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Seminar: DeepFact: Deep Learning for Automated Fact Checking. Vinay Setty, University of Stavanger
April 22 @ 12:00 - 13:00
Associate Professor Vinay Setty from the University of Stavanger will hold a seminar summarizing the latest updates in the topic of automated fact checking. The video recording is available below.
Welcome to all!
TITLE: Deep Learning for Automated Fact Checking.
WHEN: 22 April 2021, 12:00-13:00.
ABSTRACT: The interest around automated fact-checking has increased as misinformation has become a major problem online. A typical pipeline for an automated fact-checking system consists of four steps: (1) detecting check-worthy claims, (2) retrieving relevant documents, (3) selecting most relevant snippets for the claim and (4) predicting the veracity of the claim. In this talk, I will talk about the use of state-of-the-art deep neural networks such as LSTMs and Transformer architectures for these steps. Specifically, how deep hierarchical attention networks can be used for predicting the veracity of the claims and how to use the attention weights to extract the evidence for the claims. In addition, I will also talk about how to do check-worthy claim detection using Transformer models. Using several benchmarks from political debates and manual fact checking websites such as Politifact and Snopes, we show that these models outperform strong baselines. I will also summarize the state-of-the-art research within the areas of automated fact-checking and conclude with a set of challenges and problems remaining in this area.
BIO: Dr. Vinay Setty is an Associate Professor at the Department of Electrical Engineering and Computer Science. Before that he has been an Assistant Professor at Aalborg University in Denmark and Postdoctoral Researcher at Max Planck Institute for Informatics. Setty got is PhD from University of Oslo, Norway.
Dr. Setty’s recent research areas mainly include information retrieval, text and graph mining using machine learning techniques. Text mining includes dealing with unstructured text, specifically news documents for tasks such as fake news detection, news ranking, news recommendation etc. Graph mining involves training network embeddings for machine learning on graphs and knowledge graphs. He has over 30 publications including several publications in highly competitive conferences in the area of data mining and Information Retrieval TheWebConf, SIGIR, VLDB, CIKM and WSDM.