In NMT idiom-translation experiments, more encoder context and pretraining produce lower reference-translation perplexity, meaning that the model assigns greater likelihood to the reference translation. Among randomly initialized models, training on the joint data split consistently lowers perplexity, whereas upsampling idiom-training data worsens perplexity, a pattern attributed to overfitting.