Supersized pre-trained language models have pushed the accuracy of various
natural language processing (NLP) tasks to a new state-of-the-art (SOTA).
Rather than pursuing the reachless SOTA accuracy, more and more researchers
start paying attention on model efficiency and usability. Different from
accuracy, the metric for efficiency varies across different studies, making
them hard to be fairly compared. To that end, this work presents ELUE
(Efficient Language Understanding Evaluation), a standard evaluation, and a
public leaderboard for efficient NLP models. ELUE is dedicated to depict the
Pareto Frontier for various language understanding tasks, such that it can tell
whether and how much a method achieves Pareto improvement. Along with the
benchmark, we also release a strong baseline, ElasticBERT, which allows BERT to
exit at any layer in both static and dynamic ways. We demonstrate the
ElasticBERT, despite its simplicity, outperforms or performs on par with SOTA
compressed and early exiting models. With ElasticBERT, the proposed ELUE has a
strong Pareto Frontier and makes a better evaluation for efficient NLP models.

该研究提出了一种标准评估和公开排行榜 ELUE（Efficient Language Understanding Evaluation）用于评估各种自然语言处理模型的效率，包括了统计语言模型 BERT 的一个新版本 ElasticBERT，并用该模型在不同任务上完成了 Pareto 前沿。