基于提示学习与自适应损失加权的汉越产业文本分类

doi:10.11896/jsjkx.250300038

Abstract

Abstract: Cross-border industrial text classification is a fundamental task that supports big data analysis in cross-border industries.With the rapid growth of cross-border industrial data in Southeast Asia,there is an increasing demand for the analysis and processing of industrial data,particularly with respect to industrial text classification.However,cross-border industrial text classification faces several challenges,including linguistic differences across languages,data imbalance among languages,and the scarcity of annotated data.These issues are particularly pronounced in low-resource languages,making cross-border industrial data classification more difficult.To address this issue,this paper proposes a few-shot cross-border industrial text classification method based on prompt learning,combined with an adaptive loss weighting strategy,which significantly enhances the model's classification performance in cross-border scenarios.Specifically,the proposed model mitigates the issue of data scarcity within the prompt-learning framework by leveraging the prior knowledge of pre-trained models to enhance few-shot learning capabilities.Furthermore,cross-lingual text pairs are constructed to facilitate knowledge transfer and semantic alignment in semantic space.Addi-tionally,an innovative dynamic hybrid loss function is designed,integrating cross-entropy loss,focal loss,and label smoothing loss in a multi-objective optimization framework.The loss terms are dynamically weighted based on an uncertainty-based weighting mechanism:cross-entropy loss ensures fundamental classification capability,focal loss enhances the focus on hard-to-classify samples,and label smoothing effectively mitigates the risk of overfitting.Experimental results demonstrate that the proposed method significantly outperforms existing mainstream approaches in cross-border Chinese and Vietnamese industrial text classification tasks,particularly in few-shot learning scenarios with data scarcity and language imbalance.This approach provides an efficient solution and offers new research perspectives for processing low-resource languages.

Key words: Cross-border industrial text classification, Few-shot learning, Prompt learning, Adaptive loss weighting

CLC Number:

TP391

CHEN Lin, MA Longxuan, ZHANG Yongbing, HUANG Yuxin, GAO Shengxiang, YU Zhengtao. Industrial Text Classification for Chinese and Vietnamese Based on Prompt Learning and AdaptiveLoss Weighting[J].Computer Science, 2026, 53(2): 312-321.

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Industrial Text Classification for Chinese and Vietnamese Based on Prompt Learning and AdaptiveLoss Weighting

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