GenIE: Generative Information Extraction

Martin Josifoski, Nicola De Cao, Maxime Peyrard, Fabio Petroni, Robert West


Abstract
Structured and grounded representation of text is typically formalized by closed information extraction, the problem of extracting an exhaustive set of (subject, relation, object) triplets that are consistent with a predefined set of entities and relations from a knowledge base schema. Most existing works are pipelines prone to error accumulation, and all approaches are only applicable to unrealistically small numbers of entities and relations. We introduce GenIE (generative information extraction), the first end-to-end autoregressive formulation of closed information extraction. GenIE naturally exploits the language knowledge from the pre-trained transformer by autoregressively generating relations and entities in textual form. Thanks to a new bi-level constrained generation strategy, only triplets consistent with the predefined knowledge base schema are produced. Our experiments show that GenIE is state-of-the-art on closed information extraction, generalizes from fewer training data points than baselines, and scales to a previously unmanageable number of entities and relations. With this work, closed information extraction becomes practical in realistic scenarios, providing new opportunities for downstream tasks. Finally, this work paves the way towards a unified end-to-end approach to the core tasks of information extraction.
Anthology ID:
2022.naacl-main.342
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4626–4643
Language:
URL:
https://aclanthology.org/2022.naacl-main.342
DOI:
10.18653/v1/2022.naacl-main.342
Bibkey:
Cite (ACL):
Martin Josifoski, Nicola De Cao, Maxime Peyrard, Fabio Petroni, and Robert West. 2022. GenIE: Generative Information Extraction. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4626–4643, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
GenIE: Generative Information Extraction (Josifoski et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.342.pdf
Code
 epfl-dlab/genie
Data
FewRel