论文标题
使用Elbert快速准确的FSA系统：高效且轻巧的BERT
Fast and Accurate FSA System Using ELBERT: An Efficient and Lightweight BERT
论文作者
论文摘要
随着深度学习和基于变压器的预训练模型（例如BERT）的发展，许多NLP任务的准确性已得到显着提高。但是，大量参数和计算也对其部署构成了挑战。例如，使用BERT可以改善财务情感分析（FSA）任务中的预测，但会放慢速度，在这种情况下，速度和准确性在利润方面同样重要。为了解决这些问题，我们首先提出了一个高效且轻巧的BERT（ELBERT），以及一种新颖的基于信心的窗口（CWB）早期出口机制。基于Elbert，开发了一种在GPU平台上加速文本处理的创新方法，从而解决了使早期退出机制在较大的输入批量大小中更有效地工作的困难问题。之后，构建了快速且高准确的FSA系统。实验结果表明，所提出的CWB早期出口机制的准确性明显高于相同计算成本下BERT上现有的早期出口方法。通过使用这种加速方法，我们的FSA系统可以以足够的精度提高处理速度将近40次，每秒超过1000个文本，几乎是FastBert的两倍，因此为现代交易系统提供了更强大的文本处理能力。
With the development of deep learning and Transformer-based pre-trained models like BERT, the accuracy of many NLP tasks has been dramatically improved. However, the large number of parameters and computations also pose challenges for their deployment. For instance, using BERT can improve the predictions in the financial sentiment analysis (FSA) task but slow it down, where speed and accuracy are equally important in terms of profits. To address these issues, we first propose an efficient and lightweight BERT (ELBERT) along with a novel confidence-window-based (CWB) early exit mechanism. Based on ELBERT, an innovative method to accelerate text processing on the GPU platform is developed, solving the difficult problem of making the early exit mechanism work more effectively with a large input batch size. Afterward, a fast and high-accuracy FSA system is built. Experimental results show that the proposed CWB early exit mechanism achieves significantly higher accuracy than existing early exit methods on BERT under the same computation cost. By using this acceleration method, our FSA system can boost the processing speed by nearly 40 times to over 1000 texts per second with sufficient accuracy, which is nearly twice as fast as FastBERT, thus providing a more powerful text processing capability for modern trading systems.
