  Natural Language Processing tasks such as resolving the coreference of events
require understanding the relations between two text snippets. These tasks are
typically formulated as (binary) classification problems over independently
induced representations of the text snippets. In this work, we develop a
Pairwise Representation Learning (PairwiseRL) scheme for the event mention
pairs, in which we jointly encode a pair of text snippets so that the
representation of each mention in the pair is induced in the context of the
other one. Furthermore, our representation supports a finer, structured
representation of the text snippet to facilitate encoding events and their
arguments. We show that PairwiseRL, despite its simplicity, outperforms the
prior state-of-the-art event coreference systems on both cross-document and
within-document event coreference benchmarks. We also conduct in-depth analysis
in terms of the improvement and the limitation of pairwise representation so as
to provide insights for future work.
