Pretrained language models (PLMs) are today the primary model for natural language processing. Despite their impressive downstream performance, it can be difficult to apply PLMs to new languages, a barrier to making their capabilities universally accessible. While prior work has shown it possible to address this issue by learning a new embedding layer for the new language, doing so is both data and compute inefficient. We propose to use an active forgetting mechanism during pretraining, as a simple way of creating PLMs that can quickly adapt to new languages. Concretely, by resetting the embedding layer every K updates during pretraining, we encourage the PLM to improve its ability of learning new embeddings within a limited number of updates, similar to a meta-learning effect. Experiments with RoBERTa show that models pretrained with our forgetting mechanism not only demonstrate faster convergence during language adaptation but also outperform standard ones in a low-data regime, particularly for languages that are distant from English.

本文提出使用主动遗忘机制作为预训练过程中的一种简单方法，以创建能够快速适应新语言的PLMs。实验证明，与标准模型相比，在资源匮乏的情况下，使用遗忘机制的预先训练模型不仅在语言适应过程中表现出更快的收敛速度，而且在特别是对于与英语不同的语言来说表现更佳。

通过主动遗忘预训练以提高语言可塑性