MC-BERT: Efficient Language Pre-Training via a Meta Controller
This work addresses efficiency in NLP pre-training for researchers and practitioners, offering an incremental improvement over existing methods like ELECTRA.
The paper tackles the computational expense of large-scale language pre-training by proposing MC-BERT, a meta-learning framework that uses a multi-choice cloze test with a reject option, which outperforms baselines on GLUE semantic tasks under the same computational budget.
Pre-trained contextual representations (e.g., BERT) have become the foundation to achieve state-of-the-art results on many NLP tasks. However, large-scale pre-training is computationally expensive. ELECTRA, an early attempt to accelerate pre-training, trains a discriminative model that predicts whether each input token was replaced by a generator. Our studies reveal that ELECTRA's success is mainly due to its reduced complexity of the pre-training task: the binary classification (replaced token detection) is more efficient to learn than the generation task (masked language modeling). However, such a simplified task is less semantically informative. To achieve better efficiency and effectiveness, we propose a novel meta-learning framework, MC-BERT. The pre-training task is a multi-choice cloze test with a reject option, where a meta controller network provides training input and candidates. Results over GLUE natural language understanding benchmark demonstrate that our proposed method is both efficient and effective: it outperforms baselines on GLUE semantic tasks given the same computational budget.