SEACrowd/weathub

Tasks

• word-list

Citation text from the card

@inproceedings{
    mukherjee-etal-2023-global,
    title = "{G}lobal {V}oices, Local Biases: Socio-Cultural Prejudices across Languages",
    author = "Mukherjee, Anjishnu and Raj, Chahat and Zhu, Ziwei and Anastasopoulos, Antonios",
    editor = "Bouamor, Houda and Pino, Juan and Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.emnlp-main.981",
    doi = "10.18653/v1/2023.emnlp-main.981",
    pages = "15828--15845",
    abstract = "Human biases are ubiquitous but not uniform: disparities exist across linguistic, cultural, and societal borders. As large amounts of recent literature suggest, language models (LMs) trained on human data can reflect and often amplify the effects of these social biases. However, the vast majority of existing studies on bias are heavily skewed towards Western and European languages. In this work, we scale the Word Embedding Association Test (WEAT) to 24 languages, enabling broader studies and yielding interesting findings about LM bias. We additionally enhance this data with culturally relevant information for each language, capturing local contexts on a global scale. Further, to encompass more widely prevalent societal biases, we examine new bias dimensions across toxicity, ableism, and more. Moreover, we delve de

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