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Lionbridge Machine Translation Tracker

The longest-standing measurement of the leading Machine Translation engines — and now several generative AI models — will help you choose the best automated translation option to meet your needs.

Comparing Four Machine Translation Deployment Strategies


Machine Translation (MT) has been around for decades and has evolved exponentially in recent years. As companies produce ever-growing amounts of content in different languages, they see MT — and now emergent generative AI (GenAI) technology — as an opportunity to extend their reach in an increasingly globalized world.

Companies seeking automated translation solutions may select one of the following four basic strategies. The decision will influence which automation tool they want to implement.

Public MT

This strategy includes services like Google Translate or Bing Translator. Such services are readily available at no cost. However, the strategy’s shortcomings are its lack of security and quality issues in some situations, as engines have not been trained for specific domains or particular use cases.

On-Premises MT

This strategy requires a company to deploy an MT server in its IT environment. While this is the most secure option, it comes at a significant cost, is complex to deploy and manage, and requires ongoing maintenance. Importantly, this strategy often produces suboptimal MT output across multiple language pairs or content types.

Cloud MT

This strategy works like the public MT option because it is also hosted in the cloud. However, unlike public MT, it creates a company-dedicated instance. Any data provided to the service is tightly secured and not shared with third parties. It delivers additional capabilities around terminology customization and has other benefits. However, it can result in vendor lock-in and less-than-optimal MT quality across multiple language pairs.

Best-of-Breed MT

This strategy involves a single platform that allows companies to leverage multiple MT and GenAI engines. It provides a single layer of terminology customization, an easy-to-manage interface, and the ability to choose the best option for different language pairs, industries or domains, and types of content.

Want to learn more about Machine Translation terminology and the different types of automated translation technologies? Check out our blog, Machine Translation in Translation.

Which Automated Translation Engine Is Best?

There’s no simple answer.

When choosing among the many available automated translation systems, it’s important to note that some MT engines are trained to address a specific function or domain. If your needs don’t align with that purpose, the engine may perform sub-optimally, no matter how advanced it is.

Identify why you are using automated translations to determine the most suitable option. If you want an MT engine for general use, it may be appropriate to use Google Translate or Bing Translator. You may achieve better results using a different tool if you seek MT services for a specific language or domain.

Lionbridge is an expert in these technologies. In addition to having more than two decades of MT experience, we have gathered a large volume of linguistic and quality data about MT technology. Lionbridge’s Machine Translation Tracker analyzes the performance of the top five MT engines and several GenAI models. We publish the results periodically to help you understand how these tools perform, depending on the content’s domain and the language pairs used.

Check out our findings below. The results — which are everchanging — can give you an idea of which options are right for you.

Evaluating Overall MT Performance
Time
Per Language Pair Quality
Choose German, Spanish, Russian, or Chinese in the drop-down menu.
Time
Per Domain Performance
Select Domain/Subject Matter in the drop-down menu.
Time

Lionbridge Machine Translation Tracker Methodology

Lionbridge uses the inverse edit distance as a scoring method. The edit distance measures the number of characters (for Asian languages) or words (for Western languages) that need to be changed by a human post-editor before it achieves the quality level produced by a human translator. The higher the metric, the better the quality.

Additional Details About Lionbridge’s Methodology: 

  • We routinely assess the performance of Machine Translation engines and GenAI models, making our findings public periodically.

  • We provide the data for illustrative purposes; each case should be treated and assessed individually.

  • We generate this report based on source data preselected by Lionbridge’s Machine Translation teams. The same source data is submitted to every MT engine / GenAI model and language pair each time, making comparisons between the tools possible.

  • We do not use customer data to generate the report.

Read the Lionbridge Machine Translation Report for a deep dive into MT, including how it has evolved, the latest trends, and the factors paving the way for generative AI technology to disrupt the MT paradigm.

Lionbridge Expert Commentary

Navigating the rapid evolution of the MT and generative AI paradigms can be overwhelming. That’s where our team of seasoned experts comes in. They delve into the intricacies of these technologies daily, periodically sharing their insightful perspectives and thought-provoking analyses across a wide range of topics to our interested readers.

Whether you’re just starting your MT/GenAI journey or are a veteran user of automated translations, the Lionbridge expert commentary will deepen your understanding of these automated translation technologies. Your newly gained insight can help you make strategic business decisions, leading to more efficient translations, the ability to translate more content, and substantial cost-saving benefits.

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How Lionbridge’s Automated Solution Enabled the Le Monde Newspaper to Boost Its Subscriber Count

When iconic French newsgroup Le Monde released its English service, Le Monde, in English, it needed a translation solution that could handle the complexities of the 24-hour news cycle. Using Lionbridge’s Machine Translation solution Smart MT as part of the Lionbridge Language Cloud, Le Monde was able to slash translation turnaround time, cover the French presidential election, and attract thousands of new subscribers to their service.

Lionbridge Now Shares GPT-4 Machine Translation Results via Its Tracker

Explore GPT-4's translation performance via Lionbridge's MT Tracker. Compare GPT-4’s translation results to the translation results of the major Neural MT engines and monitor this generative AI engine’s progress over time.

Language Ranking Based on Machine Translatability for More Effective MT

Consider language complexity before deploying Machine Translation. Our machine-translatability ranking will help support your business decisions.

Machine Translation and Catastrophic Errors

Companies can face harsh consequences if Machine Translation output drastically deviates from the intended messages. Read how automated quality checks help.

Machine Translation and Formal vs. Informal Language

Machine Translation engines aren’t great at producing correct language formality. See how that’s changing and how Lionbridge has met the challenge.

Using Terminology to Improve Machine Translation

Generic MT engines can make detrimental translation errors. Discover how the use of terminology can help to minimize these errors and enhance MT quality.

Machine Translation Customization vs. Machine Translation Training

Learn what Machine Translation Customization and Machine Translation training are and when to use each method to improve automated translations.

Know the difference between MT and NMT? Can you define MTPE or hybrid MT engine? Click the image below to view key terms and their meanings to understand Machine Translation better.

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