Adversarial training for multi-context joint entity and relation extraction
Giannis Bekoulis, Johannes Deleu, Thomas Demeester, Chris Develder
Abstract
Adversarial training (AT) is a regularization method that can be used to improve the robustness of neural network methods by adding small perturbations in the training data. We show how to use AT for the tasks of entity recognition and relation extraction. In particular, we demonstrate that applying AT to a general purpose baseline model for jointly extracting entities and relations, allows improving the state-of-the-art effectiveness on several datasets in different contexts (i.e., news, biomedical, and real estate data) and for different languages (English and Dutch).- Anthology ID:
- D18-1307
- Original:
- D18-1307v1
- Version 2:
- D18-1307v2
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2830–2836
- Language:
- URL:
- https://aclanthology.org/D18-1307
- DOI:
- 10.18653/v1/D18-1307
- Cite (ACL):
- Giannis Bekoulis, Johannes Deleu, Thomas Demeester, and Chris Develder. 2018. Adversarial training for multi-context joint entity and relation extraction. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2830–2836, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Adversarial training for multi-context joint entity and relation extraction (Bekoulis et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/D18-1307.pdf
- Code
- bekou/multihead_joint_entity_relation_extraction
- Data
- ACE 2004, Adverse Drug Events (ADE) Corpus