When I first started working
Posted: Sat Feb 08, 2025 7:13 am
Generally, and also common to my own experience, one of the main obstacles of debiasing approaches is that of improving gender translation, while preserving overall translation quality. For instance, we have experimented with different segmentation techniques applied to the decoder side of speech-to-text systems. When segmenting at the character level, we found that translations were slightly less fluent than the state-of-the-art, but feminine translation improved. It would thus be relevant to assess end users’ own perception of this apparent trade-off.
Besides technical interventions on the model, however, a set latvia mobile database of best practices must guide the whole creation pipeline of MT. These include dedicated test sets, evaluations, and careful annotation practices. on the topic, there was no benchmark available to measure bias on naturally occurring instances of gender translation; only simple, synthetic corpora.
Hence, we created the MuST-SHE benchmark, a multilingual, dedicated resource for more realistic testing conditions. As natural data are complex and gender translation language-specific, its creation required extensive manual work for data collection and annotation. Right now, my colleagues and I are facing similar challenges in the creation of a test set for gender-neutral translation, devoid of gender marking (e., service instead of waitress/waiter). As parallel data are lacking and gender-neutral language is highly language specific, we need to manually identify relevant gendered cases, and generate a viable neutralized translation.
This article is part of our series of expert interviews on the topic of gender bias in machine translation. Check out the other parts here:
She is pretty, he is smart: More on gender bias in MT with Dr. Eva Vanmassenhove
She is pretty, he is smart: More on gender bias in MT with Dr. Eva Vanmassenhove
“Why are women beautiful and men intelligent?
Besides technical interventions on the model, however, a set latvia mobile database of best practices must guide the whole creation pipeline of MT. These include dedicated test sets, evaluations, and careful annotation practices. on the topic, there was no benchmark available to measure bias on naturally occurring instances of gender translation; only simple, synthetic corpora.
Hence, we created the MuST-SHE benchmark, a multilingual, dedicated resource for more realistic testing conditions. As natural data are complex and gender translation language-specific, its creation required extensive manual work for data collection and annotation. Right now, my colleagues and I are facing similar challenges in the creation of a test set for gender-neutral translation, devoid of gender marking (e., service instead of waitress/waiter). As parallel data are lacking and gender-neutral language is highly language specific, we need to manually identify relevant gendered cases, and generate a viable neutralized translation.
This article is part of our series of expert interviews on the topic of gender bias in machine translation. Check out the other parts here:
She is pretty, he is smart: More on gender bias in MT with Dr. Eva Vanmassenhove
She is pretty, he is smart: More on gender bias in MT with Dr. Eva Vanmassenhove
“Why are women beautiful and men intelligent?