Chinese Morph Resolution in E-commerce Live Streaming Scenarios
Jiahao Zhu, Jipeng Qiang, Ran Bai, Chenyu Liu, Xiaoye Ouyang
Abstract
E-commerce live streaming in China, particularly on platforms like Douyin, has become a major sales channel, but hosts often use morphs to evade scrutiny and engage in false advertising. This study introduces the Live Auditory Morph Resolution (LiveAMR) task to detect such violations. Unlike previous morph research focused on text-based evasion in social media and underground industries, LiveAMR targets pronunciation-based evasion in health and medical live streams. We constructed the first LiveAMR dataset with 86,790 samples and developed a method to transform the task into a text-to-text generation problem. By leveraging large language models (LLMs) to generate additional training data, we improved performance and demonstrated that morph resolution significantly enhances live streaming regulation.
BibTeX
@inproceedings{zhu-etal-2025-chinese,
title = "{C}hinese Morph Resolution in {E}-commerce Live Streaming Scenarios",
author = "Zhu, Jiahao and
Qiang, Jipeng and
Bai, Ran and
Liu, Chenyu and
Ouyang, Xiaoye",
editor = "Chen, Weizhu and
Yang, Yi and
Kachuee, Mohammad and
Fu, Xue-Yong",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-industry.32/",
pages = "380--389",
ISBN = "979-8-89176-194-0"
}