  Entity types and textual context are essential properties for sentence-level
relation extraction (RE). Existing work only encodes these properties within
individual instances, which limits the performance of RE given the insufficient
features in a single sentence. In contrast, we model these properties from the
whole dataset and use the dataset-level information to enrich the semantics of
every instance. We propose the GRAPHCACHE (Graph Neural Network as Caching)
module, that propagates the features across sentences to learn better
representations for RE. GRAPHCACHE aggregates the features from sentences in
the whole dataset to learn global representations of properties, and use them
to augment the local features within individual sentences. The global property
features act as dataset-level prior knowledge for RE, and a complement to the
sentence-level features. Inspired by the classical caching technique in
computer systems, we develop GRAPHCACHE to update the property representations
in an online manner. Overall, GRAPHCACHE yields significant effectiveness gains
on RE and enables efficient message passing across all sentences in the
dataset.
