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https://github.com/declare-lab/sat

Code for the EMNLP 2022 Findings short paper "SAT: Improving Semi-Supervised Text Classification with Simple Instance-Adaptive Self-Training"
https://github.com/declare-lab/sat

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Code for the EMNLP 2022 Findings short paper "SAT: Improving Semi-Supervised Text Classification with Simple Instance-Adaptive Self-Training"

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# SAT: Improving Semi-Supervised Text Classification with Simple Instance-Adaptive Self-Training
This repository contains the official implementation code of the EMNLP 2022 Findings short paper [SAT: Improving Semi-Supervised Text Classification with Simple Instance-Adaptive Self-Training](https://arxiv.org/pdf/2210.12653v1.pdf).

## Usage

1. Set up the environment
```
conda create -n sat python==3.7.5
conda activate sat
cd SAT/
pip3 install -r requirements.txt
```

2. Training
```
cd src/
bash run.sh
```
The parameters.txt shows a list of hyper-parameters.

## Citation
Please cite our paper if you find our work useful for your research:
```bibtex
@inproceedings{chen-etal-2022-sat,
title = "{SAT}: Improving Semi-Supervised Text Classification with Simple Instance-Adaptive Self-Training",
author = "Chen, Hui and Han, Wei and Poria, Soujanya",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.456",
pages = "6141--6146",
}

```

## Contact
Should you have any questions, feel free to contact [chchenhui1996@gmail.com](chchenhui1996@gmail.com).