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http://dx.doi.org/10.6109/jkiice.2021.25.9.1275

Automated Fact Checking Model Using Efficient Transfomer  

Yun, Hee Seung (Department of Computer Engineering, Chung-Ang University)
Jung, Jason J. (Department of Computer Engineering, Chung-Ang University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.9, 2021 , pp. 1275-1278 More about this Journal
Abstract
Nowadays, fake news from newspapers and social media is a serious issue in news credibility. Some of machine learning methods (such as LSTM, logistic regression, and Transformer) has been applied for fact checking. In this paper, we present Transformer-based fact checking model which improves computational efficiency. Locality Sensitive Hashing (LSH) is employed to efficiently compute attention value so that it can reduce the computation time. With LSH, model can group semantically similar words, and compute attention value within the group. The performance of proposed model is 75% for accuracy, 42.9% and 75% for Fl micro score and F1 macro score, respectively.
Keywords
Automated fact checking; Locality sensitive hashing; Natural language processing; Transformer;
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