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
Machine learning models benefit from transfer learning. In computer vision, neural networks trained on a large dataset are useful for initialising models on other vision tasks. How can we leverage the transfer learning technique for text?
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?
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 modelling 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 a 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.
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 converts hand-written drawings to HTML prototypes. Designers share ideas on a whiteboard, then changes are shown instantly in the browser – helping to 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.
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
Make artificial intelligence real for your business today.
Create innovative AI solutions
Discover Azure AI – a portfolio of AI services designed for developers and data scientists. Take advantage of the decades of breakthrough research, responsible AI practices and flexibility that Azure AI offers to build and deploy your own AI solutions.