  We hypothesize that explicit integration of contextual information into an
Multi-task Learning framework would emphasize the significance of context for
boosting performance in jointly learning Named Entity Recognition (NER) and
Relation Extraction (RE). Our work proves this hypothesis by segmenting
entities from their surrounding context and by building contextual
representations using each independent segment. This relation representation
allows for a joint NER/RE system that achieves near state-of-the-art (SOTA)
performance on both NER and RE tasks while beating the SOTA RE system at
end-to-end NER & RE with a 49.07 F1.
