MBCS: A few-shot intent detection model for manual inspection records

Abstract

Addressing the bottleneck issue of low accuracy and poor generalization in cargo risk intent detection caused by annotation scarcity in manual inspection scenarios within the import-export trade supervision domain, this study proposes an intent detection model named MBCS (Multi-task Learning with BERT for Classification and Semantic Similarity Comparison), designed for few-shot scenarios. To tackle the challenges of scarce domain-specific data and inadequate text representation capabilities, the research introduces a multi-task learning framework that integrates text classification with semantic similarity comparison. By incorporating semantic contrastive learning as an auxiliary task, the model’s semantic representation capability is enhanced. Concurrently, an attention-weight-based synonym substitution strategy was introduced, replacing the highest-attention words in the sequence by integrating contextual information. Experiments conducted on real-world customs business datasets demonstrate that MBCS achieves significant accuracy improvements of over 4.19% (4.91%) in 5-shot (10-shot) scenarios, substantially outperforming baseline models. This method provides an optimized solution for intent detection tasks plagued by the annotation scarcity dilemma.