SEACrowd/weathub
收藏WEATHub 数据集概述
基本信息
- 名称: WEATHub
- 语言:
- 泰语 (tha)
- 他加禄语 (tgl)
- 越南语 (vie)
- 中文 (cmn)
- 英语 (eng)
- 任务类别: 词表 (word-list)
- 标签: 词表 (word-list)
- 许可证: Creative Commons Attribution 4.0 (cc-by-4.0)
数据集描述
- 内容: 包含24种语言的词汇，组织成(target1, target2, attribute1, attribute2)的组，用于测量target1:target2 :: attribute1:attribute2的关联。例如，target1可以是昆虫，target2可以是花，测量这些词汇是否令人愉悦或不愉悦。
- 测量方法: 使用WEAT指标量化词关联，计算效应量（Cohens d）和p值（统计显著性）。
- 应用: 通过语言模型的词嵌入进行测试，理解不同语言中的偏见关联。
支持的任务
- 词表
数据集版本
- 源版本: 1.0.0
- SEACrowd版本: 2024.06.20
引用
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主要引用:
@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 deeper into the Indian linguistic landscape, conducting a comprehensive regional bias analysis across six prevalent Indian languages. Finally, we highlight the significance of these social biases and the new dimensions through an extensive comparison of embedding methods, reinforcing the need to address them in pursuit of more equitable language models." }
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SEACrowd引用:
@article{lovenia2024seacrowd, title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages}, author={Holy Lovenia and Rahmad Mahendra and Salsabil Maulana Akbar and Lester James V. Miranda and Jennifer Santoso and Elyanah Aco and Akhdan Fadhilah and Jonibek Mansurov and Joseph Marvin Imperial and Onno P. Kampman and Joel Ruben Antony Moniz and Muhammad Ravi Shulthan Habibi and Frederikus Hudi and Railey Montalan and Ryan Ignatius and Joanito Agili Lopo and William Nixon and Börje F. Karlsson and James Jaya and Ryandito Diandaru and Yuze Gao and Patrick Amadeus and Bin Wang and Jan Christian Blaise Cruz and Chenxi Whitehouse and Ivan Halim Parmonangan and Maria Khelli and Wenyu Zhang and Lucky Susanto and Reynard Adha Ryanda and Sonny Lazuardi Hermawan and Dan John Velasco and Muhammad Dehan Al Kautsar and Willy Fitra Hendria and Yasmin Moslem and Noah Flynn and Muhammad Farid Adilazuarda and Haochen Li and Johanes Lee and R. Damanhuri and Shuo Sun and Muhammad Reza Qorib and Amirbek Djanibekov and Wei Qi Leong and Quyet V. Do and Niklas Muennighoff and Tanrada Pansuwan and Ilham Firdausi Putra and Yan Xu and Ngee Chia Tai and Ayu Purwarianti and Sebastian Ruder and William Tjhi and Peerat Limkonchotiwat and Alham Fikri Aji and Sedrick Keh and Genta Indra Winata and Ruochen Zhang and Fajri Koto and Zheng-Xin Yong and Samuel Cahyawijaya}, year={2024}, eprint={2406.10118}, journal={arXiv preprint arXiv: 2406.10118} }
