Year: 2026
Type: conference-paper
Abstract: Live-stream E-commerce faces significant challenges from morphs, deliberate linguistic variants used to evade real-time voice filters and amplify product claims illegally.While critical for regulatory enforcement, Live Auditory Morph Resolution (LiveAMR) research is hindered by limited datasets: prior work relied on narrow, redundant health domain corpora, restricting model robustness.To bridge this gap, we introduce two datasets: (1) HealthAMR, a refined health-domain corpus via deduplication and re-annotation.(2) GeneralAMR, a general domain benchmark with 28K annotated sentences from 77 channels across 7 E-commerce categories.Further, we propose JointMRE, a multi-task framework that jointly resolves morphs and generates structured explanations, transferring grammatical insights from large language models to enhance generalization.Predictions are refined by our Conflict-aware Dual-output Refinement Framework (CDRF), which detects inconsistencies between corrections and explanations.Experiments show CDRF significantly improves morph resolution accuracy and interpretability.Our datasets and code are available 1 .
Authors: Jipeng Qiang, Jiahao Zhu, Yi Zhu, Chaowei Zhang
Institution: Yangzhou University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: G3595099704, G4713502116, G5643896866
OpenAlex ID: W7166884703