Recognising Agreement and Disagreement between Stances with Reason Comparing Networks
This work addresses stance analysis for natural language processing applications, but it is incremental as it extends existing methods to a broader setting.
The paper tackles the problem of detecting agreement and disagreement between stance-bearing utterances in non-dialogic settings, where existing methods rely on conversational features, and shows that using reason information improves performance, with empirical results on a stance corpus demonstrating it outperforms several baselines.
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.