Top Three of MT
On the surface, there are more than 100 companies in the world that build and offer machine translation (MT) on a noticeable scale. Below the surface, there are hundreds of thousands of individual practitioners who build MT, and the most popular tools see millions of downloads per month. With the democratization of AI, an engineer can build an MT model in one day. However, beating Google or DeepL in quality, performance, and market presence is another story. So, who are the teams that are able to compete, stand tall, and beat the odds in the market of MT? Here are some of our top picks.
Big Tech MT
All of the top software giants in the world have built MT capabilities. Search engines like Google wanted to increase the number of websites people could discover, read, and engage with. After achieving success with the Chrome integration, they built MT into a cloud business. eCommerce companies like Amazon and Alibaba started by translating millions of inventory items on their online shopping platforms and expanded the technology into business later. Big Tech leads today’s MT market by the number of business users, and quite often in linguistic quality.
Probably the largest MT business in the world in terms of sales, and one of the leading players in linguistic quality, Google Translate is ubiquitous. On top of churning billions of words each day in Chrome for the good of mankind, the product-built business features including customization with translation memory and terminology bases, and document translation via the Google Translation Hub.
The closest rival to Google Translate, Microsoft Translator has the edge when it comes to Office applications. It is built into Outlook, Word, Teams, Excel, and other apps that billions of people use every day at work. On the business side, the advantage of Microsoft Translate is probably the only translator that can be hosted in a private cloud, so a bank can unplug it from the Internet and run it on their own hardware.
Amazon entered the MT game later than its Big Tech rivals and built new features that give it a unique selling proposition to compete. One of them is ”dynamic adaptation”, the ability to learn daily from user corrections without lengthy model retraining operations. Moreover, they introduced controls for the tone of voice and formality very early on, making their MT better for conversational translations. Still lagging behind Google and Microsoft in language coverage, Amazon is able to win large accounts like BMW due to their prowess in marketing to developers.
Dedicated MT/NLP Companies
With the exception of the newly minted unicorn DeepL, dedicated MT companies are mostly niche players that generate the majority of their revenues in on-premise deals with banks, government agencies, and intelligence services. Wherever the client doesn’t want to go to the Cloud due to security issues, dedicated MT firms excel.
DeepL took the MT world by storm, and it is now strong enough to potentially de-throne Google Translate. Starting from the dictionary business Linguee, DeepL invested a lot into achieving the best possible linguistic quality for a relatively small number of languages. Building a product users love, DeepL succeeded in making a top-100 most popular website in the world, winning tens of thousands of paying customers and creating a momentum of explosive growth that attracted investors. With this, DeepL outgrew larger MT players in just five years of work.
One of the oldest pioneers in MT, French tech Systran has gone through many waves of transformation, including a sale to a Korean company, and rebuilding the commercial team. Today it’s a leading business MT provider for on-premise scenarios, powering translation programs at Nestle, Shell, and Pfizer. Systran is famous for its community initiatives, including the support for the OpenNMT project, and building a marketplace where users can sell customized MT models built on top of Systran.
Strictly speaking, AppTek is not just a MT company. Their main business is in speech recognition, data, and more recently, synthetic voice. Focusing on video and media, AppTek built excellent MT capabilities for subtitles, and got integrated into professional subtitling tools such as Ooona.
- Baring Labs
LSP MT Brands
Many translation and language services companies have built their own models over the years, and attempted to commercialize them with varying degrees of success.
1. ModernMT by Translated
ModernMT is a machine translation system that adapts to the context of the document and translation style and constantly improves with real-time neural adaptivity. It elaborates the translation based on the content of the whole document and not just the single sentence, just like a human. Therefore, any correction provided is processed in real time, with no need for retraining.
2. Language Weaver by RWS
SDL Language Weaver was the primary machine translation technology at SDL since its inception in 2002. It was retired in 2015, but RWS adopted and relaunched the tool after acquiring SDL in 2020.
PangeaMT aims to provide companies with AI services and neural machine translation technologies, including data collection, anonymization, segment analysis, and audio transcription, not to mention several combinations of AI – NLP (natural-language processing). PangeaMT also allows users to collect material or use vast language resources to build neural machine translation solutions and host them in its servers. It also features a circle of deep machine learning, feedback, and neural engine upgrade and retraining. In this way, it integrates machine translation technologies into customers’ daily activities, making it part of their technology portfolio.
TransPerfect’s GlobalLink AI Portal
Reverie Language Technologies
United Language Group
Glodom Cloud Translation
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