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Learning to Classify Open Intent via Soft Labeling and Manifold Mixup

This repo contains the code of our TASLP'2022 paper:

Learning to Classify Open Intent via Soft Labeling and Manifold Mixup

Requirements

• Python 3.6
• PyTorch 1.8.0
• transformers 2.8.0
• pytorch_pretrained_bert 0.6.2

Model Preparation

Get the pre-trained BERT model and convert it into Pytorch.

Set the path of the uncased-bert model (parameter "bert_model" in init_parameter.py).

Quick Start

Run our model:

bash run_0.25_oos.sh

If you are insterested in this work, and want to use the codes or results in this repository, please star this repository and cite by:

@article{Cheng22,
  author={Cheng, Zifeng and Jiang, Zhiwei and Yin, Yafeng and Wang, Cong and Gu, Qing},
  journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing}, 
  title={Learning to Classify Open Intent via Soft Labeling and Manifold Mixup}, 
  year={2022},
  volume={30},
  pages={635-645},
  doi={10.1109/TASLP.2022.3145308}
}

Acknowledgments

We thank all authors from this two papers: 'Deep Open Intent Classification with Adaptive Decision Boundary' and 'MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification'. We adopt many codes from their projects.