Abstract
Neural Machine Translation (NMT) continues to improve in quality and adoption, yet the inadvertent perpetuation of gender bias remains a significant concern. Despite numerous studies on gender bias in translations into English from weakly gendered-languages, there are no benchmarks for evaluating this phenomenon or for assessing mitigation strategies. To address this gap, we introduce GATE X-E, an extension to the GATE (Rarrick et al., 2023) corpus, that consists of human translations from Turkish, Hungarian, Finnish, and Persian into English. Each translation is accompanied by feminine, masculine, and neutral variants. The dataset, which contains between 1250 and 1850 instances for each of the four language pairs, features natural sentences with a wide range of sentence lengths and domains, challenging translation rewriters on various linguistic phenomena. Additionally, we present a translation gender rewriting solution built with GPT-4 and use GATE X-E to evaluate it. We open source our contributions to encourage further research on gender debiasing.
Abstract (translated)
神经机器翻译（NMT）在质量和采用方面继续改进，然而无意中延续性别偏见仍然是一个重要的问题。尽管关于弱化性别偏见在英语翻译中的研究已经很多，但目前尚无评估这一现象或评估减轻策略的基准。为了填补这一空白，我们引入了GATE X-E，一个扩展自GATE（Rarrick等人，2023）语料库的土耳其语、匈牙利语、芬兰语和波斯语对英语的人翻译。每个翻译都附带女性、男性和中性版本。该数据集（每对语言之间包含1250到1850个实例）包含了各种长度的句子和主题，挑战了翻译者应对各种语言现象。此外，我们还提出了一个基于GPT-4的翻译性别重写解决方案，并使用GATE X-E对其进行评估。我们开源我们的贡献，鼓励进一步研究性别偏见。
URL
https://arxiv.org/abs/2402.14277
