Translation Tools Could Save Less-Used Languages

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Sometimes you may feel like there's nothing worth reading on the Web, but at least there's plenty of material you can read and understand. Millions of people around the world, in contrast, speak languages that are still barely represented online, despite widespread Internet access and improving translation technology.

Translation Technology Is Getting Better. What Does That Mean For The Future?


Tools and apps like Google Translate are getting better and better at translating one language into another. Alexander Waibel, professor of computer science at Carnegie Mellon University's Language Technologies Institute (@LTIatCMU), tells Here & Now's Jeremy Hobson how translation technology works, where there's still room to improve and what could be in store in the decades to come. "Over the years I think there's been a big trend on translation to go increasingly from rule-based, knowledge-based methods to learning methods. Systems have now really achieved a phenomenally good accuracy, and so I think, within our lifetime I'm fairly sure that we'll reach -- if we haven't already done so -- human-level performance, and/or exceeding it. "The current technology that really has taken the community by storm is of course neural machine translation.

A New Input Method for Human Translators: Integrating Machine Translation Effectively and Imperceptibly

AAAI Conferences

Computer-aided translation (CAT) system is the most popular tool which helps human translators perform language translation efficiently. To further improve the efficiency, there is an increasing interest in applying the machine translation (MT) technology to upgrade CAT. Post-editing is a standard approach: human translators generate the translation by correcting MT outputs. In this paper, we propose a novel approach deeply integrating MT into CAT systems: a well-designed input method which makes full use of the knowledge adopted by MT systems, such as translation rules, decoding hypotheses and n-best translation lists. Our proposed approach allows human translators to focus on choosing better translation results with less time rather than just complete translation themselves. The extensive experiments demonstrate that our method saves more than 14% time and over 33% keystrokes, and it improves the translation quality as well by more than 3 absolute BLEU scores compared with the strong baseline, i.e., post-editing using Google Pinyin.

Why Allow Customers to Suffer the Consequences from Bad Chat Translations?

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GeoFluent Customizer is a product billed by its makers, Lionbridge, as being able to improve base language models by using your enterprise's language assets for an enhanced customer chat translation experience. Basically that means that while, yes, you can find free chat translation services out there, Lionbridge is betting you wouldn't want to actually allow valuable customers to utilize them in fear of inaccuracies. It offers real-time on-demand custom translation in a cloud app -- automated machine translation technology, instantly translating content and communications into multiple languages.The GeoFluent platform accomplishes this, Lionbridge officials say, by integrating advanced cloud-based language tools with a real-time translation service, a statistical machine translation engine from IBM's (News - Alert) Watson Research Center. The chat translation platform can also be customized using your content and configured for specific business processes for multilingual communication such as eSupport, forums, blogs, web pages, chat sessions and other such uses. What sets GeoFluent apart is the fact that is based on a powerful technology platform that uses statistical machine translation algorithms from IBM. SMT, Lionbridge officials explain, has contributed to the growth in machine translation applications in recent years since it can analyze "vast amounts of previously translated material," and target-language texts to create what Lionbridge officials say is "a real-time translation in a fraction of the time that it takes to produce traditional rules- based translation systems."