Gender Classification Accuracy and Fairness in Contextualized Models Under Static Embedding Debiasing on BiasBios

Neural machine translation has significantly pushed forward the quality of the field. However, there are remaining big issues with the output translations and one of them is fairness. Neural models are trained on large text corpora which contain biases and stereotypes. As a consequence, models inherit these social biases. Recent methods have shown results in reducing gender bias in other natural language processing tools such as word embeddings. We take advantage of the fact that word embeddings are used in neural machine translation to propose a method to equalize gender biases in neural mach

Research goal: How do static embedding debiasing techniques affect the gender classification accuracy and fairness metrics of contextualized models on the BiasBios dataset?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.7/10.