Adversarial Examples for Extreme Multilabel Text Classification
The code is adapted from the source codes of BERT-ATTACK [1], APLC_XLNet [2], and AttentionXML [3]
The code has been test by the following packages:
- python==3.6.13
- boto3==1.17.70
- ruamel.yaml==0.16.12
- numpy==1.19.2
- scipy==1.5.4
- matplotlib==3.2.2
- scikit-learn==0.24.2
- transformers==2.9.0
- torch==1.4.0
- nltk==3.4
- pandas==1.1.5
- requests==2.25.1
- tqdm==4.60.0
Downolad the data and the APLC_XLNet model trained on this data as follows:
bash download_data_model.shFor preprocessing the data and run positive-targeted attacks with 10 samples per bin, run the following:
bash pos_attack.shTo check the results of the attacks, run the following:
bash resutls.sh[1] Li et al., BERT-ATTACK: Adversarial Attack Against BERT Using BERT, EMNLP 2020
[2] Ye et al., Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Clusters for Extreme Multi-label Text Classification, ICML 2020
[3] You et al., AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification, NeurIPS 2019