In neural machine translation experiments, the upsampling testing condition is similar to the joint testing condition—which trains the model on a dataset containing both regular parallel data and a specific subset of idiom data (idiom-train data)—but the idiom-train data is upsampled $N$ times. This condition evaluates whether upsampling targeted training data is necessary or effective to achieve better idiom translation quality.