Towards Interpretable User Intent Analysis with Deficient Evidence Fusion for Pseudo-Modalities

Chaochen Wu, Guan Luo, Meiyun Zuo

Abstract: Intent analysis enhances retrieval systems' ability to understand users' motivations and interact with people intelligently. Beyond accurately obtaining intent classification results, explaining the intent classification process remains an essential yet challenging issue in multimedia retrieval. In this study, we propose an interpretable intent analysis method, the Deficient Evidence Network (DENet), to generate interpretations of semantic elements while accurately computing intent labels. Our approach masks parts of the input text to create pseudo-modalities with deficient evidence and integrates these evidence using Dempster-Shafer theory. By evaluating the deficient evidence of different masked semantic elements, our method extracts interpretable information from the original text. Currently, benchmarks for measuring the interpretability of such methods are limited. To address this, we designed a task and provided a benchmark dataset to assess the subjective interpretability of users' intent for the first time. The proposed method not only demonstrates outstanding interpretability in quantitative assessments but also enhances the classification performance of the pre-trained intent model. Our code and benchmark dataset are open-accessible in github.com/yuanxiaoheben/DENet.

External IDs:dblp:conf/mir/WuLZ25