Abstract
“Named entity recognition and relation extraction are core sub-tasks of relational triple extraction. Recent studies have used parameter sharing or joint decoding to create interaction between these two tasks. However, ensuring the specificity of task-specific traits while the two tasks interact properly is a huge difficulty. We propose a multi-gate encoder that models bidirectional task interaction while keeping sufficient feature specificity based on gating mechanism in this paper. Precisely, we design two types of independent gates: task gates to generate task-specific features and interaction gates to generate instructive features to guide the opposite task. Our experiments show that our method increases the state-of-the-art (SOTA) relation F1 scores on ACE04, ACE05 and SciERC datasets to 63.8% (+1.3%), 68.2% (+1.4%), 39.4% (+1.0%), respectively, with higher inference speed over previous SOTA model.”- Anthology ID:
- 2022.ccl-1.75
- Volume:
- Proceedings of the 21st Chinese National Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Nanchang, China
- Editors:
- Maosong Sun (孙茂松), Yang Liu (刘洋), Wanxiang Che (车万翔), Yang Feng (冯洋), Xipeng Qiu (邱锡鹏), Gaoqi Rao (饶高琦), Yubo Chen (陈玉博)
- Venue:
- CCL
- SIG:
- Publisher:
- Chinese Information Processing Society of China
- Note:
- Pages:
- 848–860
- Language:
- English
- URL:
- https://aclanthology.org/2022.ccl-1.75
- DOI:
- Cite (ACL):
- Xiong Xiong, Liu Yunfei, Liu Anqi, Gong Shuai, and Li Shengyang. 2022. A Multi-Gate Encoder for Joint Entity and Relation Extraction. In Proceedings of the 21st Chinese National Conference on Computational Linguistics, pages 848–860, Nanchang, China. Chinese Information Processing Society of China.
- Cite (Informal):
- A Multi-Gate Encoder for Joint Entity and Relation Extraction (Xiong et al., CCL 2022)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2022.ccl-1.75.pdf
- Data
- ACE 2005, SciERC