The number of people learning English around the world is currently estimated at 1.5 billion and is predicted to exceed 1.9 billion by 2020. The increasing need to communicate beyond borders has created a large unmet demand for qualified language teachers across the globe. Computational models for error detection and essay scoring can alleviate this issue by giving millions of people access to affordable learning resources. Successful systems for automated language teaching will need to analyse language at various levels of granularity and provide useful feedback to individual students.In this talk, we will explore some of the latest approaches to written language assessment, using neural architectures for composing the meaning of a sentence or text, and also discuss potential future directions in the field.
Dr Marek Rei is a Senior Research Associate in the University of Cambridge and a Fellow of King's College in Cambridge. His primary research interests are in the areas of distributional and compositional semantics, neural architectures for multi-task learning, and applications in automated language assessment. He received his PhD from Cambridge, with a thesis on semi-supervised learning methods for NLP. After finishing the studies he worked for SwiftKey, a technology start-up now acquired by Microsoft, designing neural network models for machine learning applications on mobile devices. He continues to actively collaborate with industry partners, currently advising several start-up companies in the areas of machine learning and language processing.