G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks

Zhongwei Wan; Yichun Yin; Wei Zhang; Jiaxin Shi; Lifeng Shang; Guangyong Chen; Xin Jiang; Qun Liu

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arXiv · 2212.03613

G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks

Zhongwei Wan·Yichun Yin·Wei Zhang·Jiaxin Shi·Lifeng Shang·Guangyong Chen·Xin Jiang·Qun Liu

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Abstract

Recently, domain-specific PLMs have been proposed to boost the task performance of specific domains (e.g., biomedical and computer science) by continuing to pre-train general PLMs with domain-specific corpora. However, this Domain-Adaptive Pre-Training (DAPT; Gururangan et al. (2020)) tends to forget the previous general knowledge acquired by general PLMs, which leads to a catastrophic forgetting phenomenon and sub-optimal performance. To alleviate this problem, we propose a new framework of General Memory Augmented Pre-trained Language Model (G-MAP), which augments the domain-specific PLM by a memory representation built from the frozen general PLM without losing any general knowledge. Specifically, we propose a new memory-augmented layer, and based on it, different augmented strategies are explored to build the memory representation and then adaptively fuse it into the domain-specific PLM. We demonstrate the effectiveness of G-MAP on various domains (biomedical and computer science publications, news, and reviews) and different kinds (text classification, QA, NER) of tasks, and the extensive results show that the proposed G-MAP can achieve SOTA results on all tasks.

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Zhongwei Wan, Yichun Yin, Wei Zhang, Jiaxin Shi, Lifeng Shang, Guangyong Chen, Xin Jiang, Qun Liu. 2022-12-07. G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks. https://arxiv.org/abs/2212.03613

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