Recently, more and more research has focused on addressing bias in text
classification models. However, existing research mainly focuses on the
fairness of monolingual text classification models, and research on fairness
for multilingual text classification is still very limited. In this paper, we
focus on the task of multilingual text classification and propose a debiasing
framework for multilingual text classification based on contrastive learning.
Our proposed method does not rely on any external language resources and can be
extended to any other languages. The model contains four modules: multilingual
text representation module, language fusion module, text debiasing module, and
text classification module. The multilingual text representation module uses a
multilingual pre-trained language model to represent the text, the language
fusion module makes the semantic spaces of different languages tend to be
consistent through contrastive learning, and the text debiasing module uses
contrastive learning to make the model unable to identify sensitive attributes'
information. The text classification module completes the basic tasks of
multilingual text classification. In addition, the existing research on the
fairness of multilingual text classification is relatively simple in the
evaluation mode. The evaluation method of fairness is the same as the
monolingual equality difference evaluation method, that is, the evaluation is
performed on a single language. We propose a multi-dimensional fairness
evaluation framework for multilingual text classification, which evaluates the
model's monolingual equality difference, multilingual equality difference,
multilingual equality performance difference, and destructiveness of the
fairness strategy. We hope that our work can provide a more general debiasing
method and a more comprehensive evaluation framework for multilingual text
fairness tasks