For the Misgendered Chinese in Gender Bias Research: Multi-Task Learning with Knowledge Distillation for Pinyin Name-Gender Prediction

	For the Misgendered Chinese in Gender Bias Research: Multi-Task Learning with Knowledge Distillation for Pinyin Name-Gender Prediction
	Du, Xiaocong; Zhang, Haipeng
	2024
会议录名称	PROCEEDINGS OF THE THIRTY-THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, IJCAI 2024
摘要	Achieving gender equality is a pivotal factor in realizing the UN's Global Goals for Sustainable Development. Gender bias studies work towards this and rely on name-based gender inference tools to assign individual gender labels when gender information is unavailable. However, these tools often inaccurately predict gender for Chinese Pinyin names, leading to potential bias in such studies. With the growing participation of Chinese in international activities, this situation is becoming more severe. Specifically, current tools focus on pronunciation (Pinyin) information, neglecting the fact that the latent connections between Pinyin and Chinese characters (Hanzi) behind convey critical information. As a first effort, we formulate the Pinyin name-gender guessing problem and design a Multi-Task Learning Network assisted by Knowledge Distillation that enables the Pinyin embeddings in the model to possess semantic features of Chinese characters and to learn gender information from Chinese character names. Our open-sourced method surpasses commercial name-gender guessing tools by 9.70% to 20.08% relatively, and also outperforms the state-of-the-art algorithms.
会议名称	33rd International Joint Conference on Artificial Intelligence (IJCAI)
出版地	ALBERT-LUDWIGS UNIV FREIBURG GEORGES-KOHLER-ALLEE, INST INFORMATIK, GEB 052, FREIBURG, D-79110, GERMANY
会议地点	null,Jeju,SOUTH KOREA
会议日期	AUG 03-09, 2024
URL	查看原文
收录类别	CPCI-S
语种	英语
WOS研究方向	Computer Science ; Mathematics
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Mathematics, Applied
WOS记录号	WOS:001347142807042
出版者	IJCAI-INT JOINT CONF ARTIF INTELL
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/387310
专题	信息科学与技术学院_硕士生信息科学与技术学院_PI研究组_张海鹏组
通讯作者	Zhang, Haipeng
作者单位	ShanghaiTech Univ, Shanghai, Peoples R China
第一作者单位	上海科技大学
通讯作者单位	上海科技大学
第一作者的第一单位	上海科技大学
推荐引用方式 GB/T 7714	Du, Xiaocong,Zhang, Haipeng. For the Misgendered Chinese in Gender Bias Research: Multi-Task Learning with Knowledge Distillation for Pinyin Name-Gender Prediction[C]. ALBERT-LUDWIGS UNIV FREIBURG GEORGES-KOHLER-ALLEE, INST INFORMATIK, GEB 052, FREIBURG, D-79110, GERMANY:IJCAI-INT JOINT CONF ARTIF INTELL,2024.