Source code of "Towards Robust Low-Resource Fine-Tuning with Multi-View Compressed Representation" (ACL23)

This code is tested on:

• Python 3.6.12
• transformers 3.3.1
• pytorch 1.10.1

• DATA_SEED Seed used to sample data for low-resource scenario
• NUM_SAMPLES Number of training and dev data
• TASK Name of the task e.g. mnli
• NUM_DIM_SET Number of dimension sets
• DIM_SET MVCR dimesion set e.g. 128,256,512
• LAYER_SET Layers that MVCR adds to e.g. 2,12
• GPU CUDA GPU
• SEED Runnning seed
• RECON_LOSS Ratio used for reconstruction loss
• DROPOUT Dropout rate for all layers
• MIXOUT Mixout rate
• to run the model on the subsampled datasets, add --sample_train option.

We provide the following sample scripts.

1. To Train MVCR on sentence-level tasks:

bash aebert.sh bert-base-uncased DATA_SEED NUM_SAMPLES TASK NUM_DIM_SET DIM_SET LAYER_SET GPU SEED RECON_LOSS DROPOUT MIXOUT AE2TK

2. To Train MVCR on token-level tasks:

bash panx.sh

If you find this code useful, please cite our paper:

@misc{liu2022robust,
      title={Towards Robust Low-Resource Fine-Tuning with Multi-View Compressed Representations}, 
      author={Linlin Liu and Xingxuan Li and Megh Thakkar and Xin Li and Lidong Bing and Shafiq Joty and Luo Si},
      year={2022},
      eprint={2211.08794},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}