Set Learning for Generative Information Extraction
Jiangnan Li, Yice Zhang, Bin Liang, Kam-Fai Wong, Ruifeng Xu
Abstract
Recent efforts have endeavored to employ the sequence-to-sequence (Seq2Seq) model in Information Extraction (IE) due to its potential to tackle multiple IE tasks in a unified manner. Under this formalization, multiple structured objects are concatenated as the target sequence in a predefined order. However, structured objects, by their nature, constitute an unordered set. Consequently, this formalization introduces a potential order bias, which can impair model learning. Targeting this issue, this paper proposes a set learning approach that considers multiple permutations of structured objects to optimize set probability approximately. Notably, our approach does not require any modifications to model structures, making it easily integrated into existing generative IE frameworks. Experiments show that our method consistently improves existing frameworks on vast tasks and datasets.- Anthology ID:
- 2023.emnlp-main.806
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 13043–13052
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.806
- DOI:
- 10.18653/v1/2023.emnlp-main.806
- Cite (ACL):
- Jiangnan Li, Yice Zhang, Bin Liang, Kam-Fai Wong, and Ruifeng Xu. 2023. Set Learning for Generative Information Extraction. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 13043–13052, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Set Learning for Generative Information Extraction (Li et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2023.emnlp-main.806.pdf