Title: Attention-based argumentation mining

Authors: Derwin Suhartono; Aryo Pradipta Gema; Suhendro Winton; Theodorus David; Mohamad Ivan Fanany; Aniati Murni Arymurthy

Addresses: Computer Science Department, School of Computer Science, Bina Nusantara University, Jl. K.H. Syahdan No. 9 Kemanggisan, Jakarta 11480, Indonesia; Machine Learning and Computer Vision Laboratory, Faculty of Computer Science, Universitas Indonesia, Depok 16424, Indonesia ' Computer Science Department, School of Computer Science, Bina Nusantara University, Jl. K.H. Syahdan No. 9 Kemanggisan, Jakarta 11480, Indonesia ' Computer Science Department, School of Computer Science, Bina Nusantara University, Jl. K.H. Syahdan No. 9 Kemanggisan, Jakarta 11480, Indonesia ' Computer Science Department, School of Computer Science, Bina Nusantara University, Jl. K.H. Syahdan No. 9 Kemanggisan, Jakarta 11480, Indonesia ' Machine Learning and Computer Vision Laboratory, Faculty of Computer Science, Universitas Indonesia, Depok 16424, Indonesia ' Machine Learning and Computer Vision Laboratory, Faculty of Computer Science, Universitas Indonesia, Depok 16424, Indonesia

Abstract: 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.

Keywords: argumentation mining; hand-crafted features; deep learning; attention mechanism; hierarchical attention network; word-level; XGBoost; sentence-level.

DOI: 10.1504/IJCVR.2019.102282

International Journal of Computational Vision and Robotics, 2019 Vol.9 No.5, pp.414 - 437

Received: 14 Feb 2018
Accepted: 12 Apr 2018

Published online: 11 Sep 2019 *

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