BERxiT: Early Exiting for BERT with Better Fine-Tuning and Extension to Regression
Ji Xin, Raphael Tang, Yaoliang Yu, Jimmy Lin
Abstract
The slow speed of BERT has motivated much research on accelerating its inference, and the early exiting idea has been proposed to make trade-offs between model quality and efficiency. This paper aims to address two weaknesses of previous work: (1) existing fine-tuning strategies for early exiting models fail to take full advantage of BERT; (2) methods to make exiting decisions are limited to classification tasks. We propose a more advanced fine-tuning strategy and a learning-toexit module that extends early exiting to tasks other than classification. Experiments demonstrate improved early exiting for BERT, with better trade-offs obtained by the proposed finetuning strategy, successful application to regression tasks, and the possibility to combine it with other acceleration methods.