This repo contains the code of our TASLP'2022 paper:
Learning to Classify Open Intent via Soft Labeling and Manifold Mixup
- Python 3.6
- PyTorch 1.8.0
- transformers 2.8.0
- pytorch_pretrained_bert 0.6.2
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).
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}
}
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. Thank a lot!