Attention-based argumentation mining

Derwin Suhartono, Aryo Pradipta Gema, Suhendro Winton, Theodorus David, Mohamad Ivan Fanany, Aniati Murni Arymurthy

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


This paper is intended to make a breakthrough in argumentation mining field. Current trends in argumentation mining research use handcrafted features and traditional machine learning (e.g., support vector machine). We worked on two tasks: identifying argument components and recognising insufficiently supported arguments. We utilise deep learning approach and implement attention mechanism on top of it to gain the best result. We do also implement Hierarchical Attention Network (HAN) in this task. HAN is a neural network that gives attention to two levels, which are word-level and sentence-level. Deep learning with attention mechanism models can achieve better result compared with other deep learning methods. This paper also proves that on research task with hierarchically-structured data, HAN will perform remarkably well. We do present our result on using XGBoost instead of a regular non-ensemble classifier as well.

Original languageEnglish
Pages (from-to)414-437
Number of pages24
JournalInternational Journal of Computational Vision and Robotics
Issue number5
Publication statusPublished - 1 Jan 2019


  • Argumentation mining
  • Attention mechanism
  • Deep learning
  • Hand-crafted features
  • Hierarchical attention network
  • Sentence-level
  • Word-level
  • XGBoost


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