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…
Access and Understanding in the Classroom: How Deaf Children Learn (or not)
Marc Marschark, Ph.D. Center for Education Research Partnerships National Technical Institute for the Deaf – Rochester Institute of Technology For more than 100 years, investigators have taken a keen interest in language and cognition of…
Using Deep Learning to Understand Creative Language
Creative language – the sort found in novels, film, and comics – contains a wide range of linguistic phenomena, from phrasal and sentential syntactic complexity to high-level discourse structures such as narrative and character arcs.…
Learning Language Through Interaction
Natural language processing systems build using machine learning techniques are amazingly effective when plentiful labeled training data exists for the task/domain of interest. Unfortunately, for broad coverage (both in task and domain and language) language…
Building a Machine that Can Learn to Understand, Reason and Learn
My talk will first briefly review recent advances in memory augmented neural nets and then present my own contribution, Neural Semantic Encoders (NSE). With a special focus on NSE, I show that external memory in…