GPU-accelerated PyTorch implementation of Zero-shot User Intent Detection via Capsule Neural Networks

This repository implements a capsule model IntentCapsNet-ZSL on the SNIPS-NLU dataset in Python 3 with PyTorch, first introduced in the paper Zero-shot User Intent Detection via Capsule Neural Networks.

The code aims to follow PyTorch best practices, using torch instead of numpy where possible, and using .cuda() for GPU computation. Feel free to contribute via pull requests.

Requirements

python 3.6+

torch 1.0.1

numpy

gensim

scikit-learn

Usage and Modification

• To run the training-validation loop: python run.py.
• The custom Dataset class is implemented in dataset.py.

Acknowledgements

• Original repository (TensorFlow, Python 2): https://github.com/congyingxia/ZeroShotCapsule
• Re-implementation (PyTorch, Python 2): https://github.com/nhhoang96/ZeroShotCapsule-PyTorch-

Please see the following paper for the details:

Congying Xia, Chenwei Zhang, Xiaohui Yan, Yi Chang, Philip S. Yu. Zero-shot User Intent Detection via Capsule Neural Networks. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2018.

https://arxiv.org/abs/1809.00385

@article{xia2018zero,
  title={Zero-shot User Intent Detection via Capsule Neural Networks},
  author={Xia, Congying and Zhang, Chenwei and Yan, Xiaohui and Chang, Yi and Yu, Philip S},
  journal={arXiv preprint arXiv:1809.00385},  
  year={2018}
}

References

• https://github.com/soskek/dynamic_routing_between_capsules
• https://github.com/ExplorerFreda/Structured-Self-Attentive-Sentence-Embedding