@inproceedings{eberts-ulges-2021-end,
title = "An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning",
author = "Eberts, Markus and
Ulges, Adrian",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.319",
doi = "10.18653/v1/2021.eacl-main.319",
pages = "3650--3660",
abstract = "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.",
}
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%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
Markdown (Informal)
[An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning](https://aclanthology.org/2021.eacl-main.319) (Eberts & Ulges, EACL 2021)
ACL