  Achieving gender equality is an important pillar for humankind's sustainable
future. Pioneering data-driven gender bias research is based on large-scale
public records such as scientific papers, patents, and company registrations,
covering female researchers, inventors and entrepreneurs, and so on. Since
gender information is often missing in relevant datasets, studies rely on tools
to infer genders from names. However, available open-sourced Chinese
gender-guessing tools are not yet suitable for scientific purposes, which may
be partially responsible for female Chinese being underrepresented in
mainstream gender bias research and affect their universality. Specifically,
these tools focus on character-level information while overlooking the fact
that the combinations of Chinese characters in multi-character names, as well
as 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 incorporate
the pronunciations of characters. Our model largely surpasses current tools and
also outperforms the state-of-the-art algorithm. Last but not least, the most
popular Chinese name-gender dataset is single-character based with far less
female coverage from an unreliable source, naturally hindering relevant
studies. We open-source a more balanced multi-character dataset from an
official source together with our code, hoping to help future research
promoting gender equality.
