Grammarly.AI Club #3: Learning to Read for Automated Fact Checking
The spread of misinformation and disinformation is growing, and it’s 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 which takes place on the 22nd of November 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.
Who will be interested:
The topic of the talk will be relevant to people working in machine learning, natural language processing, information retrieval, knowledge base engineering, or political science. To understand the topic, basic knowledge of machine learning and NLP is required.
Isabelle Augenstein, Assistant Professor, University of Copenhagen. 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 EMNLP 2017.
Price: 150 UAH (hot drinks&sweets are included).
Location: Creative space "Сhasopys", Lva Tolstovo, 3.
Date: Wednesday, November 22nd, 7 PM.
The number of tickets is limited! You can register and buy tickets here.