Fake News Detection as Natural Language Inference
Published in
arXiv.org(2019)External Links:
TL
TL;DR
This report describes the entry by the Intelligent Knowledge Management (IKM) Lab in the WSDM 2019 Fake News Classification challenge, treating the task as natural language inference (NLI), and individually train a number of the strongest NLI models as well as BERT.
Abstract
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.
References13 items
1
Directional Skip-Gram: Explicitly Distinguishing Left and Right Context for Word Embeddings
2
Semantic Sentence Matching with Densely-connected Recurrent and Co-attentive Information
3
Analogical Reasoning on Chinese Morphological and Semantic Relations
4
Language Modeling with Gated Convolutional Networks
5