Discrete Argument Representation Learning for Interactive Argument Pair Identification
This work addresses the challenge of identifying argument pairs in online discussions, which is incremental as it builds on existing methods with a novel representation approach.
The paper tackles the problem of extracting interactive argument pairs from posts with opposite stances by learning discrete representations to capture aspects like debate focus and participant behavior, and it reports significantly outperforming competitive baselines on a large-scale CMV dataset.
In this paper, we focus on extracting interactive argument pairs from two posts with opposite stances to a certain topic. Considering opinions are exchanged from different perspectives of the discussing topic, we study the discrete representations for arguments to capture varying aspects in argumentation languages (e.g., the debate focus and the participant behavior). Moreover, we utilize hierarchical structure to model post-wise information incorporating contextual knowledge. Experimental results on the large-scale dataset collected from CMV show that our proposed framework can significantly outperform the competitive baselines. Further analyses reveal why our model yields superior performance and prove the usefulness of our learned representations.