OIE@OIA: an Adaptable and Efficient Open Information Extraction Framework

Xin Wang, Minlong Peng, Mingming Sun, Ping Li


Abstract
Different Open Information Extraction (OIE) tasks require different types of information, so the OIE field requires strong adaptability of OIE algorithms to meet different task requirements. This paper discusses the adaptability problem in existing OIE systems and designs a new adaptable and efficient OIE system - OIE@OIA as a solution. OIE@OIA follows the methodology of Open Information eXpression (OIX): parsing a sentence to an Open Information Annotation (OIA) Graph and then adapting the OIA graph to different OIE tasks with simple rules. As the core of our OIE@OIA system, we implement an end-to-end OIA generator by annotating a dataset (we make it open available) and designing an efficient learning algorithm for the complex OIA graph. We easily adapt the OIE@OIA system to accomplish three popular OIE tasks. The experimental show that our OIE@OIA achieves new SOTA performances on these tasks, showing the great adaptability of our OIE@OIA system. Furthermore, compared to other end-to-end OIE baselines that need millions of samples for training, our OIE@OIA needs much fewer training samples (12K), showing a significant advantage in terms of efficiency.
Anthology ID:
2022.acl-long.430
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Smaranda Muresan, Preslav Nakov, Aline Villavicencio
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6213–6226
Language:
URL:
https://aclanthology.org/2022.acl-long.430
DOI:
10.18653/v1/2022.acl-long.430
Bibkey:
Cite (ACL):
Xin Wang, Minlong Peng, Mingming Sun, and Ping Li. 2022. OIE@OIA: an Adaptable and Efficient Open Information Extraction Framework. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6213–6226, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
OIE@OIA: an Adaptable and Efficient Open Information Extraction Framework (Wang et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.430.pdf
Data
CaRBOIE2016