Fake News Detection as Natural Language Inference
This work addresses fake news classification for online content moderation, but it is incremental as it applies existing NLI methods to a specific challenge.
The authors tackled fake news detection by treating it as a natural language inference (NLI) problem, using an ensemble of NLI models and BERT with noisy label retraining and transitivity analysis, achieving 88.063% accuracy and third place in the WSDM 2019 competition.
This report describes the entry by the Intelligent Knowledge Management (IKM) Lab in the WSDM 2019 Fake News Classification challenge. We treat the task as natural language inference (NLI). We individually train a number of the strongest NLI models as well as BERT. We ensemble these results and retrain with noisy labels in two stages. We analyze transitivity relations in the train and test sets and determine a set of test cases that can be reliably classified on this basis. The remainder of test cases are classified by our ensemble. Our entry achieves test set accuracy of 88.063% for 3rd place in the competition.