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Hierarchical Attention Network with XGBoost for Recognizing Insufficiently Supported Argument

  • Derwin Suhartono
  • Aryo Pradipta Gema
  • Suhendro Winton
  • Theodorus David
  • Mohamad Ivan Fanany
  • Aniati Murni Arymurthy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10607)

Abstract

In this paper, we propose the empirical analysis of Hierarchical Attention Network (HAN) as a feature extractor that works conjointly with eXtreme Gradient Boosting (XGBoost) as the classifier to recognize insufficiently supported arguments using a publicly available dataset. Besides HAN + XGBoost, we performed experiments with several other deep learning models, such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), bidirectional LSTM, and bidirectional GRU. All results with the best hyper-parameters are presented. In this paper, we present the following three key findings: (1) Shallow models work significantly better than the deep models when using only a small dataset. (2) Attention mechanism can improve the deep model’s result. In average, it improves Area Under the Receiver Operating Characteristic Curve (ROC-AUC) score of Recurrent Neural Network (RNN) with a margin of 18.94%. The hierarchical attention network gave a higher ROC-AUC score by 2.25% in comparison to the non-hierarchical one. (3) The use of XGBoost as the replacement for the last fully connected layer improved the F1 macro score by 5.26%. Overall our best setting achieves 1.88% improvement compared to the state-of-the-art result.

Keywords

Hierarchical Attention Network XGBoost Insufficiently supported argument Shallow learning Deep learning 

Notes

Acknowledgments

This research was fully funded by “Penelitian Disertasi Doktor” from Ministry of Research, Technology and Higher Education of Indonesia with contract number 039A/VR.RTT/VI/2017.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Derwin Suhartono
    • 1
    • 2
  • Aryo Pradipta Gema
    • 1
  • Suhendro Winton
    • 1
  • Theodorus David
    • 1
  • Mohamad Ivan Fanany
    • 2
  • Aniati Murni Arymurthy
    • 2
  1. 1.Computer Science Department, School of Computer ScienceBina Nusantara UniversityJakartaIndonesia
  2. 2.Machine Learning and Computer Vision (MLCV) LaboratoryUniversitas IndonesiaDepokIndonesia

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