Cross-Cultural Transfer Learning for C hinese Offensive Language Detection (2023)

Abstract: Detecting offensive language is a challenging task. Generalizing across different cultures and languages becomes even more challenging: besides lexical, syntactic and semantic differences, pragmatic aspects such as cultural norms and sensitivities, which are particularly relevant in this context, vary greatly. In this paper, we target Chinese offensive language detection and aim to investigate the impact of transfer learning using offensive language detection data from different cultural backgrounds, specifically Korean and English. We find that culture-specific biases in what is considered offensive negatively impact the transferability of language models (LMs) and that LMs trained on diverse cultural data are sensitive to different features in Chinese offensive language detection. In a few-shot learning scenario, however, our study shows promising prospects for non-English offensive language detection with limited resources. Our findings highlight the importance of cross-cultural transfer learning in improving offensive language detection and promoting inclusive digital spaces.

## introduction the proliferation of offensive language and hate speech in online platforms, especially on social media, has significantly increased in recent years @xcite @xcite . there is a fine line between offensive language and hate speech as few universal definitions exist @xcite . therefore, hate speech can be classified as a subtype of offensive language. in this paper, we do not differentiate them in detail, and instead, refer to the task of offensive language detection (old). despite numerous breakthroughs in the development of nlp methods for old @xcite , some significant obstacles remain unsolved @xcite , including the shortage of data resources for research purposes and bias in human annotation. since most of the available approaches and resources for old are designed for english @xcite , the resulting trained models operate within a monocultural background that caters to english speakers. 1 however, @xcite believe that old has strong cultural implications, unlike other nlp tasks, because an utterance's offensiveness can vary based on an individual's cultural background. people with different backgrounds react to inputs differently and communicate differently, so their tolerance for the presence of offensive terms, e.g., slur, may differ, as well as what is altogether considered offensive @xcite . cultural differences have been explored in humor perception @xcite , swearing reception @xcite , translation in semantic inconsistencies @xcite and honorifics expression @xcite . even in less obvious cases, however, they bear meaningful significance on how to pose and solve nlp tasks, as cultures differ with respect to style, values, common ground and topics of interest @xcite . therefore, we argue that there is a need for addressing cross-cultural aspects in offensive language detection. although culture is intricate and challenging to define clearly, language still remains as one of the most straightforward manifestation of culture. while recent work @xcite has demonstrated the effectiveness of cross-lingual transfer learning 1 importantly, "culture" is multifaceted and complex. when referring to english speakers, we assume that there are general unique features that characterize them, but of course there is enormous diversity within speakers of the same language. as a first step towards the analysis of cross-cultural old, we restrict ourselves to the level of language categories. in the text classification and offensive language (hate speech) detection, they don't consider the impact of cultural background differences (e.g., eastern and western culture). in this paper, we take a step forward in this direction and explore the influence of offensive content from diverse cultural background on old, focusing on evaluation in chinese. ## related work offensive language detection. although most of the research on old has focused on english (fortuna and nunes, 2018), there exist datasets in multiple languages: chinese @xcite , korean @xcite , danish (sigurbergsson and derczynski, 2020), bengali @xcite , to name a few. however, language models commonly rely on prior distributions from training data, that reflects a discourse that is temporally and culturally situated @xcite . in a comprehensive analysis of geographically-related content and its influence on performance disparities of offensive language detection models, @xcite find that current models do not generalize across locations. @xcite call for contextualizing offensive (toxicity) labels in social variables as determining what is toxic is subjective, and annotator beliefs can be reflected in the data collected. cross-lingual transfer learning. cross-lingual transfer appears as a potential solution to the issue of language-specific resource scarcity @xcite . @xcite demonstrates the limits of cross-lingual zero-shot transfer for hate speech detection in english, italian and spanish. the benefits of few-shot learning is evident in works from @xcite and @xcite , who confirmed the effectiveness of fewshot learning for the task of hate speech detection in under-resourced languages. @xcite harness cross-cultural differences for english formality and sarcasm detection based on german and japanese, respectively. litvak et al. (2022) show that, in the context of old, knowledge transfer is not bidirectional and efficient transfer learning holds from arabic to hebrew in terms of recall. ## experiments implementation. in our experiments, we only evaluate on cold and try different training settings with cold, kold and haten. in particular, because the data volume of haten is relatively small, we use all of its data as the training set. the actual training set of three datasets has offensive data to non-offensive data ratios of 0.98, 1.01, and 1.02 (refer to ## conclusion our study highlights the challenges of detecting offensive language across different cultures and languages. we show that transfer learning using data chinese korean labels 黑人反对歧视黑人有啥错？ ᄒ ᅳ ᆨᄋ ᅵ ᆫ ᄃ ᅢᄐ ᅩ ᆼᄅ ᅧ ᆼᄃ ᅩ ᄂ ᅡᄋ ᅩ ᆫ ᄆ ᅵᄀ ᅮ ᆨ, 0 / 1 what is wrong with blacks ᄋ ᅵᄌ ᅦ ᄋ ᅵ ᆫᄌ ᅩ ᆼᄎ ᅡᄇ ᅧ ᆯᄋ ᅳ ᆫ ᄉ ᅡᄅ ᅡᄌ ᅧ ᆻᄃ ᅡ? against discrimination america with a black president, against blacks? now racism has disappeared? 中国哪有那么容易搞到 ᄌ ᅮ ᆼᄀ ᅮ ᆨᄋ ᅦᄉ ᅥ ᄇ ᅥ ᆷᄋ ᅳ ᆫ ᄌ ᅡ ᆸᄒ ᅵᄆ ᅧ ᆫ ᄆ ᅯᄀ ᅡ 0 / 1 毒品? ᄌ ᅡ ᆯᄅ ᅵ ᆯᄁ ᅡ.. how can it be so easy what will happen if a to get drugs in china. from diverse cultural backgrounds have different negative effects on the transferability of language models due to culture-specific biases. however, our findings also indicate promising prospects for improving offensive language detection in promoting inclusive digital spaces, particularly in a few-shot learning scenario. we call for more research on cross-cultural offensive language detection, which is important to deploy effective moderation strategies for social media platforms, improving crosscultural communication, and reducing harmful online behavior.