Microsoft Research Blog

The Microsoft Research blog provides in-depth views and perspectives from our researchers, scientists and engineers, plus information about noteworthy events and conferences, scholarships, and fellowships designed for academic and scientific communities.

Machine Reading Systems Are Becoming More Conversational

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 by their ability to understand a text passage and answer a series of interconnected questions that appear in a conversation. Microsoft is currently the only team to have reached human…

May 2019

Microsoft Research Blog

Towards universal language embeddings

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 many NLU tasks. Two popular approaches to learning language embeddings are language model pre-training and multi-task learning (MTL). While the former learns universal language embeddings by leveraging large amounts of…

March 2019

Microsoft Research Blog

Thinking outside-of-the-black-box of machine learning on the long quest to perfecting automatic speech recognition

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 of the research space of automatic speech recognition. Significant technological progress that has been made over decades has shaped automatic speech recognition technology into its current form, which is already…

August 2018

Microsoft Research Blog

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

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 recently achieved a historic milestone in machine translation – human parity in translation of news articles from Chinese to English. Our state-of-the-art approach is a Neural Machine Translation system that…

May 2018

Microsoft Research Blog

Sounding the Future: Microsoft Research brings its best to ICASSP 2018 in Calgary

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 VoIP, teleconferencing systems, home automation, and so on. Its importance has grown exponentially with the emergence of mobile and wearable devices and many existing and upcoming Microsoft services, devices and…

May 2018

Microsoft Research Blog

Customized neural machine translation with Microsoft Translator

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 advances in translation quality over the then industry-standard statistical machine translation (SMT) technology. Because NMT better captures the context of full sentences before translating them, it provides higher quality, more…

May 2018

Microsoft Research Blog

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

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. While highly successful with continuous data, generation of discrete data with GANs remains a challenging problem that limits its applications in language and other important domains. In this post, we…

April 2018

Microsoft Research Blog

From research idea to research-powered product: behind the scenes with Azure Sphere

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 to serve a new era of computing with securely connected devices at tremendous scale. Just as Microsoft brought affordable PCs to every desk, with Azure Sphere we aim to enable…

April 2018

Microsoft Research Blog

Platform for Situational Intelligence

Platform for Situated Intelligence: Tools and Framework for Multimodal Interaction Research

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 human performance levels on specific problem sets. However, building end-to-end, multimodal interactive systems that bring together multiple AI technologies and interact with people in the open world remains an important…

April 2018

Microsoft Research Blog

Unsupervised Metrics in Task-Oriented Dialogue for Evaluating Natural Language Generation

Relevance of Unsupervised Metrics in Task-Oriented Dialogue for Evaluating Natural Language Generation

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 – semantically controlled – LSTM (ld-sc-LSTM) model for Natural Language Generation (NLG) which outperformed state-of-the-art delexicalized approaches. In this new work, we perform an empirical study to explore the relevance of…

July 2017

Microsoft Research Blog

QA model

A Joint Model for Question Answering and Question Generation

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 machine literacy via reading comprehension. Researchers in psychology have observed that humans improve their reading comprehension by asking questions about the material at hand. Inspired by this phenomenon, our latest effort…

June 2017

Microsoft Research Blog

Maluuba Question Generation video

Advancing machine comprehension with question generation

Microsoft Research Montreal lab’s vision is to create machines that can comprehend, reason and communicate with humans. We see a future where humans interact with machines just as they would with another human. We could ask a question in natural language and have the machine respond with an appropriate answer. Yet answering questions is only one part of an interaction. In addition to our work in training machines to seek information and then read and reason upon text and…

May 2017

Microsoft Research Blog