C hinese Morph Resolution in E -commerce Live Streaming Scenarios (2025)

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.

## related work there has been extensive research on morph resolution across different language backgrounds including english @xcite @xcite , and chinese @xcite @xcite , etc. in this paper, we only focus on morph resolution in chinese. because chinese is a pictographic language, methods for identifying morph words in other languages cannot be applied to chinese. existing research on chinese morphs primarily focuses on social media and underground industries. initially, it was considered a filtering problem, with researchers using statistical and rule-based matching methods to identify problematic text @xcite @xcite . subsequently, sha et al. @xcite proposed incorporating radicals into chinese characters to enhance their features and improve morphs resolution. you et al. @xcite further extracted actual contextual information and enhanced embedded representations by integrating transformed mentions or target candidates with their relevant context into an autoencoder. recently, addressing the characteristics of morph words in underground industries, wang et al. @xcite introduced a morph parsing algorithm based on machine translation models. however, existing research on morphs mainly focuses on social media and underground industries, with studies on morph resolution in the emerging context of live streaming still being relatively scarce. ## task definition in the research context of this paper, 'morph' refers to the process where live streamers avoid platform censorship by replacing sensitive or restrictive words during product promotion, while ensuring that the audience can easily understand the original meaning conveyed by the transformation. here, we formally define the auditory-based morph resolution task in live screaming scenarios as the liveamr task. by analyzing thousands of videos, the main types of transformations can be categorized into three major types (transformation, homophones, and synonyms), as shown in suppose one example is "咱 们 一 些<小 糖 人>都是一样可以放心去喝，也不用去找<白 褂褂>了。" (some diabetes patients can safely drink without needing to consult a doctor.) with two morphs "小糖人"(sugar doll)->"糖尿病患 者"(diabetic) and "白褂褂"(people with white)->"医生"( doctor). the correct output by liveamr method should be "咱们一些<糖尿病患者>都是 一样可以放心去喝，也不用去找<医生>了。". type characteristic examples transformation insert meaningless characters into words, or change the structure while keeping the sound similar to the original words. 某医某院:医院 (hospital) mǒu y ī mǒu yuàn:y ī yuàn 祛什么斑:祛斑 (spot removal) qū shén mē bān:qū bān 小问小题:问题 (problem) xiǎo wèn xiǎo tí:wèn tí homophone use symbols to replace chinese characters k糖:抗糖 (anti glycemic) k táng: kàng táng k老:抗老 (anti aging) k lǎo: kàng lǎo synonyms use words that are highly related or synonymous with the target word 白大褂:医生 (people in white:doctor) 心灵之窗:眼睛 (windows to the soul:eyes) ## methods liveamr method: existing morph resolution methods generally use non-autoregressive language model macbert, a corrective masked language model pre-training task was added to the bert model @xcite . in the liveamr task, since the length of the variant words does not equal the length of the original word, we will use a textto-text pre-trained model as a backbone, such as bart @xcite and mengzi-t5 @xcite . below are the steps involved in this process. the created dataset consists of source-target pairs (x and y ), where: x is the input text ( live stream transcript), y is the desired output text (the normal text without morph words). the goal of the model is to learn a mapping from x to y . the pre-trained model m is a transformer-based sequence-to-sequence architecture, which is typically structured as: (1) encoder: takes the input sequence x and encodes it into hidden states; (2) decoder: takes the encoder's hidden states and generates the target sequence y . during training, the model aims to minimize the loss, which is typically the cross-entropy loss for text generation tasks. the formula for cross-entropy loss is: during training, the model minimizes the loss function l with respect to the model parameters θ over multiple iterations (epochs): where e denotes the expectation over the training data, l(x, y ; θ) is the loss function dependent on the input x, the target y , and the model parameters θ. after fine-tuning, the model generates new outputs for unseen inputs. this is done by feeding the input x input through the model to obtain the predicted sequence y pred : where y pred is the generated sequence, which can be decoded back into text. data augmentation via llms: some studies suggest that llms can be used to generate training datasets @xcite . although manual annotation can yield morph data from the real world, it comes at a high cost and may contain some redundancy, limiting the scale and diversity of the dataset. therefore, we aim to leverage llms to generate more morph data to supplement manually annotated data and enhance the model's generalization ability. however, given the complexity of morph forms and the limitations of llms in understanding them, we did not directly ask the llms to generate sentences containing morphs. to this end, we propose a more reliable construction strategy that combines the annotated morphs lexicon with llm capabilities. the specific steps are as follows: through this approach, we constructed a manually created morph dataset containing 11,280 positive samples and 2,155 negative samples. additionally, each positive sample generated by the llm averages 2.87 morphs. this data effectively supplements the manually annotated data, increasing the scale and diversity of the model's training data. in baselines. the following models were selected as the baseline for comparison: ## conclusion this study introduces the task of morph resolution in live streaming scenarios, termed liveamr. a liveamr dataset was created through human-llm collaboration, comprising 7,836 positive and 91,119 negative samples. the study analyzed task characteristics and utilized a text-to-text model architecture for morph resolution. given the impracticality of manually constructing large-scale training corpora, an efficient data augmentation method based on llms was proposed, leveraging existing annotated data. experimental results show that this augmentation method enhances model performance compared to baselines. the findings also indicate that morph resolution can contribute positively to streaming regulation.