Why is it important for us to address gender bias in MT?
It is important because we are leaving out translations that are equally valid from a linguistic point of view. Furthermore, there is no way to tell the system which gender is preferred. As such, when translating a speech of someone who identifies as a woman/man, the translation will most likely only partially be correct (as it will sometimes use the masculine and sometimes the feminine gender depending on the data it was trained on).
Additionally, MT is sometimes used for downstream latvia mobile database tasks (example is given in the talk as well, for instance when you are looking for a "plumber" or a "nurse" and you use an MT system, half of the candidates will be eliminated).
There is this cool project 'Fairslator' (.fairslatorom) that does a pretty good job at providing multiple translations. Main challenges are that languages differ considerably when it comes to gender agreement/markers. As such, there is no one-solution-fits-all.
It furthermore requires quite some linguistic knowledge to be able to tackle gender in MT. People have experimented with adding additional features that incorporate information such as gender to teach the system how to generate the right translation given a particular context. Others have focused on debiasing word embeddings (although this doesn't really resolve the issue for MT specifically; it attempts to make the data more fair).