For many NLP applications, the natural unit of interest may be larger than a single word (or token). Question answering, syntactic parsing, coreference and semantic role labeling applications all involve the identification and classification of constituents, or phrases. This suggests that a span-oriented masked learning objective might provide improved performance on such tasks.