In your own practice, what have been the challenges?
Posted: Sat Feb 08, 2025 7:13 am
Since gender bias in MT is a sociotechnical problem, in the paper my colleagues and I systematized existing work on the topic, also informed by relevant knowledge and notions from neighbouring fields, like sociolinguistics and the social sciences. Also, to counter the widespread but limited view that singles out gender asymmetries in the training data as the ultimate cause for bias in MT, the paper overviews several other constraints and factors that contribute to its emergence.
Finally, we found a series of blindspots and challenges for gender latvia mobile database bias in MT. Among them is a trajectory towards the inclusion of non-binary gender and language, which is now growing. Also, we underscored how gender bias in MT has been largely limited to text-to-text systems. The implications of gender bias for other non-textual modalities, however, might be different. Indeed, in our work, we found that speech-to-text systems exploit speakers’ vocal characteristics as a gender cue to improve feminine translation. However, relying on physical gender cues (e., pitch) for such a task implies reductionist gender classifications making systems potentially harmful for a diverse range of users.
What are the ways that we can address gender bias in MT?
As mentioned above, different mitigation strategies for (binary) gender bias in MT have been put forward. Some of the most popular approaches imply injecting external knowledge into the model (e., speaker’s gender) to guide translation. Others, instead, attempt to prevent models from learning stereotypical associations at training time by creating more balanced training data. For ambiguous queries, also, an additional rewriting step can be applied to an MT output, so as to always obtain both masculine and feminine translation alternatives.
Finally, we found a series of blindspots and challenges for gender latvia mobile database bias in MT. Among them is a trajectory towards the inclusion of non-binary gender and language, which is now growing. Also, we underscored how gender bias in MT has been largely limited to text-to-text systems. The implications of gender bias for other non-textual modalities, however, might be different. Indeed, in our work, we found that speech-to-text systems exploit speakers’ vocal characteristics as a gender cue to improve feminine translation. However, relying on physical gender cues (e., pitch) for such a task implies reductionist gender classifications making systems potentially harmful for a diverse range of users.
What are the ways that we can address gender bias in MT?
As mentioned above, different mitigation strategies for (binary) gender bias in MT have been put forward. Some of the most popular approaches imply injecting external knowledge into the model (e., speaker’s gender) to guide translation. Others, instead, attempt to prevent models from learning stereotypical associations at training time by creating more balanced training data. For ambiguous queries, also, an additional rewriting step can be applied to an MT output, so as to always obtain both masculine and feminine translation alternatives.