Machine Learning seminar
Who: Sebastian Riedel, University College London
Title: Reading and Reasoning with Neural Program Interpreters
Abstract: 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 the 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 an 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.
Sebastian Riedel is a reader in Natural Language Processing and Machine Learning at the University College London (UCL), where he is leading the Machine Reading lab. He is also the head of research at Bloomsbury AI and an Allen Distinguished Investigator. He works in the intersection of Natural Language Processing and Machine Learning, and focuses on teaching machines how to read and reason. He was educated in Hamburg-Harburg (Dipl. Ing) and Edinburgh (MSc., PhD), and worked at the University of Massachusetts Amherst and Tokyo University before joining UCL.
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