![]() The goal was to establish a data center in Iceland, where the naturally cool climate and abundant energy meant low-cost power. Linguee turned to Verne Global, a company specializing in intensive AI support. Now, the only thing missing was the computing power needed to run the billions of mathematical calculations integral to NMT. As it happened, Linguee - with its extensive set of parallel language pairs covering a wide range of fields - was able to do just that. “Before neural machine translation was an option, we had always felt that given the massive resources invested by very large players into statistical machine translation models, we would not have been able to close the gap.”Īs Andy Way, Deputy Director of AI research center ADAPT, told Slator, data for NMT needs to tick three boxes: it must be of high quality, and high volume, and it must align with the field in which the MT system will be deployed. “Machine learning has always been at the heart of Linguee, so when neural networks became an option for machine translation, we saw an opening,” then CTO, now CEO Kutylowski explained to Slator back in 2017. When neural machine translation ( NMT) became state of the art in 2016, easily outperforming models based on statistical machine translation ( SMT), Linguee saw an opportunity. “Terminology has always been the weakest element of machine translation integration, but the new glossary provides a marriage of our terminology with the high quality of DeepL” - Annette Kraus, Head of Language Management, Deutsche Bahn Linguee also worked intensively with in-house language specialists and hundreds of freelance linguists to create, refine, and evaluate dictionary entries, addressing nuances such as register and field. $585 BUY NOW Included in our Pro and Enterprise plan. To create its product, Linguee scraped bilingual text samples from the Internet using web crawlers and then applied machine learning algorithms to evaluate quality. Linguee is essentially a translation memory: it consists of millions of language-pair parallel segments. So how did they do it? Timing Is EverythingĭeepL was originally founded in 2009 in Cologne, Germany as Linguee, an online dictionary. Now, four years after initial release, DeepL has indeed taken its position alongside Google, Microsoft, Facebook, and Amazon as a industry-leading MT provider. CEO Jaroslaw Kutylowski told Slator in a 2017 interview, “We are a German company and we’re aware that we are going up against US-based firms.” Regulatory filings also show that DeepL reported EUR 142,000 in annual profit for 2020, down from EUR 0.95m in 2019 - which suggests the company is heavily investing back into the business.įrom inception, the founders of DeepL had always meant to take on big tech. However, certain limited information indicates that DeepL’s revenues in 2020 had not yet exceeded EUR 40m, a key regulatory threshold. Germany does not require companies to publicly file full financial accounts. Subscribe now!įinancial data about DeepL is hard to come by. $590 BUY NOW Included in our Pro and Enterprise plan. An acceleration in hiring activity this year, particularly over the last three months, makes it likely that the number of full-time employees has surpassed 150. The number of full-time staff at DeepL doubled between 20 from 43 to 86, according to regulatory filings. Among other investors are btov, a European venture capital firm with offices in Germany, Switzerland, and Luxembourg. ![]() In 2018, the company attracted an investment from one of Silicon Valley’s most high-profile venture capital firms, Benchmark Capital, which took a 13.6% stake. It’s really an amazing evolution.” Funding and Growth ![]() “Terminology has always been the weakest element of machine translation integration, but the new glossary provides a marriage of our terminology with the high quality of DeepL. Kraus sees it as a significant advance in DeepL’s offering. I echo many other users in saying it has the best results in terms of grammar, style, and fluency.”Ī new glossary feature, rolled out by DeepL in May 2020, allows users to define and enforce custom terminology. Why DeepL and not another MT provider? Kraus is unequivocal: “It’s simply amazing. DB’s Head of Language Management, Annette Kraus, told Slator that it quickly became popular and is now heavily used across the company daily. ![]() Subscribe now!ĭeutsche Bahn (DB), the world’s second largest transport company, started using DeepL three years ago. $570 BUY NOW Included in our Pro and Enterprise plan. ![]()
0 Comments
Leave a Reply.AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |