Reading and Reasoning with Neural Program Interpreters

We are getting better at teaching end-to-end neural models how to answer questions about content in natural language text. However, progress has been mostly restricted to extracting answers that are directly stated in text. In this talk, I will present our work towards teaching machines not only to read, but also to reason with what was read and to do this in a interpretable and controlled fashion. Our main hypothesis is that this can be achieved by the development of neural abstract machines that follow the blueprint of program interpreters for real-world programming languages. We test this idea using two languages: an imperative (Forth) and a declarative (Prolog/Datalog) one. In both cases we implement differentiable interpreters that can be used for learning reasoning patterns. Crucially, because they are based on interpretable host languages, the interpreters also allow users to easily inject prior knowledge and inspect the learnt patterns. Moreover, on tasks such as math word problems and relational reasoning our approach compares favourably to state-of-the-art methods.