related ... (2020) "A Scalable Approach to Reducing Gender Bias in Google Translate" https://ai.googleblog.com/2020/04/a-scalable-approach-to-reducing-gender.

related ... (2020) "A Scalable Approach to Reducing Gender Bias in Google Translate" https://ai.googleblog.com/2020/04/a-scalable-approach-to-reducing-gender.html https://ai.googleblog.com/2020/04/a-scalable-approach-to-red... "We also devised a new method of evaluation, named bias reduction, which measures the relative reduction of bias between the new translation system and the existing system. Here “bias” is defined as making a gender choice in the translation that is unspecified in the source. For example, if the current system is biased 90% of the time and the new system is biased 45% of the time, this results in a 50% relative bias reduction. Using this metric, the new approach results in a bias reduction of ≥90% for translations from Hungarian, Finnish and Persian-to-English. The bias reduction of the existing Turkish-to-English system improved from 60% to 95% with the new approach. Our system triggers gender-specific translations with an average precision of 97% (i.e., when we decide to show gender-specific translations we’re right 97% of the time)." + (2018) "Providing Gender-Specific Translations in Google Translate" https://ai.googleblog.com/2018/12/providing-gender-specific-translations.html https://ai.googleblog.com/2018/12/providing-gender-specific-... "Detecting Gender-Neutral Queries Many Turkish sentences that refer to people are gender-neutral, but not all are. Detecting which queries are eligible for gender-specific translations is a hard problem because Turkish is morphologically complex, meaning that reference to a person can either be explicit with a gender-neutral pronoun (e.g. O, Ona) or implicitly encoded. For example, the sentence “Biliyor mu?” has no explicit gender-neutral pronoun but can be translated as either “Does she know?” or “Does he know?”. This complexity means that we cannot use a simple list of gender-neutral pronouns to detect gender-neutral Turkish queries and need a machine-learned system. We estimate that approximately 10% of Turkish Translate queries are ambiguous, and eligible for both feminine and masculine translations."