Machine Reading Comprehension
MRC scans documents and extracts meaning from the text, just like a human reader. You can ask MRC questions about a document and it will use different parts of the content until an answer is formed.
Try out MRC
The need
Machine learning models benefit from transfer learning. In computer vision, neural networks trained on a large dataset are useful for initializing models on other vision tasks. How can we leverage the transfer learning technique for text?
The idea
Suppose you have a very domain specific question and you want to get the best answer from a document. What if we could read thousands of documents and then use AI to answer that question with the best context and inference?
The solution
Using a neural network called the Reasoning Network (ReasoNet), researchers mimic the inference process of human readers. With a question in mind, ReasoNet scans a document and focuses on different parts until it gets an answer.
Machine Reading Comprehension
Using a novel neural network architecture called the Reasoning Network (ReasoNet), we were able to mimic the inference process of human readers.
Technical details of Machine Reading
MRC requires modeling complex interactions between the context and the query. Using a novel neural network architecture called the Reasoning Network (ReasoNet), researchers were able to mimic the inference process of human readers.
With a question in mind, ReasoNet reads a document repeatedly, each time focusing on different parts of the document until a satisfying answer is found or formed. Microsoft researchers today have been able to surpass human-level parity on SQuAD dataset using an unique MRC algorithm called R-NET: Machine reading comprehension with self-matching networks. R-NET applies a self-matching attention mechanism to refine the representation by matching the passage against itself, which effectively encodes information from the whole passage.
When we applied these MRC algorithms to the book, Future Computed by Brad Smith and Harry Shum, it was incredible to see that we can answer so many interesting questions. We can apply this to solve enterprise data challenges and answer enterprise domain specific questions.
Resources:
Pix2Story
Pix2Story uses Natural Language Processing (NLP) for storytelling. AI scans a picture, applies a writing style, and generates a story—demonstrating how AI can drive creativity.
Sketch2Code
Sketch2Code converts hand-written drawings to HTML prototypes. Designers share ideas on a whiteboard, then changes are shown instantly in the browser—helping improve collaboration between the designer, developer, and customer.
Responsible Conversational AI
Conversational AI is a new way for companies to interact with their customers across any channel, like digital assistants, chat or social media. To be effective, conversational bots need to be developed in a way that earns people’s trust.
Machine Teaching
Explore the concepts of machine teaching, allowing developers or subject matter experts with little AI expertise to provide abstract concepts to an intelligent system.
Explore the possibilities of AI
Jumpstart your own AI innovations with learning resources and development solutions from Microsoft AI.
Innovation Tech Hub
Explore insights and behind-the-scenes technology for breakthrough AI innovations. From Tech Minutes videos to Technology Deep Dives, learn about the engineering that powers the future of AI.
AI School
Learn to create your own AI experiences with learning paths in conversational AI, machine learning, AI for devices, cognitive services, autonomous systems, AI strategy, and more.
AI platform
Start building AI solutions with powerful tools and services. Microsoft AI is a robust framework for developing AI solutions in conversational AI, machine learning, data sciences, robotics, IoT, and more.
