- Keywords: NLP, Deep Learning, Lifelong Learning
- TL;DR: Language modeling is all you need for lifelong language learning.
- Abstract: Most research on lifelong learning (LLL) applies to images or games, but not language. We present LAMAL, a simple yet effective method for LLL based on language modeling. LAMAL replays pseudo-samples of previous tasks while requiring no extra memory or model capacity. Specifically, LAMAL is a language model that simultaneously learns to solve the task 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 LAMAL prevents catastrophic forgetting without any sign of intransigence and can perform up to five very different language tasks sequentially with only one model. Overall, LAMAL 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/xxx.
- Code: https://drive.google.com/file/d/1arQD40NfkbD_cS2vW2LwWtESYeO13SQt/view