@inproceedings{qiang-etal-2026-chinese,
    title = "{C}hinese Live-Streaming {E}-Commerce Morph Resolution: Datasets and Methods",
    author = "Qiang, Jipeng  and
      Zhu, Jiahao  and
      Zhu, Yi  and
      Zhang, Chaowei",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.126/",
    pages = "2632--2645",
    ISBN = "979-8-89176-395-1",
    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 [{\ensuremath{<}}https://anonymous.4open.science/r/Morph-Resolution-Datasets-and-Methods-611E{\ensuremath{>}}]."
}