Forging Multiple Training Objectives for Pre-trained Language Models via Meta-Learning

• URL: http://arxiv.org/abs/2210.10293v1
• Date: Wed, 19 Oct 2022 04:38:26 GMT
• Title: Forging Multiple Training Objectives for Pre-trained Language Models via Meta-Learning
• Authors: Hongqiu Wu, Ruixue Ding, Hai Zhao, Boli Chen, Pengjun Xie, Fei Huang, Min Zhang
• Abstract summary: Multiple pre-training objectives fill the vacancy of the understanding capability of single-objective language modeling. We propose textitMOMETAS, a novel adaptive sampler based on meta-learning, which learns the latent sampling pattern on arbitrary pre-training objectives.
• Score: 97.28779163988833
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multiple pre-training objectives fill the vacancy of the understanding capability of single-objective language modeling, which serves the ultimate purpose of pre-trained language models (PrLMs), generalizing well on a mass of scenarios. However, learning multiple training objectives in a single model is challenging due to the unknown relative significance as well as the potential contrariety between them. Empirical studies have shown that the current objective sampling in an ad-hoc manual setting makes the learned language representation barely converge to the desired optimum. Thus, we propose \textit{MOMETAS}, a novel adaptive sampler based on meta-learning, which learns the latent sampling pattern on arbitrary pre-training objectives. Such a design is lightweight with negligible additional training overhead. To validate our approach, we adopt five objectives and conduct continual pre-training with BERT-base and BERT-large models, where MOMETAS demonstrates universal performance gain over other rule-based sampling strategies on 14 natural language processing tasks.

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