Reducing Gender Bias in Machine Translation through Counterfactual Data Generation

TL;DR

This paper introduces a simple yet effective method to reduce gender bias in neural machine translation by augmenting training data with counterfactual examples, improving gender accuracy across multiple languages.

Contribution

It proposes a novel domain-adaptation approach using counterfactual data generation to mitigate gender bias without sacrificing translation quality.

Findings

Significant reduction in gender bias on WinoMT test set

Effective across English to French, Spanish, and Italian translations

Maintains translation quality while reducing bias

Abstract

Recent advances in neural methods have led to substantial improvement in the quality of Neural Machine Translation (NMT) systems. However, these systems frequently produce translations with inaccurate gender (Stanovsky et al., 2019), which can be traced to bias in training data. Saunders and Byrne (2020) tackle this problem with a handcrafted dataset containing balanced gendered profession words. By using this data to fine-tune an existing NMT model, they show that gender bias can be significantly mitigated, albeit at the expense of translation quality due to catastrophic forgetting. They recover some of the lost quality with modified training objectives or additional models at inference. We find, however, that simply supplementing the handcrafted dataset with a random sample from the base model training corpus is enough to significantly reduce the catastrophic forgetting. We also propose a novel domain-adaptation technique that leverages in-domain data created with the counterfactual data generation techniques proposed by Zmigrod et al. (2019) to further improve accuracy on the WinoMT challenge test set without significant loss in translation quality. We show its effectiveness in NMT systems from English into three morphologically rich languages French, Spanish, and Italian. The relevant dataset and code will be available at Github.