Machine translation (MT) isn’t new to the localization world. While it has made a significant impact on translation in recent years, we are now entering a new wave of innovation driven by AI and large language models (LLMs). While MT falls under this category, the ability to train an LLM on a business’s content and translation needs is enabling more accurate translations before human intervention than ever before.
This shift is transforming localization workflows and allowing businesses to grow globally while hyper-personalizing content for their target audiences. However, despite these advancements, fully trusting MT without human review remains a challenge for businesses.
So, how can businesses take advantage of AI-driven cost savings while maintaining quality through human oversight? This is where machine translation quality estimation (MTQE) comes in. MTQE automates the assessment of translation accuracy, helping companies streamline workflows, cut costs, and ensure consistency across languages. Let’s explore the top three benefits of implementing MTQE in your translation strategy.
Is your team bogged down with manual quality control? MTQE reduces the burden by automatically evaluating translations for accuracy, enabling faster project turnaround times.
Instead of spending hours manually reviewing every translated segment, MTQE tools analyze text at a granular level, identifying potential errors before human linguists step in. Unlike quality evaluation, which requires a reference translation to compare against and often involves human input, quality estimation works independently, assessing the likelihood of errors without needing a pre-existing benchmark.
The best QE engines are trained on ample high-quality data and specifically designed to flag questionable translation quality. Additionally, some QE engines allow businesses to customize the QE model by including training data from their own translated content for even better results. This means:
By incorporating automated quality checks, teams can streamline their translation processes and ensure projects move forward without bottlenecks.
Post-editing can be costly and time-consuming, especially for long texts, but it’s essential to ensure translation quality and consistency. Adding QE to the MT process (MTQE) helps reduce the workload by identifying high-confidence translations and only flagging problematic segments for review.
With MTQE, companies can:
By reducing the time and effort spent on post-editing, companies can achieve substantial cost savings while maintaining translation quality.
For businesses expanding globally, consistency in translation is crucial. Without proper quality control, MT-generated content can vary in accuracy and style across different languages, potentially harming brand credibility.
Combining MTQE with an up-to-date translation memory (TM) and style guide ensures consistency by:
Maintaining a uniform tone and accuracy across multiple markets strengthens brand integrity and builds trust with international audiences.
If you’re integrating MTQE into your workflow on your own, the process requires careful planning. Here’s how to get started:
If you’re looking for a faster, easier way to integrate MTQE and professional human review into your localization workflow, reach out to BLEND’s team to discover how to simplify your translation process and save on translation costs.
As AI-driven language models continue to evolve, MTQE is expected to become even more sophisticated, offering greater accuracy and efficiency. Future advancements will enhance error detection through more advanced machine learning models, making it easier to identify inconsistencies and translation errors. Additionally, MTQE will improve adaptation to industry-specific content, ensuring that specialized fields receive more precise and contextually accurate translations.
Refined scoring mechanisms will provide deeper insights into translation quality, allowing businesses to assess the reliability of MT-generated content with greater confidence. Contextual AI will play a crucial role in understanding linguistic nuances, ultimately leading to more natural and accurate translations. Moreover, the integration of predictive analytics will help businesses anticipate quality issues before they arise, enabling proactive adjustments to translation workflows.
While automation in machine translation and quality estimation will continue to advance, human expertise will remain essential for final reviews and nuanced translations, ensuring the highest level of linguistic and cultural accuracy.
Looking to enhance your localization strategy with machine translation? MTQE can help you scale faster while maintaining quality and consistency.
At BLEND, we combine cutting-edge MTQE technology with expert human oversight to optimize your translation workflows. Whether you’re localizing for one market or expanding globally, our solutions help you achieve high-quality results with greater efficiency. Our entire translation workflow—including MTQE, translation memory (TM), style guides, and human review—can seamlessly integrate with your CMS or wherever you’re creating and publishing content, ensuring a streamlined and efficient localization process.
Want to see how MTQE can transform your business? Let’s talk!
With over 10 years of experience in the localization industry, Adria is an expert in designing localization and translation solutions for global businesses in every industry. Adria is BLEND’s Solutions Architect and the Chapter Manager of Women in Localization Romania.
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