uber.
- Paula Kalfirtova, vendor and quality manager at Comunica Translations.
- Rafa Zaragoza, quality and language delivery director at Toppan Digital Language.
Satu Suomalainen, program manager at Beyont, moderated the conversation and audience Q&A.
Key Takeaways: Copiloting with AI
Our panelists painted a complex picture of AI as a copilot, not a replacement, for human quality reviewers and subject-matter experts. Below are some of the most essential points from the discussion.
1. Language quality management still depends on human expertise.
While AI is taking on a larger role in translation, humans still have abilities that large language models lack. Human specialists remain essential to many stages of quality management—from initial content preparation and adaptation to final post-editing, quality review, and feedback.
Humans, unlike machines, excel at dealing with nuanced, specialized, and creative content. They bring a deep understanding of idioms, subtext, and local cultural context, especially for certain complex languages. Human experts are also vital for ensuring legal and regulatory compliance in fields such as medical translation.
2. Roles, responsibilities, and required skills are evolving.
With the growth of AI, language quality professionals need a different mix of skills. Demand is growing for people who understand applied linguistics, not just languages. For reviewers, basic translation skills are becoming less valuable than the ability to deal with sophisticated linguistic nuances and context.
Meanwhile, language quality managers are grappling with a new need for strategies that work for AI-generated content. They’re responsible not only for identifying the right personnel, but also for ensuring their teams have the right linguistic assets and quality frameworks. A strong knowledge of natural language processing (NLP) is among the skills they need to develop.
3. AI’s greatest benefit is quality management at scale.
Faced with escalating amounts of translated content, language quality managers are searching for ways to keep up. AI can help them handle increased volume by streamlining quality assessments and reducing time-consuming tasks.
For example, AI can speed up the process of categorizing and tracking errors, freeing quality reviewers from having to copy and paste between documents. By eliminating manual tasks no one wants to do, AI makes the feedback loop more efficient and allows humans to focus on tasks that only they can perform.
4. Quality managers and teams are only beginning to operationalize AI.
AI can help with a wide range of tasks such as categorizing errors, analyzing sentiment, and extracting terminology. However, it’s still a major challenge for quality managers to integrate all these tools into their teams’ workflows.
Cost, data organization, latency, GPU requirements, security, and confidentiality can all complicate AI deployment. As the industry rushes to embrace the new technology, language quality managers are spearheading the race to work out practical, day-to-day solutions to these problems.
5. Client education is more essential than ever.
For clients, AI presents an appealing way to save time and money. At the same time, they aren’t always fully aware of the technology’s limitations, the possible risks to confidentiality, or the problems that can result from over-automation.
In the age of AI, effective language quality management means educating clients about the risks of leaving humans out of the loop. Quality managers need to recommend up-to-date best practices and provide guidance to linguists, while meeting clients halfway on their desire for faster and more cost-effective processes.
Watch the Video to Learn More
The human touch is indispensable for language quality management—but is your organization ready for the age of AI? Catch the full panel discussion for more insights to boost localization quality success.