Colloquium: Imitation learning, zero-shot learning, and automated fact checking

9 July 2018

Abstract. 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.

Monday, 09 July 2018, 17:15, Konrad-Zuse Hörsaal, B-201, Informatics Campus, Stellingen

Speaker: Prof. Dr. Andreas Vlachos, University of Sheffield, UK.