Attention-based argumentation mining. (11th September 2019) - Suhartono, Derwin; Gema, Aryo Pradipta; Winton, Suhendro; David, Theodorus; Fanany, Mohamad Ivan; Arymurthy, Aniati Murni
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.