Variational information bottleneck and multi-task learning for multi-domain text classification
Multi-domain text classification is challenged by domain and vocabulary differences,resulting in low accuracy and generalization.Traditional methods are ineffective in addressing this issue.This paper pro-poses a multi-domain text classification method based on a variational information bottleneck multi-task algo-rithm.The task is formulated as a hierarchical learning representation problem that extracts task-specific fea-tures from comprehensive features.Firstly,we introduce additive between comprehensive features and task-specific features,following the information bottleneck principle.Secondly,we construct a model loss function to limit the information flow through the variational boundary of the information bottleneck,decoupling the comprehensive features with additive noise into task-specific features.Finally,the classifier in the decoder uti-lizes the task-specific features to generate text classification results.The proposed model achieves an average classification accuracy of 92.17%on the FDU-MTL multi-domain text classification dataset,outperforming several compared models and demostrating better interpretability.
Information bottleneckMulti-task modelMulti-domainVariational boundaryInterpretability
国家科技期刊平台
NSTL
万方数据
