DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding


  • Tao Shen University of Technology Sydney
  • Tianyi Zhou University of Washington
  • Guodong Long University of Technology Sydney
  • Jing Jiang University of Technology Sydney
  • Shirui Pan University of Technology Sydney
  • Chengqi Zhang University of Technology Sydney




Deep Learning, Attention Mechanism, Natural Language Processing, Sentence Encoding, Text Classification


Recurrent neural nets (RNN) and convolutional neural nets (CNN) are widely used on NLP tasks to capture the long-term and local dependencies, respectively. Attention mechanisms have recently attracted enormous interest due to their highly parallelizable computation, significantly less training time, and flexibility in modeling dependencies. We propose a novel attention mechanism in which the attention between elements from input sequence(s) is directional and multi-dimensional (i.e., feature-wise). A light-weight neural net, "Directional Self-Attention Network (DiSAN)," is then proposed to learn sentence embedding, based solely on the proposed attention without any RNN/CNN structure. DiSAN is only composed of a directional self-attention with temporal order encoded, followed by a multi-dimensional attention that compresses the sequence into a vector representation. Despite its simple form, DiSAN outperforms complicated RNN models on both prediction quality and time efficiency. It achieves the best test accuracy among all sentence encoding methods and improves the most recent best result by 1.02% on the Stanford Natural Language Inference (SNLI) dataset, and shows state-of-the-art test accuracy on the Stanford Sentiment Treebank (SST), Multi-Genre natural language inference (MultiNLI), Sentences Involving Compositional Knowledge (SICK), Customer Review, MPQA, TREC question-type classification and Subjectivity (SUBJ) datasets.




How to Cite

Shen, T., Zhou, T., Long, G., Jiang, J., Pan, S., & Zhang, C. (2018). DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.11941