RNT

A model for learning under semi-supervised settings

This is the source code for the paper: Murtadha, Ahmed, et al. "Rank-Aware Negative Training for Semi-Supervised Text Classification".

Data

The datasets used in our experminents can be downloaded from this link.

Prerequisites:

Required packages are listed in the requirements.txt file:

pip install -r requirements.txt

Training

• Go to code/
• Run the following code to train RNT:

python run.py --dataset='SST-5' --train-sample=30

• The params could be :
  • --dataset ={AG,yelp, yahoo, TREC,SST, SST-5, CR, MR}
  • --train_sample ={0, 30,1000, 10000}, where 0 denotes 10% of the labeled data

The results will be written to results/main_nt.txt

Evaluation

• Go to code/
• Run the following code to evaluate RNT:

python evaluate.py --dataset='SST-5' --train-sample=30

If you use the code, please cite the paper:

@article{RNT-TACL-2023,
 author       = {Ahmed Murtadha and
                 Shengfeng Pan and
                 Wen Bo and
                 Jianlin Su and
                 Xinxin Cao and
                 Wenze Zhang and
                 Yunfeng Liu},
 title        = {Rank-Aware Negative Training for Semi-Supervised Text Classification},
 journal      = {Transactions of the Association for Computational Linguistics (TACL, 2023)},
 volume       = {abs/2306.07621},
 year         = {2023},
 url          = {https://doi.org/10.48550/arXiv.2306.07621},
 doi          = {10.48550/arXiv.2306.07621}
}