Can Siamese Networks help in stance detection?

An important component of fake news detection is to evaluate the stance, different news sources take towards the assertion. Automatic stance detection, would facilitate the process of fact checking. In this paper, we present our stance detection system which comprises of siamese adaptation of Long Short Term Memory (LSTM) networks augmented with an attention mechanism, as siamese adaptation forces the LSTM to entirely capture the semantic differences during training, rather than supplementing the network with a more complex learner that can help resolve shortcomings in the learned representations. Our experiments on a public benchmark dataset, FakeNewsChallenge (FNC), demonstrate the effectiveness of our approach. It focuses on classifying the stance of a news article body relative to a headline as agree, disagree, discuss, or unrelated.