A Multi-domain Text Classification Method Based on Recurrent Convolution Multi-task Learning

In the text classification task, many texts in different domains are similarly expressed and have the characteristics of correlation, which can solve the problem of insufficient training data with labels. The text of different fields can be combined with the multi-task learning method, and the training accuracy and speed of the model can be improved. A Recurrent Convolution Multi-Task Learning (MTL-RC) model for text multi-classification is proposed, jointly modeling the text of multiple tasks, and taking advantage of multi-task learning, Recurrent Neural Network(RNN) and Convolutional Neural Network(CNN) models to obtain the correlation between multi-domain texts, long-term dependence of text. Local features of text are extracted. Rich experiments are carried out based on multi-domain text classification datasets, the Recurrent Convolution Multi-Task Learning(MTL-LC) proposed in this paper has an average accuracy of 90.1% for text classification in different fields, which is 6.5% higher than the single-task learning model STL-LC. Compared with mainstream multi-tasking learning models Full Shared Multi-Task Learning(FS-MTL), Adversarial Multi-Task Learninng(ASP-MTL), and Indirect Communciation for Multi-Task Learning(IC-MTL) have increased by 5.4%, 4%, and 2.8%, respectively.