  As natural language processing (NLP) for gender bias becomes a significant
interdisciplinary topic, the prevalent data-driven techniques such as
large-scale language models suffer from data inadequacy and biased corpus,
especially for languages with insufficient resources such as Chinese. To this
end, we propose a Chinese cOrpus foR Gender bIas Probing and Mitigation
CORGI-PM, which contains 32.9k sentences with high-quality labels derived by
following an annotation scheme specifically developed for gender bias in the
Chinese context. Moreover, we address three challenges for automatic textual
gender bias mitigation, which requires the models to detect, classify, and
mitigate textual gender bias. We also conduct experiments with state-of-the-art
language models to provide baselines. To our best knowledge, CORGI-PM is the
first sentence-level Chinese corpus for gender bias probing and mitigation.
