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Machine Translation (MT) has been around for decades. In recent years, it has evolved at an exponential rate. As companies produce ever-growing amounts of content in different languages, they have come to see MT as an opportunity to extend their reach in an increasingly globalized world.
Companies seeking to deploy MT can explore the following four basic strategies.
No matter which strategy you are contemplating, selecting the right engine may be challenging without the proper data and MT experience. Lionbridge is an expert in MT. 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 that will help you make the right choice. This webpage provides basic information about the performance of popular MT engines for the most common language pairs to help you select the best option based on your content.
When choosing among the many available MT systems, it’s important to note some engines 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. To determine the best option, first, identify why you are using MT.
If you want an MT engine for general use, it may be appropriate to use Google Translate or Bing Translator. If you seek MT services for a specific language or domain, you may achieve better results by turning to Amazon Translate or DeepL Translator.
Lionbridge’s Machine Translation Tracker analyzes engine performance monthly to help you figure out the best MT engine depending on the language pairs you use. The next time you ask which MT engine is best, reframe the question to, “Which MT engine is best for me?” And count on Lionbridge for guidance.
Want to learn more about the different types of MT technologies? Check out our blog Machine Translation in Translation.
We’ve seen a nice overall improvement in Microsoft’s Machine Translation (MT) results during October 11-November 1. With this recent quality increase by Bing Translator, the main MT engines are producing very similar results. As such, they face a tight battle for the top leadership position.
The major MT engines have not shown interesting improvements for months. Let’s hope this development from Microsoft breaks that trend and is the start of forthcoming progress by these engines.
We went beyond our usual measure of single-reference translations and confirmed the Microsoft improvement results with a second tracking that encompassed multiple references. In this MT evaluation, we used 10 reference translations completed by humans — the gold standard — instead of just one translation to get a more precise Edit Distance metric that considers multiple possible correct translations in the final results.
As we reach the end of the year, we note that 2022 has had very flat MT results. We observed little change; this Microsoft Bing MT development may be the most notable advancement of the whole year. As commented on earlier in the year, the current MT paradigm may be reaching a plateau. We look forward to seeing what 2023 holds for Machine Translation.
—Rafa Moral, Lionbridge Vice President, Innovation
For more insights and future trends about Machine Translation, read our Future of Language Tech blog post – Future of Machine Translation.
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