What is Your Favorite Gender, MLM? Gender Bias Evaluation in
  Multilingual Masked Language Models
Jeongrok Yu Seong Ug Kim Jacob Choi Jinho D. Choi
Bias is a disproportionate prejudice in favor of one side against another.
Due to the success of transformer-based Masked Language Models (MLMs) and their
impact on many NLP tasks, a systematic evaluation of bias in these models is
needed more than ever. While many studies have evaluated gender bias in English
MLMs, only a few works have been conducted for the task in other languages.
This paper proposes a multilingual approach to estimate gender bias in MLMs
from 5 languages: Chinese, English, German, Portuguese, and Spanish. Unlike
previous work, our approach does not depend on parallel corpora coupled with
English to detect gender bias in other languages using multilingual lexicons.
Moreover, a novel model-based method is presented to generate sentence pairs
for a more robust analysis of gender bias, compared to the traditional
lexicon-based method. For each language, both the lexicon-based and model-based
methods are applied to create two datasets respectively, which are used to
evaluate gender bias in an MLM specifically trained for that language using one
existing and 3 new scoring metrics. Our results show that the previous approach
is data-sensitive and not stable as it does not remove contextual dependencies
irrelevant to gender. In fact, the results often flip when different scoring
metrics are used on the same dataset, suggesting that gender bias should be
studied on a large dataset using multiple evaluation metrics for best practice.