Student Speak: Using KantanMT for Medical Domain

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Fiorenza Miriam Catania, an Italian student at University of International Studies of Rome (UNINT) recently completed her Master’s thesis on Machine Translation output for the Medical domain. In this post, she shares her experience of using KantanMT for her research. Continue reading

5 Best Practices for Checking Machine Translation Quality

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You have your finger on the pulse of latest technologies, and you are proud to use the latest automated technology for your localization needs. But, sometimes it might feel like you are still stuck in the 90s when it comes to reviewing your Machine Translation (MT) output for quality – especially, if you are using spreadsheets to collate your reviewers’ feedback on segments.

Traditionally language quality review for MT involves the Project Managers (PMs) sending copies of a static spreadsheet to a team of translators. This spreadsheet contains lines of source and target segments, with additional columns where the reviewers score the translated segments according to a set of predefined parameters.

Once the spreadsheets are sent off to the reviewers, PMs are completely in the dark – with no idea how the reviewers are progressing, when they might complete the review, or if they have even started the project.

If that sounds tiring, imagine what the PM has to go through!

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Why Neural Machine Translation (NMT) Might be the Next Big Step for the Industry

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Last month, Dr Dimitar Shterionov published an article on MultiLingual, a highly respected magazine on the localization industry, where he talks about the changing landscape of Machine Translation (MT) in the language industry, and how Neural Machine Translation (NMT) has very recently emerged as a revolutionary new paradigm in MT research.

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Let us take a look at some of the key points put forward by Dimitar and what is happening with NMT. Thanks to increasingly robust technological advancements, Machine Translated content has proven to be incredibly accurate, especially when compared to the output of traditional MT models.

What is Artificial Neural Network (ANN) and Neural Machine Translation (NMT)? Continue reading

Translation Automation in the Automotive Industry

Case Study: Automotive translation with WK Automotive
Case Study: Automotive translation with WK Automotive

The globalised make-up of the car industry, means automated translation is an important tool for those working in the automotive industry. KantanMT has helped clients use Machine Translation to efficiently translate technical documentation, motor part catalogues and how-to manuals, whilst automotive websites, such as ChromeData use KantanMT to translate content, so it can give detailed vehicle info and specifications for thousands of websites and dealerships around the globe.

The automotive industry has always been one of change. That change is leading to fundamental shifts in car technology and how users interact with them. In 2016, a typical car coming off the production line will contain 100 million lines of code. 20 million of those lines of code are required just to run a standard navigation and infotainment system. This increasing complexity inevitably leads to increasing level of customisation.

Changing Automotive Industry

While technology continues to advance, car manufacturers are increasingly looking at it as an area of differentiation. As manufacturers explore ways of delivering superior performance, implementing software that can be updated regularly, similar to that of a mobile phone, will enter mainstream usage in our cars. Technology centric car companies such as Tesla are already utilising such conveniences and it is inevitable more will follow.

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New Feature Release: Interview with Louise Faherty, Project Manager, Professional Services Team on features and benefits of KantanLQR™

Following KantanMT’s announcement of the roll out of the much-anticipated KantanLQR™ platform to all its Partners worldwide, Louise Irwin from Digital Marketing Team caught up Louise Faherty, Project Manager, Professional Services, KantanMT to talk about the features, benefits and the impetus behind creating the tool. Continue reading

University Speak: Translation Technology with UCL Senior Lecturer, Mark Shuttleworth

KantanMT has an ongoing University_College_London,_by_William_Wilkins.jpgAcademic Partnership with Centre for Multidisciplinary and Intercultural Inquiry (CMII) at University College London to accelerate research and learning in the field of Machine Translation (MT). The postgraduate students of the department were able to use the KantanMT platform to update or gain new skills in Translation Technology. With help of the KantanMT platform, the students learnt how to build and customise their own Statistical Machine Translation (SMT) systems in a real world scenario.

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Machine Translation Trend in 2016: The Age of Automatic Workflows and More Collaboration

2016Trends_1_ImageKantanMT recently published a brand-new white paper on what global companies can expect to see in 2016 for Machine Translation (MT). The MT industry is rapidly changing and moulding itself to the technical needs and globalization requirements of the present day. Our white paper puts forward six major MT trends that all businesses need to KNOW in order to stay relevant and ahead of their competitors.

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