Sebastian Riedel

Title:  Reading and Reasoning with Vector Representations

Abstract:
In recent years, vector representations of knowledge have become popular in NLP and beyond. 
They have at least two core benefits: reasoning with (low-dimensional) vectors tends to lead to better generalisation, 
and usually scales very well. But they raise their own set of questions: What type of inferences do they support? 
How can they capture asymmetry? How can explicit background knowledge be injected into vector-based architectures? 
How can we provide "proofs" that justify predictions? In this talk, I sketch some initial answers to these questions 
based on work we have developed recently. In particular, I will illustrate how a vector space can simulate the workings of logic.