  Span identification aims at identifying specific text spans from text input
and classifying them into pre-defined categories. Different from previous works
that merely leverage the Subordinate (SUB) relation (i.e. if a span is an
instance of a certain category) to train models, this paper for the first time
explores the Peer (PR) relation, which indicates that two spans are instances
of the same category and share similar features. Specifically, a novel Peer
Data Augmentation (PeerDA) approach is proposed which employs span pairs with
the PR relation as the augmentation data for training. PeerDA has two unique
advantages: (1) There are a large number of PR span pairs for augmenting the
training data. (2) The augmented data can prevent the trained model from
over-fitting the superficial span-category mapping by pushing the model to
leverage the span semantics. Experimental results on ten datasets over four
diverse tasks across seven domains demonstrate the effectiveness of PeerDA.
Notably, PeerDA achieves state-of-the-art results on six of them.
