Research in natural language processing (NLP) has seen striking advances in recent years but most of this success has focused on English. In this talk, I will give an overview of approaches that transfer knowledge across languages and enable us to scale NLP models to more of the world's 7,000 languages. I will cover open challenges in this area such as evaluation in the face of limited labelled data, generalizing to low-resource languages and different scripts, and dealing with erroneous segmentations and discuss approaches that help mitigate them.
Sebastian Ruder is a research scientist in the Language team at DeepMind, London. He completed his PhD in Natural Language Processing and Deep Learning at the Insight Research Centre for Data Analytics, while working as a research scientist at Dublin-based text analytics startup AYLIEN. Previously, he studied Computational Linguistics at the University of Heidelberg, Germany and at Trinity College, Dublin. He is interested in transfer learning for NLP and making ML and NLP more accessible.
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