Can you give us a quick explanation of what your speech was about?
Posted: Sat Feb 08, 2025 7:13 am
In this article, we catch up with Matt Riemland, who presented his talk titled “Machine translation and technocracy: Mitigating issues of power parity in MT for low-resource languages” at the NeTTT conference this year. Here, he talks about how machine translation can impact speakers of low-resource languages, and what needs to be done to mitigate the risks of the technology toward these marginalized communities.
My presentation focused on the ways in which low-resource latvia mobile database machine translation (MT) systems may intertwine with social inequalities in humanitarian contexts. Although researchers have long envisioned that machine translation would be a tool used for humanitarian purposes, progress towards these altruistic ambitions has been somewhat disappointing.
MT has greatly enhanced translation practices in commercial industries, of course, but it’s still largely absent from humanitarian work, by which I’m primarily referring to global development initiatives and crisis responses. (There are notable exceptions, such as Translators without Borders’ Gamayun initiative.)
Part of the reason for this gap is that the marginalized (typically Global South) communities targeted by these humanitarian efforts generally speak low-resource languages. Essentially, there is far less training data available for these languages, so they’re much less conducive to modern data-driven MT architectures.
My presentation focused on the ways in which low-resource latvia mobile database machine translation (MT) systems may intertwine with social inequalities in humanitarian contexts. Although researchers have long envisioned that machine translation would be a tool used for humanitarian purposes, progress towards these altruistic ambitions has been somewhat disappointing.
MT has greatly enhanced translation practices in commercial industries, of course, but it’s still largely absent from humanitarian work, by which I’m primarily referring to global development initiatives and crisis responses. (There are notable exceptions, such as Translators without Borders’ Gamayun initiative.)
Part of the reason for this gap is that the marginalized (typically Global South) communities targeted by these humanitarian efforts generally speak low-resource languages. Essentially, there is far less training data available for these languages, so they’re much less conducive to modern data-driven MT architectures.