Microsoft Research Blog

Bringing low-resource languages and spoken dialects into play with Semi-Supervised Universal Neural Machine Translation 

May 17, 2018 | Hany Hassan Awadalla
Machine translation has become a crucial component in the advancing of global communication. Millions of people are using online translation systems and mobile applications to communicate across language barriers. Machine translation has made rapid advances in recent years with the deep learning wave. Microsoft Research…

Recent Posts

  2. Insights on the future of video calling in emergency situations 

    May 16, 2018

    For most of us, a call to emergency services is a rare act, or one thankfully that we’ve never had to take. Anyone having had to make that call often will, in a calmer future moment, reflect on what transpired during it – the quality…

  5. Rapid Adaptation and Metalearning with Conditionally Shifted Neurons 

    May 11, 2018 | Tsendsuren Munkhdalai, Eric Yuan, Soroush Mehri, and Adam Trischler

    The Machine Comprehension team at MSR-Montreal recently developed a neural mechanism for metalearning that we call conditionally shifted neurons. Conditionally shifted neurons (CSNs) adapt their activation values rapidly to new data to help neural networks solve new tasks. They do this with task-specific, additive shifts…

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    Customized neural machine translation with Microsoft Translator 

    May 7, 2018

    Released in preview this week at Build 2018, the new Microsoft Translator custom feature lets users customize neural machine translation systems. These customizations can be applied to both text and speech translation workflows. Microsoft Translator released neural machine translation (NMT) in 2016. NMT provided major…

  7. Learning from Source Code 

    May 1, 2018

    Over the last five years, deep learning-based methods have revolutionised a wide range of applications, for example those requiring understanding of pictures, speech and natural language. For computer scientists, a naturally arising question is whether computers learn to understand source code? It appears to be…

  8. Boundary-seeking GANs: A new method for adversarial generation of discrete data 

    April 30, 2018 | Devon Hjelm and Athul Jacob

    Generative models are an important subset of machine learning goals and tasks that require realistic and statistically accurate generation of target data. Among all available generative models, generative adversarial networks (GANs) have emerged recently as a leading and state-of-the-art method, particularly in image generation tasks.…

  9. Neural-Guided Deductive Search: A best of both worlds approach to program synthesis 

    April 27, 2018 | Alex Polozov

    Program synthesis — automatically generating a program that satisfies a given specification — is a major challenge in AI. In addition to changing the way we design software, it has the potential to revolutionize task automation. End users without programming skills can easily provide input-output…

  10. Measuring employment demand with internet search data 

    April 25, 2018 | Scott Counts and Justin Cranshaw

    The United States, along with much of the world, is in the midst of an economic transition from manual to intellectual labor. The changing nature of work, including the automation of labor, is an important issue facing society, with implications not only for our standard…

  12. ChatPainter: Improving text-to-image generation by using dialogue 

    April 23, 2018

    Generating realistic images from a text description is a challenging task for a bot. A solution to this task has potential applications in the video game and image editing industries, among many others. Recently, researchers at Microsoft and elsewhere have been exploring ways to enable…

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