Machine Translation: The Combination of Machine Learning and Human Intelligence - insideBIGDATA

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In this special guest feature, Vasco Pedro, CEO and Co-Founder of Unbabel, discusses the importance of machine translation for natural languages and how it currently lacks the quality companies demand for their content. Dr. Pedro' company is Unbabel, the Y Combinator-backed startup that combines crowdsourced human translation and machine learning to deliver fast translation services to businesses with human tone and nuance. Vasco previously worked for Google helping to develop technology for data computation and language at scale, and served as a research faculty member at the Technical University of Lisbon. Vasco holds a PhD in Language Technologies from Carnegie Mellon University in the field of computational semantics. Additionally, Vasco is a Fulbright Scholar, mentor, and advisor to a number of startups on top of being a serial entrepreneur.


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

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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 AI-powered translation needs a lot of work

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The latest scare story around the rise of robots is that within 120 years all human jobs will be automated. If that study from Oxford University is to be believed, we're just 3 to 4 generations away from perpetual holiday. The report goes on to predict when AI will outperform humans and -- more interestingly -- how. Some aspects will be of genuine concern to certain industries: AI will be a better driver than human heavy goods vehicles drivers by 2027, AI will write better novels than we can by 2049, and, closest to today, AI will be better at translation by 2024. AI has the potential to significantly reshape the translation sector, as it's doing to many other industries already.


The rise of AI translators - Raconteur

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In The Hitchhiker's Guide to the Galaxy, writer Douglas Adams describes a "small, yellow, leech-like creature" called the Babel fish which "feeds on brain-wave energy, absorbing all unconscious frequencies and then excreting telepathically a matrix formed from the conscious frequencies and nerve signals picked up from the speech centres of the brain, the practical upshot of which is that if you stick one in your ear, you can instantly understand anything said to you in any form of language". Botanists have not discovered anything like the Babel fish, but the science fiction of universal translation is rapidly becoming reality thanks to technological advances. Most exciting for Hitchhiker fans is the Pilot earbud, backed by $3.5 million in crowdfunding raised by a startup called Waverly Labs. The company's chief executive Andrew Ochoa says: "We were really inspired with wearable technology and began working on the idea of a smart earpiece that could solve a global challenge. We were a small team back then, but we all came from different backgrounds and spoke different languages, and that's how we came up with the idea."


How Google translations are getting more natural

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Mumbai: Researchers are increasingly striving to help machines translate words from one language to another the way professional translators would. This implies that machines must understand the context of words and sentences, and make sense of idioms, phrases and jokes. However, despite the fact that billions of words are being translated daily by multilingual machine translation services like Google Translate, Microsoft Translator, Systran's Pure Neural Machine Translator, WordLingo, SDL FreeTranslation, China's Baidu, Russia's Yandex or Babel Fish, machines have a long way to go before they can function as fluently as humans do when speaking in, and translating, different tongues. Barak Turovsky, product lead at Google Translate--a free multilingual machine translation service from Google Inc.--understands this dilemma well. "Today, translation by machines can be likened to my five-year-old son speaking Russian.