In his view, with that growing demand, approaches to localization might be too slow and are just not able to scale. Kutylowski commented that the world is becoming more interconnected every year, which serves to increase the importance of language translation and communication. There are numerous challenges that enterprises face when dealing with translation that DeepL is looking to help solve. “As we develop further as a company we see us using the underlying technology to help humans to communicate also in other ways - with new products that facilitate communication.” The continuing enterprise challenges of translation “In the beginning we understood this vision as being very strongly tied to translation specifically,” he said. The original core vision of the company was to break down language barriers - and Kutylowski emphasized that the company continues to focus on this area. “We do not disclose the details of our translation technology, but can say that as a company we’ve always been pushing the boundaries of how neural networks are designed to maximize translation quality,” Kutylowski explained. Taking a deep neural network approach to language translationĭeepL has developed a language translation engine that relies on neural networks (NN) to infer accurate translations.Īccording to the company, it uses a novel NN design to understand the nuanced interpretations of phrases and sentences and is able to convey them in a target language. It translates between lots of languages, works very quickly, looks great, and doesnt stop at just normal text translations. Meta (fomerly known as Facebook) isn’t being left out of the party either, announcing its AI powered universal speech translator (UST) project in October 2022. Microsoft has been actively updating its Azure Translator service with AI models that the company claims improves overall quality.
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Google has been advancing its Google Translate service in recent years with a series of different approaches, including the use of a recurrent neural network (RNN). The basic semantic constructs of pattern matching, however, don’t scale for larger scale translations, where context and tone matters.ĭeepL is one of many vendors that have been applying advanced AI techniques to better translate human language.
The earliest days of language translation were driven by basic pattern matching techniques.įor example, a user types “hello” into a database that matches its equivalent in another language for example, French (“bonjour”). AI-powered translation is a growing trend As the company is growing, he noted, it will be spending more on fundamental AI research, venturing into new product areas and also expanding its portfolio towards enterprise customers. While coy on what the actual funding amount, Kutylowski has very clear objectives on what the money will be used for.
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