Large Language Models (LLMs) have proven their usefulness for streamlining workflows, optimizing linguistic assets, and shortening quality assurance cycles, among many other applications.
Through LLMs, AI is not just automating tasks, but also reshaping how language service providers (LSPs) and localization divisions at the enterprise operate, deliver value, and grow their businesses.
These changes and the overall impact of AI in translation are being felt across the language industry, and the second edition of Slator Pro Guide: Translation AI captures new and updated use cases illustrating this rapid evolution.
The guide examines not only impact, but also value, implementation effort, technology involved, business opportunities, and user perspectives for 20 use cases.
2024 Slator Pro Guide: Translation AI
The 2024 Slator Pro Guide presents 20 new and impactful ways that LLMs can be used to enhance translation workflows.
Highlighting the wide-ranging applications of AI in translation, from core machine translation (MT) to sophisticated production and linguistic tasks, a few primary business areas appear to be influenced by AI, though its impact is certainly not limited to these:
- Automation, as the overarching impact of AI in translation: Macro- and micro-tasks are being automated through agentic workflows, from source content preparation to final target delivery. Human intervention is reduced, allowing production teams to handle much larger volumes of content with more efficiency.
Orchestrator technologies enable “hyper-automation,” or a way to intelligently route content through steps based on factors like language pairs, context, and machine translation quality.
- Resources, with AI enabling a more targeted and effective use of human expertise and linguistic assets: Machine translation quality estimation (MTQE) tools, for example, predict which segments require human review, allowing linguists to focus where they are most needed.
AI-enhanced translation memories now improve fuzzy matches, potentially eliminating the need for human intervention in minor updates. This approach maximizes the value of both human and machine resources, and allows project managers and linguist roles to migrate to an oversight capacity.
- Quality assurance (QA) via augmented features that go beyond traditional grammar and syntax checks: LLMs with semantic error detection capabilities can identify mistranslations and stylistic inconsistencies that might be missed by conventional QA tools.
Higher-quality AI-enabled translations, particularly for technical or high-risk content, are now possible by matching the right content with the right MT models. Also, increased maintenance of linguistic assets is contributing to a reduced risk of quality issues.
- Additional revenue streams are emerging from the ability to generate multilingual content using client-specific linguistic assets: LSPs can now offer content creation services alongside translation, tapping into a growing market.
AI-enabled post-editing tools are also making faster turnaround times and more competitive pricing possible for the localization of certain content types. This, in turn, offers LSPs a higher level of scalability and revenue-boosting, while at the same time allowing them to leverage assets like translation memories and term banks with LLMs.
- Productivity gains across all workflows, including all linguistic tasks. Automatic semantic corrections, alternative translations/rephrasing suggestions, and SEO optimization are some examples of what LLMs can now help simplify and automate.
The ability to generate multiple target versions is another way in which linguists can work faster and more efficiently. Plus, AI-driven automation of administrative tasks further frees up time for linguists to focus on core linguistic tasks.
To find out how LLMs can be used to enhance translation quality, streamline workflows, optimize linguistic assets, and produce customized, industry-specific translations with ease, get your copy of the practical Slator Pro Guide: Translation AI.
