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GradTS: A Gradient-Based Automatic Auxiliary Task Selection Method Based on Transformer Networks

The source code for GradTS (appeared in EMNLP 2021), where most of the resources in this repository are based on MT-DNN.

The main system requirements:

  • python >= 3.6
  • pytorch >= 1.5
  • transformers
  • numpy

Preperations

1. Environments

We recommend the readers to use the same environment as us.

conda create -n gradts python==3.6.5
conda activate gradts
sh setup_env.sh

2. Datasets & Processing

Run the following script to download and process datasets for 8 glue tasks.

sh setup_data.sh

After that, run this script to tokenize all the datasets.

sh setup_tok.sh

Besides the glue tasks, you can use any other task (e.g., pos, ner). But make sure that you:

  • Add task definition under the experiments/, it should be a .yml file that is similar to glue_task_def.yml.
  • Process your datasets to .json files, which are similar to data/canonical_data/bert-base-cased/cola_train.json.

Quick Start

Various encoders are used in our experiments (e.g., bert, roberta). Here, we set bert-base-cased as an example.

1. Correlation

To fine-tune PLM and get the head importance matrices, run the following commands.

sh tune_task_level_bert-base-cased.sh 0 4 # GPU, training batch size 
sh tune_ins_level_bert-base-cased.sh 0 4 # GPU, training batch size

Then, use this script to get the task- and instance-level correlation.

python calculate_corr.py --bert_model_type bert-base-cased --task_list mnli,rte,qqp,qnli,mrpc,sst,cola,wnli

2. GradTS-trial

For task-level trial-based selection (i.e., GradTS-trial), utilize these two commands below.

python ex_generator_GradTS-trial.py -T mnli,rte,qqp,qnli,mrpc,sst,cola,wnli -B bert-base-cased
sh GradTS-trial_8_bert-base-cased.sh 4 0 7  # training batch size, GPU, epoch

Next, run the following script.

python read_trial_results.py -T mnli,rte,qqp,qnli,mrpc,sst,cola,wnli -B bert-base-cased

You can find the selection results and the best performance in checkpoints/8_bert-base-cased/GradTS-trial.

3. GradTS-fg

For instance-level selection (i.e., GradTS-fg), run these scripts.

python ex_generator_GradTS-fg.py -T mnli,rte,qqp,qnli,mrpc,sst,cola,wnli -B bert-base-cased --thres 0.62
sh GradTS-fg_8_bert-base-cased_0.62.sh 4 0 7  # training batch size, GPU, epoch

You can find the best performance in checkpoints/8_bert-base-cased/GradTS-fg.

Reference

If you use our code for any publications, please cite us:

@inproceedings{ma2021gradts,
  title={GradTS: A Gradient-Based Automatic Auxiliary Task Selection Method Based on Transformer Networks},
  author={Ma, Weicheng and Lou, Renze and Zhang, Kai and Wang, Lili and Vosoughi, Soroush},
  booktitle={Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing},
  pages={5621--5632},
  year={2021}
}

Feel free to contact us at [email protected] or [email protected] if there is any confusion.