Cross-Cultural Transfer Learning for Chinese Offensive Language Detection

Building upon the considerable advances in Large Language Models (LLMs), we are now equipped to address more sophisticated tasks demanding a nuanced understanding of cross-cultural contexts. A key example is recipe adaptation, which goes beyond simple translation to include a grasp of ingredients, culinary techniques, and dietary preferences specific to a given culture. We introduce a new task involving the translation and cultural adaptation of recipes between Chinese- and English-speaking cuisines. To support this investigation, we present CulturalRecipes, a unique dataset composed of automatically paired recipes written in Mandarin Chinese and English. This dataset is further enriched with a human-written and curated test set. In this intricate task of cross-cultural recipe adaptation, we evaluate the performance of various methods, including GPT-4 and other LLMs, traditional machine translation, and information retrieval techniques. Our comprehensive analysis includes both automatic and human evaluation metrics. While GPT-4 exhibits impressive abilities in adapting Chinese recipes into English, it still lags behind human expertise when translating English recipes into Chinese. This underscores the multifaceted nature of cultural adaptations. We anticipate that these insights will significantly contribute to future research on culturally aware language models and their practical application in culturally diverse contexts.

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RB_BG_MHA: A RoBERTa-Based Model with Bi-GRU and Multi-Head Attention for Chinese Offensive Language Detection in Social Media

Xu,

Liu

2023

Applied Sciences

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Offensive language in social media affects the social experience of individuals and groups and hurts social harmony and moral values. Therefore, in recent years, the problem of offensive language detection has attracted the attention of many researchers. However, the primary research currently focuses on detecting English offensive language, while few studies on the Chinese language exist. In this paper, we propose an innovative approach to detect Chinese offensive language. First, unlike previous approaches, we utilized both RoBERTa’s sentence-level and word-level embedding, combining the sentence embedding and word embedding of RoBERTa’s model, bidirectional GRU, and multi-head self-attention mechanism. This feature fusion allows the model to consider sentence-level and word-level semantic information at the same time so as to capture the semantic information of Chinese text more comprehensively. Second, by concatenating the output results of multi-head attention with RoBERTa’s sentence embedding, we achieved an efficient fusion of local and global information and improved the representation ability of the model. The experiments showed that the proposed model achieved 82.931% accuracy and 82.842% F1-score in Chinese offensive language detection tasks, delivering high performance and broad application potential.

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Cross-Cultural Transfer Learning for Chinese Offensive Language Detection

Cited by 3 publications

References 24 publications

Domain Adaptation for Chinese Offensive Language Detection

Domain Adaptation for Chinese Offensive Language Detection

Cultural Adaptation of Recipes

RB_BG_MHA: A RoBERTa-Based Model with Bi-GRU and Multi-Head Attention for Chinese Offensive Language Detection in Social Media

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