Post-editing gender-fair language is faster than translating it, but still cognitively demanding.
What is it about?
The article investigates whether machine translation can be useful to produce gender-fair translations despite its gender bias. Twelve language professionals had to either translate or post-edit three English-language texts mentioning non-binary actors into German. For each text, they had to use a different gender-fair language approach, i.e. gender-neutral rewording, gender-inclusive characters, and neosystems. Results from screen recordings, retrospective interviews, and target text analysis show that, while post-editing is usually faster than translation, the perceived cognitive effort is generally high with no significant differences emerging in the translation process and, partially, the number of mistakes in the use of GFL.
Why is it important?
The article promotes the visibility of non-binary people, investigating the use of different linguistic strategies to make them visible. Findings show that post-editing gender biased MT outputs is cognitively demanding but it can lead to the production of more gender-fair texts.