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Achieving gender equality is an important pillar for humankind's sustainablefuture. Pioneering data-driven gender bias research is based on large-scalepublic records such as scientific papers, patents, and company registrations,covering female researchers, inventors and entrepreneurs, and so on. Sincegender information is often missing in relevant datasets, studies rely on toolsto infer genders from names. However, available open-sourced Chinesegender-guessing tools are not yet suitable for scientific purposes, which maybe partially responsible for female Chinese being underrepresented inmainstream gender bias research and affect their universality. Specifically,these tools focus on character-level information while overlooking the factthat the combinations of Chinese characters in multi-character names, as wellas the components and pronunciations of characters, convey important messages.As a first effort, we design a Chinese Heterogeneous Graph Attention (CHGAT)model to capture the heterogeneity in component relationships and incorporatethe pronunciations of characters. Our model largely surpasses current tools andalso outperforms the state-of-the-art algorithm. Last but not least, the mostpopular Chinese name-gender dataset is single-character based with far lessfemale coverage from an unreliable source, naturally hindering relevantstudies. We open-source a more balanced multi-character dataset from anofficial source together with our code, hoping to help future researchpromoting gender equality.

For the Underrepresented in Gender Bias Research: Chinese Name Gender Prediction with Heterogeneous Graph Attention Network