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Learning to Read for Automated Fact Checking

Spreading of mis- and disinformation is growing and is having a big impact on interpersonal communications, politics and even science.

Traditional methods, e.g. manual fact-checking by reporters cannot keep up with the growth of information. On the other hand, there has been much progress in natural language processing recently, partly due to the resurgence of neural methods. How can natural language processing methods fill this gap and help to automatically check facts?

This talk will explore different ways to frame fact checking and detail our ongoing work on learning to encode documents for automated fact checking, as well as describe future challenges.


Isabelle Augenstein is a a tenure-track assistant professor at the University of Copenhagen, Department of Computer Science, affiliated with the CoAStAL NLP group and work in the general areas of Statistical Natural Language Processing and Machine Learning. Her main research interests are weakly supervised and low-resource learning with applications including information extraction, machine reading and fact checking. Previously she was a postdoctoral research associate at UCL and was awarded a PhD from the University of Sheffield. She has recently co-organised the Deep Structured Prediction workshop at ICML 2017, the WiNLP workshop at ACL 2017 and was one of the organizers of EMNLP 2017.