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Modularized and Attention-Based Recurrent Convolutional Neural Network for Automatic Academic Paper Aspect Scoring

  • Feng QiaoEmail author
  • Lizhen Xu
  • Xiaowei Han
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11242)

Abstract

Thousands of academic papers are submitted at top venues each year. Manual audits are time-consuming and laborious. And the result may be influenced by human factors. This paper investigates a modularized and attention-based recurrent convolutional network model to represent academic paper and predict aspect scores. This model treats input text as module-document hierarchies, uses attention pooling CNN and LSTM to represent text, and outputs prediction with a linear layer. Empirical results on PeerRead data show that this model give the best performance among the baseline models.

Keywords

Aspect scoring Convolutional neural network Deep learning LSTM Attention pooling 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringSoutheast University NanjingNanjingChina

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