Recognising Agreement and Disagreement between Stances with Reason
Comparing Networks
Abstract
We identify agreement and disagreement between utterances that express stances towards
a topic of discussion. Existing methods focus mainly on conversational settings, where
dialogic features are used for (dis)agreement
inference. We extend this scope and seek
to detect stance (dis)agreement in a broader
setting, where independent stance-bearing utterances, which prevail in many stance corpora and real-world scenarios, are compared.
To cope with such non-dialogic utterances,
we find that the reasons uttered to back
up a specific stance can help predict stance
(dis)agreements. We propose a reason comparing network (RCN) to leverage reason information for stance comparison. Empirical
results on a well-known stance corpus show
that our method can discover useful reason information, enabling it to outperform several
baselines in stance (dis)agreement detection.