This sometimes can lead to poor quality translations because translation context is limited by the value of n. In contrast, example-based MT (EBMT) usually stores the full sentence pairs in source and target data in an example base and uses translations of sentences similar to the input sentence as a template for translation.
eBMT systems can often better capture long range dependencies and rich morphology.