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.

The little Sesame Street muppet has taken the world of Natural Language Processing by storm and the storm is picking up speed. We have seen a number of NLP problems solved by neural network architectures built on top of contextual representations of BERT. To name a few BERT based models have pushed the state of the art for SQUAD 2.0 question answering, GLUE multi task learning, Google natural questions task and Biomedical domain specific tasks -- BioBERT. This opened the door for the amazing developers at Hugging Face who built the PyTorch port for BERT. With this library, geniuses i.e. developers and data scientists can use BERT models for text classification, question answering, fine tuning language model and more.