A Scalable Approach to Reducing Gender Bias in Google Translate

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This research details an improved, scalable method using a rewriting-based paradigm to reduce gender bias in Google Translate's output.

This article describes Google's efforts to address gender bias in its Translate service, specifically focusing on translations between gender-specific and gender-neutral languages. Initially, Google Translate provided gender-specific options for Turkish-to-English translations, but this approach faced scalability issues, leading to low recall and data-intensive requirements for detecting gender-neutrality across languages. The improved method, a rewriting-based approach, generates an initial translation, identifies gender-specific instances from gender-neutral sources, and then applies a sentence-level rewriter to create an alternative gendered translation. This new paradigm is more scalable as it eliminates the need for a gender-neutrality detector, especially for translations from gender-neutral languages to English. The rewriter is trained on a large dataset of programmatically generated masculine and feminine phrase pairs, using a language model to resolve ambiguities. This enhanced approach has expanded gender-specific translations to include Finnish, Hungarian, and Persian-to-English, and has replaced the previous Turkish-to-English system. Evaluation using a 'bias reduction' metric shows a significant decrease in gender bias (≥90% for new language pairs, 95% for Turkish-to-English) and a high precision of 97% for triggering gender-specific translations. Google plans to extend this work to document-level translation.

Key Features

Detecting gender-neutral queries
Generating gender-specific translations
Rewriting-based translation method
Sentence-level rewriter
Language model for candidate selection
Bias reduction evaluation metric

Pros

Significantly reduces gender bias in translations (≥90% bias reduction)
More scalable than previous methods, especially for gender-neutral source languages
Does not require a gender-neutrality detector for each source language
High precision (97%) in triggering gender-specific translations
Expanded gender-specific translations to more language pairs

Cons

Initial approach had low recall (failed to show gender-specific translations for up to 40% of queries)
Initial approach was data-intensive for building gender-neutrality classifiers
Requires generating large datasets for rewriter training
Focuses on sentence-level translation, with document-level translation as a future plan
Specific limitations of the language model for tie-breaking are not detailed

Use Cases

Translating gender-neutral phrases into gender-specific languages with accurate gender options
Improving fairness and reducing bias in AI-powered translation services
Developing scalable solutions for complex NLP challenges

Best For

Google Translate users
Researchers in NLP and machine translation
Developers interested in AI ethics and bias mitigation

Platforms: Web

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