Reducing idiom context results in worse translation scores, with full-context pretraining yielding the best results. While reducing idiom context barely affects random models, it is catastrophic for pretrained models. This implies that pretrained models are more global, containing information related to surrounding idiom context, whereas random models are myopic and rely on information related only to the idiom tokens themselves.