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1-min read

Facebook AI Significantly Improves Urdu to English Translation

Facebook AI researchers seek to understand and develop systems with human-level intelligence by advancing the longer-term academic problems surrounding AI.

IANS

Updated:September 3, 2018, 5:39 PM IST
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Facebook AI Significantly Improves Urdu to English Translation
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Artificial Intelligence (AI) researchers at Facebook have set a new record in improving translation from Urdu to English. Neural Machine Translation (NMT) is the field concerned with using AI to do translations in any language. The team from Facebook AI Research (FAIR) has seen a dramatic improvement in its results, the Forbes reported on Saturday. "To give some idea of the level of advancement, an improvement of 1 BLEU point (a common metric for judging the accuracy of MT) is considered a remarkable achievement in this field; our methods showed an improvement of more than 10 BLEU points," the team said in a paper that described translation from Urdu to English.

Facebook AI researchers seek to understand and develop systems with human-level intelligence by advancing the longer-term academic problems surrounding AI. The research covers the full spectrum of topics related to AI, and to deriving knowledge from data: theory, algorithms, applications, software infrastructure and hardware infrastructure.

"Long-term objectives of understanding intelligence and building intelligent machines are bold and ambitious, and we know that making significant progress towards AI can't be done in isolation," said researchers from FAIR. FAIR researchers have tested a new approach that teaches bots how to chit-chat like humans. Facebook is making deep investments in AI technology and in May announced the next version of its open-source AI framework for developers.

Microsoft is currently leading when it comes to AI and Deep Neural Networks to improve real-time language translation. Earlier this year, Microsoft brought machine learning to improve language translation for Hindi, Bengali and Tamil. With Deep Neural Networks-powered language translation, the results are more accurate and the sound more natural.

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