“Automated Fact Checking: a natural language processing perspective”
14 September 2020, 12:30-13:30 CEST
Fact checking is the task of verifying a claim against sources such as knowledge bases and text collections. While this task has been of great importance for journalism, it has recently become of interest to the general public as it is one of the weapons against misinformation. In this talk, I will first discuss the task and what should be the expectations from automated methods for it. Following this, I will present our approach for fact checking simple numerical statements which we were able to learn without explicitly labelled data. Then I will describe how we automated part of the manual process of the debunking website emergent.info, which later evolved into the Fake News Challenge with 50 participants. Finally, I will present the Fact Extraction and Verification shared task, which took place in 2018, as well as the recent second edition which conducted an adversarial evaluation of the proposed approaches.
I am a senior lecturer at the Natural Language and Information Processing group at the Department of Computer Science and Technology at the University of Cambridge. Current projects include natural language generation, automated fact checking and imitation learning. I have also worked on semantic parsing, summarization, language modelling, information extraction, active learning, clustering and biomedical text mining. My research is supported by ERC, Facebook, Google, Amazon and Huawei.
Prior to this I was a lecturer at the University of Sheffield, working on the intersection of Natural Language Processing and Machine Learning. Previously I was a postdoc at the Machine Reading group at UCL working with Sebastian Riedel, at the NLIP group at the University of Cambridge working with Stephen Clark and at the University of Wisconsin-Madison working with Mark Craven. I did my PhD at the Unversity of Cambridge with Ted Briscoe and Zoubin Ghahramani.
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