Robust Language Representation Learning via Multi-task Knowledge Distillation
Language Representation Learning maps symbolic natural language texts (for example, words, phrases and sentences) to semantic vectors. Robust and universal language representations are crucial to achieving state-of-the-art results on many Natural Language Processing (NLP) tasks.…
Speech and language: the crown jewel of AI with Dr. Xuedong Huang
Episode 76, May 15, 2019 When was the last time you had a meaningful conversation with your computer… and felt like it truly understood you? Well, if Dr. Xuedong Huang, a Microsoft Technical Fellow and…
Content Transfer through Grounded Text Generation
Recent work in neural generation has attracted significant interest in controlling the form of text, such as style, persona, and politeness. However, there has been less work on controlling neural text generation for content. This…
Azure Speech Service Vision Keynote Demo // Microsoft Build 2019
Our latest research progress enables dynamic creation of a virtual microphone array with a set of existing devices equipped with ordinary microphones. It helps our customers more easily transcribe conversations at anytime anywhere. Also, Azure…
Microsoft @ ICASSP 2019
Microsoft is excited to be a Silver sponsor of the 44th International Conference on Acoustics, Speech, and Signal Processing (ICASSP) May 12 – 17, 2019, in Brighton, UK.
SpaceFusion: Structuring the unstructured latent space for conversational AI
A palette makes it easy for painters to arrange and mix paints of different colors as they create art on the canvas before them. Having a similar tool that could allow AI to jointly learn…
New Advancements in Spoken Language Processing
Deep learning algorithms, supported by the availability of powerful Azure computing infrastructure and massive training data, constitutes the most significant driving force in our AI evolution journey. In the past three years, Microsoft reached several…
Bing Artificial Search Sessions
Bing Artificial Search Sessions(BASS) is a collection of 18m Artificial Search session that were created by taking real conversational Search Sessions and mapping them to publicly available queries using vector space embeddings.