If you are interested in a PhD in any of the topics below contact me!
Prime minster said: ”Our government has halved youth unemployment!” True or False? Fact checking is one of the main tasks performed by journalists, especially in an era in which information sources abound. In our first paper in 2014 we discussed the main challenges, namely the open domain nature of the task and the importance of context. We followed this up by papers on distantly supervised approach for fact-checking simple claims about statistical properties (EMNLP2015, EACL2017) and automating the debunking process of journalists (NAACL2016). We are collaborating with our project partners in the EU-funded SUMMA project and co-organizing the Fact Extraction and Verification workshop using the 200K claims dataset described in this NAACL2018 paper. See this recent presentation at the NIPS2017 workshop on Prioritizing Online Content for an overview, as well as some media coverage in the outreach.
Imitation Learning for Structured Prediction
Imitation learning is a paradigm originally developed in robotics that has been applied successfully to a variety of structured prediction tasks in NLP. Intuitively, it decomposes the usually complex output (e.g. a graph) to a sequence of actions that construct it. These actions are predicted by a suitalby trained policy. This framework has the advantage of being able to learn policies with non-decomposable loss functions without explicit enumeration of the output search space and has been applied successfully to a variety of applications, including information extraction (BMC Bioinformatics or EMNLP2015), semantic parsing (TACL2014, ACL2016) and natural language generation (Coling2016). We are collaborating with the Heriot Watt University NLP lab) and the UCL Machine Reading Group in the EPSRC-funded project Diligent. For an overview of imitation learning for structured prediction in NLP see our EACL2017 tutorial. Also look at our implementation of imitation learning around the excellent scikit-learn to use it in your work.