Summary of Evaluating Gender Bias in the Translation Of Gender-neutral Languages Into English, by Spencer Rarrick et al.
Evaluating Gender Bias in the Translation of Gender-Neutral Languages into English
by Spencer Rarrick, Ranjita Naik, Sundar Poudel, Vishal Chowdhary
First submitted to arxiv on: 15 Nov 2023
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning educators can breathe a sigh of relief as this paper tackles the long-standing issue of gender bias in machine translation (MT). The researchers introduce GATE X-E, an extension to the GATE corpus that provides human translations from Turkish, Hungarian, Finnish, and Persian into English with feminine, masculine, and neutral variants for each possible gender interpretation. This comprehensive dataset features natural sentences with varying lengths and domains, making it a challenging yet essential benchmark for evaluating MT models and assessing mitigation strategies. The authors also present an English gender rewriting solution built on GPT-3.5 Turbo and use GATE X-E to evaluate its performance. By opening sourcing their contributions, the researchers encourage further research on debiasing machine translations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine translation is getting better, but there’s a problem – it often repeats biases from one language to another. For example, if a machine translates a sentence about a doctor into English, it might use masculine pronouns even if the original sentence used gender-neutral terms. This paper helps fix this issue by creating a big dataset of translations with different genders (feminine, masculine, and neutral). They also test an AI model that can rewrite sentences to be more inclusive. By making their work open source, they hope others will help develop solutions to make machine translation more fair. |
Keywords
* Artificial intelligence * Gpt * Machine learning * Translation
