Abstract
Previous neural Seq2Seq models have shown the effectiveness for jointly extracting relation triplets. However, most of these models suffer from incompletion and disorder problems when they extract multi-token entities from input sentences. To tackle these problems, we propose a generative, multi-task learning framework, named GenerativeRE. We firstly propose a special entity labelling method on both input and output sequences. During the training stage, GenerativeRE fine-tunes the pre-trained generative model and learns the special entity labels simultaneously. During the inference stage, we propose a novel copy mechanism equipped with three mask strategies, to generate the most probable tokens by diminishing the scope of the model decoder. Experimental results show that our model achieves 4.6% and 0.9% F1 score improvements over the current state-of-the-art methods in the NYT24 and NYT29 benchmark datasets respectively.- Anthology ID:
- 2021.findings-emnlp.182
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2119–2126
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.182
- DOI:
- 10.18653/v1/2021.findings-emnlp.182
- Cite (ACL):
- Jiarun Cao and Sophia Ananiadou. 2021. GenerativeRE: Incorporating a Novel Copy Mechanism and Pretrained Model for Joint Entity and Relation Extraction. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2119–2126, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- GenerativeRE: Incorporating a Novel Copy Mechanism and Pretrained Model for Joint Entity and Relation Extraction (Cao & Ananiadou, Findings 2021)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/2021.findings-emnlp.182.pdf