Indeed, gender bias in MT leads to feminine under-representation and yields a reduced quality of the MT service offered to women. And if we account for non-binary language and individuals, they are completely absent in current MT systems.
Note that biased MT outputs do not merely reflect our own societal biases, but can actively reinforce them. Such is the case of stereotypical translations, which can feed into existing prejudiced assumptions and negative generalizations (e., only men are qualified for high-level positions.
As MT is increasingly deployed at large scale and under different latvia mobile database scenarios -- ranging from social media to work-related activities or even for legal texts -- gender bias has the potential to impact a wide array of people. Additionally, since MT output can be used as textual data to develop future language technologies, biased language will be fed into and propagated by the next set of models as well.
Can you give us a quick overview of your findings from your research work,
The paper is a literature review of current studies on the understanding, assessment and mitigation of gender bias in MT. First and foremost, the review exhibited how this rapidly growing area of research had been characterized by disparate, technically-oriented efforts, based on occasionally incompatible and narrow conceptualizations of gender bias.