Explicit Interaction Model towards Text Classification


  • Cunxiao Du Shandong University
  • Zhaozheng Chen Shandong University
  • Fuli Feng National University of Singapore
  • Lei Zhu Shandong Normal Unversity
  • Tian Gan Shandong University
  • Liqiang Nie Shandong University




Text classification is one of the fundamental tasks in natural language processing. Recently, deep neural networks have achieved promising performance in the text classification task compared to shallow models. Despite of the significance of deep models, they ignore the fine-grained (matching signals between words and classes) classification clues since their classifications mainly rely on the text-level representations. To address this problem, we introduce the interaction mechanism to incorporate word-level matching signals into the text classification task. In particular, we design a novel framework, EXplicit interAction Model (dubbed as EXAM), equipped with the interaction mechanism. We justified the proposed approach on several benchmark datasets including both multilabel and multi-class text classification tasks. Extensive experimental results demonstrate the superiority of the proposed method. As a byproduct, we have released the codes and parameter settings to facilitate other researches.




How to Cite

Du, C., Chen, Z., Feng, F., Zhu, L., Gan, T., & Nie, L. (2019). Explicit Interaction Model towards Text Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 6359-6366. https://doi.org/10.1609/aaai.v33i01.33016359



AAAI Technical Track: Natural Language Processing