%0 Conference Proceedings %T An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning %A Eberts, Markus %A Ulges, Adrian %Y Merlo, Paola %Y Tiedemann, Jorg %Y Tsarfaty, Reut %S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume %D 2021 %8 April %I Association for Computational Linguistics %C Online %F eberts-ulges-2021-end %X We present a joint model for entity-level relation extraction from documents. In contrast to other approaches - which focus on local intra-sentence mention pairs and thus require annotations on mention level - our model operates on entity level. To do so, a multi-task approach is followed that builds upon coreference resolution and gathers relevant signals via multi-instance learning with multi-level representations combining global entity and local mention information. We achieve state-of-the-art relation extraction results on the DocRED dataset and report the first entity-level end-to-end relation extraction results for future reference. Finally, our experimental results suggest that a joint approach is on par with task-specific learning, though more efficient due to shared parameters and training steps. %R 10.18653/v1/2021.eacl-main.319 %U https://aclanthology.org/2021.eacl-main.319 %U https://doi.org/10.18653/v1/2021.eacl-main.319 %P 3650-3660