Stance Detection with Bidirectional Conditional Encoding

Understand

Stance detection is the task of classifying the attitude expressed in a text towards a target such as Hillary Clinton to be "positive", negative" or "neutral".

• Previous work has assumed that either the target is mentioned in the text or that training data for every target is given.
• This paper considers the more challenging version of this task, where targets are not always mentioned and no training data is available for the test targets.
• We experiment with conditional LSTM encoding, which builds a representation of the tweet that is dependent on the target, and demonstrate that it outperforms encoding the tweet and the target independently.