Summary of Gate X-e : a Challenge Set For Gender-fair Translations From Weakly-gendered Languages, by Spencer Rarrick et al.
GATE X-E : A Challenge Set for Gender-Fair Translations from Weakly-Gendered Languages
by Spencer Rarrick, Ranjita Naik, Sundar Poudel, Vishal Chowdhary
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Neural Machine Translation (NMT) has made significant progress, but concerns about gender bias persist. Despite studies on gender bias in translations from weakly gendered languages into English, there are no benchmarks for evaluating this phenomenon or assessing mitigation strategies. To address this gap, we introduce GATE X-E, an extension to the GATE corpus, which features human translations from Turkish, Hungarian, Finnish, and Persian into English, accompanied by feminine, masculine, and neutral variants. This dataset, containing 1250-1850 instances per language pair, challenges translation rewriters on various linguistic phenomena. We also present a translation gender rewriting solution built with GPT-4 and evaluate it using GATE X-E. Our open-source contributions aim to encourage further research on gender debiasing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Neural Machine Translation is getting better, but there’s still a problem – gender bias. When machines translate texts from some languages into English, they can pick up biases and stereotypes that aren’t always fair or accurate. To help solve this issue, we’ve created a new dataset called GATE X-E. This dataset includes human translations of Turkish, Hungarian, Finnish, and Persian texts into English, along with variations that show feminine, masculine, and neutral options. We hope that by sharing our work, others will be inspired to help reduce gender bias in machine translation. |
Keywords
» Artificial intelligence » Gpt » Translation
