Rehearsal-free Continual Language Learning via Efficient Parameter Isolation
Zhicheng Wang, Yufang Liu, Tao Ji, Xiaoling Wang, Yuanbin Wu, Congcong Jiang, Ye Chao, Zhencong Han
Abstract
We study the problem of defying catastrophic forgetting when learning a series of language processing tasks. Compared with previous methods, we emphasize the importance of not caching history tasks’ data, which makes the problem more challenging. Our proposed method applies the parameter isolation strategy. For each task, it allocates a small portion of private parameters and learns them with a shared pre-trained model. To load correct parameters at testing time, we introduce a simple yet effective non-parametric method. Experiments on continual language learning benchmarks show that our method is significantly better than all existing no-data-cache methods, and is comparable (or even better) than those using historical data.
BibTeX
@inproceedings{wang-etal-2023-rehearsal,
title = "Rehearsal-free Continual Language Learning via Efficient Parameter Isolation",
author = "Wang, Zhicheng and
Liu, Yufang and
Ji, Tao and
Wang, Xiaoling and
Wu, Yuanbin and
Jiang, Congcong and
Chao, Ye and
Han, Zhencong and
Wang, Ling and
Shao, Xu and
Zeng, Wenqiu",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.612/",
doi = "10.18653/v1/2023.acl-long.612",
pages = "10933--10946"
}