Open Access

ARTICLE

PAL-BERT: An Improved Question Answering Model

Wenfeng Zheng¹, Siyu Lu¹, Zhuohang Cai¹, Ruiyang Wang¹, Lei Wang², Lirong Yin^2,*

1 School of Automation, University of Electronic Science and Technology of China, Chengdu, 610054, China
2 Department of Geography and Anthropology, Louisiana State University, Baton Rouge, LA, 70803, USA

* Corresponding Author: Lirong Yin. Email:

Computer Modeling in Engineering & Sciences 2024, 139(3), 2729-2745. https://doi.org/10.32604/cmes.2023.046692

Received 11 October 2023; Accepted 14 December 2023; Issue published 11 March 2024

Abstract

In the field of natural language processing (NLP), there have been various pre-training language models in recent years, with question answering systems gaining significant attention. However, as algorithms, data, and computing power advance, the issue of increasingly larger models and a growing number of parameters has surfaced. Consequently, model training has become more costly and less efficient. To enhance the efficiency and accuracy of the training process while reducing the model volume, this paper proposes a first-order pruning model PAL-BERT based on the ALBERT model according to the characteristics of question-answering (QA) system and language model. Firstly, a first-order network pruning method based on the ALBERT model is designed, and the PAL-BERT model is formed. Then, the parameter optimization strategy of the PAL-BERT model is formulated, and the Mish function was used as an activation function instead of ReLU to improve the performance. Finally, after comparison experiments with traditional deep learning models TextCNN and BiLSTM, it is confirmed that PAL-BERT is a pruning model compression method that can significantly reduce training time and optimize training efficiency. Compared with traditional models, PAL-BERT significantly improves the NLP task’s performance.

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Keywords

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