Learning across Adverse Conditions in Natural Language Processing


Transferring knowledge to solve a related problem and learning from limited, unreliable inputs are examples of extraordinary human ability. State-of-the-art machine learning models based on deep learning often fail under such adverse conditions. So, how can we build Natural Language Processing technology which transfers better to new conditions, such as learning to process a new language, learning to answer new types of questions, or learning with uncertainty stemming from human labelling? In this talk, I will present some recent work to address these ubiquitous challenges using neural networks in NLP. In particular, I will include work on cross-lingual learning for NLP and work at the interface of language and vision.

About the speaker

Barbara Plank Barbara Plank is Associate Professor of Natural Language Processing (NLP) in the Computer Science Department at ITU (IT University of Copenhagen) where she leads a research lab in natural language processing.​​​