There’s been a fair amount of recent research highlighting the importance of MT literacy among end users. It’s not hard to imagine a scenario in which ill-equipped users of low-resource.
If they don’t understand how the technology works, what kinds of texts it is suitable for, or how to post-edit MT output effectively, they won’t get much use out of it. Not to mention the risk of translation errors that might get overlooked by post-editors who aren’t accustomed to working with MT output.
In the case of machine translation, how might it exacerbate latvia mobile database or perpetuate inequalities related to low-resource languages (and the communities that speak them)?
Well, imagine a future scenario in which a large, well-funded NGO—operating in a major international language like Spanish—implements an MT system that allows them to translate materials about their development projects from Spanish into the language of a local indigenous community. If the NGO is uncritical about its unilateral decision-making, and if its goal is merely to inform the indigenous community members of whatever they decide, the organization may consider this one-way communication channel to be a success. The development planners may believe that they’re promoting inclusivity by translating into a low-resource language, when in practice they’ve simply reinforced that the indigenous community is merely there to receive instructions.