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Word embeddings have had a big impact on many applications in natural language processing (NLP) and information retrieval. It is, therefore, crucial to open the blackbox and understand their meaning representation. We propose probing tasks for analyzing the meaning representation in word embeddings. Our tasks…
A team of researchers from the Natural Language Processing (NLP) Group at Microsoft Research Asia (MSRA) and the Speech Dialog Research Group at Microsoft Redmond are currently leading in the Conversational Question Answering (CoQA) Challenge organized by Stanford University. In this challenge, machines are measured…
Language embedding is a process of mapping symbolic natural language text (for example, words, phrases and sentences) to semantic vector representations. This is fundamental to deep learning approaches to natural language understanding (NLU). It is highly desirable to learn language embeddings that are universal to…
Speech recognition is something we humans do remarkably well, which includes our ability to understand speech even in noisy multi-talker environments. While our natural sophistication at this is something we take for granted, speech recognition researchers continue to pursue refinements and improvements on the frontiers…
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…
Introduction Speech technology has come a long way since Alexander Graham Bell's famous Mr. Watson – Come here – I want to see you became the first speech to be heard over the telephone in 1876. Today, speech technology has moved into realms such as…
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…
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.…
At RSA Conference 2018, Microsoft announced Azure Sphere, previewing a unique new solution to help connect and secure the most populous category of computing today: the tens of billions of devices powered by microcontrollers (MCUs). Azure Sphere represents an opportunity for Microsoft and our partners…
Over the last decade, advances in machine learning coupled with the availability of large amounts of data have led to significant progress on long-standing AI challenges. In domains like computer vision, speech recognition, machine translation and image captioning, machines have reached and sometimes even exceeded…
The vision of the researchers at the Microsoft Research Montreal lab is to create machines that can comprehend, reason and communicate with humans. As part of this vision, our dialogue team has been doing research on task-oriented dialogue systems. We had earlier proposed the lexicalized delexicalized…
At the Microsoft Research Montreal lab, one of our primary research focuses is to advance the field of Question Answering. Automatic question-answering systems can provide humans with efficient access to vast amounts of information, and the task also acts as an important proxy for assessing…
