What you need to know about machine translation quality estimation (MTQE) — from benefits and challenges to industry adoption — as discussed by Adam Bittlingmayer and Conchita Laguardia.
New research suggests biases of QE datasets lead to guesstimates on machine translation quality. Researchers propose modified dataset, debut it at WMT 2020.
US researchers outline how technology could be used to support the work of simultaneous interpreters. They propose analyzing the linguist’s performance on the fly via real-time quality feedback loop and offer help only when the interpreter is struggling.
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