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Class-Incremental Learning based on Label Generation

This repository contains the code for our ACL'23 paper Class-Incremental Learning based on Label Generation by Yijia Shao, Yiduo Guo, Dongyan Zhao, and Bing Liu.

Overview

In this work, we study the class-incremental learning (CIL) by taking PLMs' properties into account. We find using the generation objective can better preserve PLMs' representation ability in the continual learning processing and yielding much better CIL results.

We further propose the VAG (Vocabulary-Aware Label Generation) system for CIL which reframes CIL as a continual label generation problem.

Requirements

We developed the code based on python 3.9.0. The required packages can be found in requirements.txt. You can run the following commands to set up the environment:

1. Create a new conda environment:

   conda create -n vag python==3.9
   conda activate vag
   

2. Install Pytorch based on your device version.
3. Install the required packages:

   pip install -r requirements.txt
   

Datasets

We use CLINC150, Banking77, 20News, FewRel, TACRED for our experiments. The processed data can be found in ./data . (If you want to use data in other directory, please modify DATA_ROOT in data.py accordingly.)

data.py provide an interface for preparing datasets for class-incremental learning.

Experiments

In this work, we found that using PLMs in a generation framework is better than using them in the traditional classification framework for CIL. finetune_classifier.py provides the entry point for the classifier framework and finetune_gen.py provides the entry point for the generation framework.

We include example scripts in ./script for a quick start. To train VAG system on CLINC150 (for other datasets, use corresponding scripts in ./script), run

CUDA_VISIBLE_DEVICES=$gpu_id SEED=2021 BASELINE=bart_retrieve_bart-base_restrict_vocabulary_label_replay STORE_RATIO=0 LAMB=1 bash script/run_clinc_gen.sh

We use BASELINE to indicate different methods. You can set it to different values to experiment on the baseline methods:

• BASELINE=bart_retrieve_bart-base_vanilla: Vanilla-G
• BASELINE=bart_retrieve_bart-base_ewc: EWC-G (we set LAMB to 5000 for EWC-G)
• BASELINE=bart_retrieve_bart-base_distill: KD-G (we set LAMB to 0.1 for KD-G)
• BASELINE=bart_retrieve_bart-base_l2p: L2P-G

For baselines using the classifier framework, run

CUDA_VISIBLE_DEVICES=$gpu_id SEED=2021 BASELINE=$BASELINE STORE_RATIO=0 LAMB=$LAMB bash script/run_clinc_classifier.sh

We also use BASELINE to indicate different methods:

• BASELINE=bart_classification_bart-base_vanilla: Vanilla
• BASELINE=bart_classification_bart-base_ewc: EWC (we set LAMB to 5000 for EWC)
• BASELINE=bart_classification_bart-base_distill: KD (we set LAMB to 0.1 for KD)
• BASELINE=bart_classification_bart-base_l2p: L2P

Extend VAG system to exemplar-based setting

Notably, VAG system can be extended to exemplar-based setting when real replay data are available (see Section 4.2). Run the following command to reproduce the experiment:

CUDA_VISIBLE_DEVICES=$gpu_id SEED=2021 BASELINE=bart_retrieve_bart-base_restrict_vocabulary_label_replay_experience_replay STORE_RATIO=$STORE_RATIO LAMB=1 bash script/run_clinc_gen.sh

We set STORE_RATIO to 0.01, 0.03, 0.05 in our experiments to try out different buffer sizes.

For replay-based baselines, run

CUDA_VISIBLE_DEVICES=$gpu_id SEED=2021 BASELINE=$BASELINE STORE_RATIO=0 LAMB=0 bash script/run_clinc_classifier.sh

• BASELINE=bart_classification_bart-base_experience_replay: ER
• BASELINE=bart_classification_bart-base_derpp: DER++
• BASELINE=bart_classification_bart-base_ldbr: LDBR

Run VAG system on your own dataset

You can easily run VAG system on your own dataset with the following steps:

1. Add the data processing code in get_dataset() in data.py.
2. Add a file under ./sequences to indicate the task sequence. The sequence file name should be the same with your dataset name. Register the sequence information in prepare_sequence_finetune() in utils.py.
3. Complete the following script and run!

   for ft_task in  $(seq 0 $TASK_CNT-1);
   do
     CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES python finetune_gen.py \
     --max_seq_length 128 \
     --ft_task ${ft_task} \
     --seed $SEED \
     --sequence_file 'sequences/20news' \
     --baseline bart_retrieve_bart-base_restrict_vocabulary_label_replay \
     --epoch 10 \
     --batch_size 8 --store_ratio 0 --learning_rate 1e-5 --lamb 1 --use_dev
   done
   

Bugs or questions?

If you have any questions related to the code or the paper, feel free to email Yijia. If you encounter any problems when using the code, or want to report a bug, you can open an issue. Please try to specify the problem with details so we can help you better and quicker!

Acknowledgements

We thank PyContinual for providing an extendable framework for continual learning. We use their code structure as a reference when developing this code base.

Citation

Please cite our paper if you use this code or parts of it:

@inproceedings{shao-etal-2023-class,
    title = "Class-Incremental Learning based on Label Generation",
    author = "Shao, Yijia  and
      Guo, Yiduo  and
      Zhao, Dongyan  and
      Liu, Bing",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-short.109",
    doi = "10.18653/v1/2023.acl-short.109",
    pages = "1263--1276",
}