Fast and Accurate FSA System Using ELBERT: An Efficient and Lightweight BERT

As an application of Natural Language Processing (NLP) techniques, financial sentiment analysis (FSA) has become an invaluable tool for investors. Its speed and accuracy can significantly impact the returns of trading strategies.With the development of deep learning and Transformer-based pre-trained models like BERT, the accuracy of FSA has been much improved, but these time-consuming big models will also slow down the computation. To boost the processing speed of the FSA system and ensure high precision, 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. Besides, our FSA system can boost the processing speed to over 1000 texts per second with sufficient accuracy by using this acceleration method, which is nearly twice as fast as the FastBERT. Hence, this system can enable modern trading systems to quickly and accurately process financial text data.