Stance Detection with Bidirectional Conditional Encoding

Isabelle Augenstein, Tim Rocktäschel, Andreas Vlachos, Kalina Bontcheva

Abstract:

Stance detection is the task of classifying the
attitude expressed in a text towards a target
such as “Climate Change is a Real Concern”
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 the independent encoding
of tweet and target. Performance improves
even further when the conditional model is
augmented with bidirectional encoding. The
method is evaluated on the SemEval 2016
Task 6 Twitter Stance Detection corpus and
achieves performance second best only to a
system trained on semi-automatically labelled
tweets for the test target. When such weak
supervision is added, our approach achieves
state–of-the-art results.