Chinese Morph Resolution in E-commerce Live Streaming Scenarios
This addresses the issue of false advertising and regulation in e-commerce live streaming for platforms and consumers, but it is incremental as it adapts existing morph detection to a new scenario.
The study tackled the problem of detecting pronunciation-based morphs used by hosts to evade scrutiny in Chinese e-commerce live streaming, particularly in health and medical contexts, by introducing the LiveAMR task and constructing a dataset of 86,790 samples, resulting in improved performance through a text-to-text generation method with LLM-generated training data.
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.