The present disclosure relates to a method and attention neural network for automatically learning embeddings for various latent aspects of textual claims and documents performed in an attention neural network comprising one or more latent aspect models for guiding an attention mechanism of the neural network, wherein the method comprises the steps of inserting a claim document pair, in each of the latent aspect models and a latent aspect vector to select significant sentences to form document representations for each respective latent aspect of the latent aspect vector, concatenating the document representations to establish an overall document representation, calculating a class probability distribution by means of the overall document representation, and classifying the claim of document as true or false using the class probability distribution.
DEEP NEURAL ARCHITECTURES FOR DETECTING FALSE CLAIMS