Recognising Agreement and Disagreement between Stances with Reason Comparing Networks
Chang Xu, Cécile L. Paris, Surya Nepal, Ross Stewart Sparks · 2019
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.