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.- Anthology ID:
- 2021.eacl-main.319
- Original:
- 2021.eacl-main.319v1
- Version 2:
- 2021.eacl-main.319v2
- Volume:
- Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Editors:
- Paola Merlo, Jorg Tiedemann, Reut Tsarfaty
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3650–3660
- Language:
- URL:
- https://aclanthology.org/2021.eacl-main.319
- DOI:
- 10.18653/v1/2021.eacl-main.319
- Cite (ACL):
- Markus Eberts and Adrian Ulges. 2021. An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 3650–3660, Online. Association for Computational Linguistics.
- Cite (Informal):
- An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning (Eberts & Ulges, EACL 2021)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2021.eacl-main.319.pdf
- Code
- lavis-nlp/jerex
- Data
- DocRED, Re-DocRED