  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.
