Machine translation systems are trained on a large parallel corpus of sentences of variable length. The models are extremely opaque (a complex system whose core

Machine translation systems are trained on a large parallel corpus of sentences of variable length. The models are extremely opaque (a complex system whose core is typically deep neural network). They are trained to take an arbitrary-length sequence of symbols as input, and produce an arbitrary-length sequence as output. There's basically no human in the loop. Data goes in, model comes out, it's tested for accuracy (using a metric called BLEU) on a test set, and if it does well on the test set, it is shipped. These systems are immensely complex, and it'd be quite silly to manually check for every possible error case.