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

TL;DR

This paper introduces a multi-task learning approach with knowledge distillation to improve gender prediction accuracy for Chinese Pinyin names, addressing biases in existing tools and enhancing fairness in gender bias research.

Contribution

It presents the first formulation of Pinyin name-gender prediction using multi-task learning with knowledge distillation, incorporating semantic features from Chinese characters.

Findings

Outperforms commercial tools by up to 20.08%

Surpasses state-of-the-art algorithms in accuracy

Provides an open-source method for gender prediction

Abstract

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.