Discrete Argument Representation Learning for Interactive Argument Pair Identification

Lu Ji, Zhongyu Wei, Jing Li, Qi Zhang, Xuanjing Huang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


In this paper, we focus on identifying 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.
Original languageEnglish
Title of host publication2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Number of pages12
Publication statusPublished - 6 Jun 2021


  • Natural Language Processing
  • argumentation mining


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