  Identifying related entities and events within and across documents is
fundamental to natural language understanding. We present an approach to entity
and event coreference resolution utilizing contrastive representation learning.
Earlier state-of-the-art methods have formulated this problem as a binary
classification problem and leveraged large transformers in a cross-encoder
architecture to achieve their results. For large collections of documents and
corresponding set of $n$ mentions, the necessity of performing $n^{2}$
transformer computations in these earlier approaches can be computationally
intensive. We show that it is possible to reduce this burden by applying
contrastive learning techniques that only require $n$ transformer computations
at inference time. Our method achieves state-of-the-art results on a number of
key metrics on the ECB+ corpus and is competitive on others.
