In order to improve the accuracy performance of Chinese text classification models with low hardware requirements, an improved concatenation-based model is designed in this paper, which is a concatenation of 5 different sub-models, including TextCNN, LSTM, and Bi-LSTM. Compared with the existing ensemble learning method, for a text classification mission, this model's accuracy is 2% higher. Meanwhile, the hardware requirements of this model are much lower than the BERT-based model.
@article{arxiv.2010.14784,
title = {A Chinese Text Classification Method With Low Hardware Requirement Based on Improved Model Concatenation},
author = {Qingli Man and Yuanhao Zhuo},
journal= {arXiv preprint arXiv:2010.14784},
year = {2021}
}