Research
Advancing how machines understand and generate human language across speech, text, and translation.
The need for labeled data is one of the largest bottlenecks in training supervised learning models like deep neural networks. This is especially the case for many real-world tasks where large scale annotated examples are…
Deep neural networks including pre-trained language models like BERT, Turing-NLG and GPT-3 require thousands of labeled training examples to obtain state-of-the-art performance for downstream tasks and applications. Such large number of labeled examples are difficult…
Modern machine learning applications have enjoyed a great boost utilizing deep and large neural network models, allowing them to achieve state-of-the-art results on a wide range of tasks such as question-answering, conversational AI, search and…
Microsoft is proud to be a Diversity and Inclusion Champion sponsor of the 58th Annual Meeting of the Association for Computational Linguistics (ACL) happening on July 5-10, 2020. See the details on our contributions to…
