A Multitask Learning Aspect-Based Model for Product Defect Detection
提出一个多任务学习框架，结合方面术语提取和方面级缺陷检测，减少专家依赖并提升缺陷检测的细粒度，在汽车数据集上F1分数提升5%至0.88。
Product defect detection (PDD) is a critical industrial big data application that leverages social media text mining to identify product defects. However, existing PDD approaches face significant challenges: heavy reliance on expert-driven aspect term extraction (ATE) limits scalability and increases costs, and they fail to capture detailed defect information like affected aspects, defect causes, and severity. To address these limitations, this study proposes a novel multi-task learning framework that integrates ATE and aspect-level defect detection. By enhancing ATE with semantic augmentation, this model reduces expert dependence and scales effectively. Experiments on automotive datasets show the proposed method improves the F1 score by 5% (to 0.88) over benchmark models. Furthermore, ablation studies reveal a 36.5% F1 score improvement for the ATE component. The proposed method outperforms benchmark PDD models and provides more fine-grained defect information, enabling a more comprehensive understanding and supporting precise decision-making in quality management.