Abstract
Relation Extraction is the task of identifying
entity mention spans in raw text and then identifying relations between pairs of the entity
mentions. Recent approaches for this spanlevel task have been token-level models which
have inherent limitations. They cannot easily define and implement span-level features,
cannot model overlapping entity mentions and
have cascading errors due to the use of sequential decoding. To address these concerns, we
present a model which directly models all possible spans and performs joint entity mention
detection and relation extraction. We report a
new state-of-the-art performance of 62.83 F1
(prev best was 60.49) on the ACE2005 dataset.