LAMOL: LAnguage MOdeling for Lifelong Language Learning
Abstract
Most research on lifelong learning applies to images or games, but not language. We present LAMOL, a simple yet effective method for lifelong language learning (LLL) based on language modeling. LAMOL replays pseudo-samples of previous tasks while requiring no extra memory or model capacity. Specifically, LAMOL is a language model that simultaneously learns to solve the tasks and generate training samples. When the model is trained for a new task, it generates pseudo-samples of previous tasks for training alongside data for the new task. The results show that LAMOL prevents catastrophic forgetting without any sign of intransigence and can perform five very different language tasks sequentially with only one model. Overall, LAMOL outperforms previous methods by a considerable margin and is only 2-3% worse than multitasking, which is usually considered the LLL upper bound. The source code is available at https://github.com/jojotenya/LAMOL.
Cite
Text
Sun et al. "LAMOL: LAnguage MOdeling for Lifelong Language Learning." International Conference on Learning Representations, 2020.Markdown
[Sun et al. "LAMOL: LAnguage MOdeling for Lifelong Language Learning." International Conference on Learning Representations, 2020.](https://mlanthology.org/iclr/2020/sun2020iclr-lamol/)BibTeX
@inproceedings{sun2020iclr-lamol,
title = {{LAMOL: LAnguage MOdeling for Lifelong Language Learning}},
author = {Sun, Fan-Keng and Ho, Cheng-Hao and Lee, Hung-Yi},
booktitle = {International Conference on Learning Representations},
year = {2020},
url = {https://mlanthology.org/iclr/2020/sun2020iclr-lamol/}
}