Automatically Identifying Gender Bias in Machine Translation using Perturbations
Abstract
Gender bias has been shown to affect many tasks applications in NLU.
In the setting of machine translation (MT), research has primarily focused on measuring bias via synthetic datasets.
We present an automatic method for identifying gender biases in MT using a novel-application of BERT-generated sentence perturbations.
Using this method, we compile a dataset to serve as a benchmark for evaluating gender bias in MT across a diverse range of languages.
Our dataset further serves to highlight the limitations of the current task definition which requires a single translation be produced, even in the presence of underspecified input.
In the setting of machine translation (MT), research has primarily focused on measuring bias via synthetic datasets.
We present an automatic method for identifying gender biases in MT using a novel-application of BERT-generated sentence perturbations.
Using this method, we compile a dataset to serve as a benchmark for evaluating gender bias in MT across a diverse range of languages.
Our dataset further serves to highlight the limitations of the current task definition which requires a single translation be produced, even in the presence of underspecified input.
Research Areas
