Nowadays, Pangeanic’s neural machine translation engines are first-of-class and are chosen for data privacy by US government agencies, the European Union and Member States, as well as many companies that need to process thousands of pages daily at scale – including human-in-the-loop for final publication services.
Corporations, businesses and language service providers have benefited from our user-centric flexible approach. Users are in control of their own data and can customize and take ownership of their own machine translation software. Our proprietary Deep Adaptive Machine Translation technology ensures you can impact Machine Learning at different levels, prioritizing your own content and choose how you customize MT engines by domain, by user, by specific jobs.
PangeaMT was developed upon a large statistical framework in 2010, with quality estimation and re-training.
Statistics worked very well in several related languages (Romance languages and English, German and Scandinavian languages). However, our links to Japanese industry soon provided requests to add Japanese and Chinese to our service portfolio.
In 2011, Pangeanic developed hybrid machine translation services in which were included as part of the system features. In 2017, all our systems were migrated to a new framework based on neural networks and many more language pairs are available as a result of our NTEU project for the EC creating the largest neural machine translation engine farm not pivoting through English.
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Avoid having you locked-in by expensive upgrades year after year
It is a general agreement that Neural Machine Translation (NMT) has surpassed Statistical Machine Translation (SMT) in terms of fluency and adequacy when humans read the texts produced by the software. NMT uses a large artificial neural network that resembles what happens in the human brain with thousands of connections. One of the main advantages of NMT is that the context of the translation is much longer than SMT (phrase-level translation). Currently, developers mostly use sequence-to-sequence approaches where the full context of the sentence is taken into account. Accuracy and fluency of the translations increase with the use of NMT. Other advantages of NMT in respect to SMT are that NMT only requires a fraction of the memory needed by SMT and all parts of the NMT models are trained jointly (end-to-end approach) in order to maximize the target translation performance. Pangeanic is at the forefront of research and development of translation technologies incorporating NMT, embedding it in different processes.
Pangeanic has developed and used machine translation for many applications. It has reported successful use cases for many of its clients at industry events like Localization World Barcelona 2011, Localization World Paris 2012, Localization World London 2013, as well as numerous TAUS summits in the United States, Europe and in Japan, META Forum Berlin 2013 and Japan Translation Federation.
Pangeanic was also one of the largest donors of training data to TAUS, which in turn provided access to millions of words as training corpus. This enhanced PangeaMT platform and provided our team with the opportunity to experiment further, with millions and millions of aligned sentences. Machine translation became part of company culture since 2009. Since then, machine translation services to corporations and even other translation companies have become part of Pangeanic’s range of services. From 2012 to 2016, Pangeanic has been a member of the EU’s Marie Curie action EXPERT Project, advancing the state-of-the-art with young and experienced researchers.
Another breakthrough is our in-line mark-up parser and full document reconstruction of machine translated documents in other languages. PangeaMT handles tags extremely efficiently. Machine translation systems (as they come from open sources releases) usually produce plain text output because this is also the format they process. PangeaMT implementations at the Spanish Tax Office, news agencies, legal firms require full document translation, which means keeping the format as close to the original as possible (MS Office family formats such as Word, Excel, PowerPoint and others), including layout, image distribution, tables, etc.
For language professionals using CAT tools, our parser can identify in-lines without attempting to translate them, and it places them back in the resulting text, too. An in-line placeholder acts first by copying and transferring all XML and code information to a separate module. The translation engine does its work and then places the in-line back into the translated segment. At the time of its release, our in-line parser constituted an innovation well-above the current level of maturity of other well-known systems.
Please visit our machine translation division website to learn more about PangeaMT features.
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