%0 Conference Proceedings %T Span-Level Model for Relation Extraction %A Dixit, Kalpit %A Al-Onaizan, Yaser %Y Korhonen, Anna %Y Traum, David %Y Màrquez, Lluís %S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics %D 2019 %8 July %I Association for Computational Linguistics %C Florence, Italy %F dixit-al-onaizan-2019-span %X 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 span-level 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. %R 10.18653/v1/P19-1525 %U https://aclanthology.org/P19-1525 %U https://doi.org/10.18653/v1/P19-1525 %P 5308-5314