8 March 2019 - Andreas Vlachos: Seminar
Imitation learning, zero-shot learning and automated fact checking
In this talk I will give an overview of my research in machine learning for natural language processing. I will begin by introducing my work on imitation learning, a machine learning paradigm I have used to develop novel algorithms for structure prediction that have been applied successfully to a number of tasks such as semantic parsing, natural language generation and information extraction. Key advantages are the ability to handle large output search spaces and to learn with non-decomposable loss functions. Following this, I will discuss my work on zero-shot learning using neural networks, which enabled us to learn models that can predict labels for which no data was observed during training. I will conclude with my work on automated fact-checking, a challenge we proposed in order to stimulate progress in machine learning, natural language processing and, more broadly, artificial intelligence.
Since October 2018, I am a senior lecturer at the Natural Language and Information Processing group at the Department of Computer Science and Technology at the University of Cambridge. Current projects include natural language generation, automated fact checking and imitation learning. I have also worked on semantic parsing, language modelling, information extraction, active learning, clustering and biomedical text mining. Prior to this I was a lecturer at the University of Sheffield, working on the intersection of Natural Language Processing and Machine Learning. Previously I was a postdoc at the Machine Reading group at UCL working with Sebastian Riedel, at the NLIP group at the University of Cambridge working with Stephen Clark and at the University of Wisconsin-Madison working with Mark Craven. I did my PhD at the Unversity of Cambridge with Ted Briscoe and Zoubin Ghahramani.