MILIE: Modular & Iterative Multilingual Open Information Extraction

Bhushan Kotnis, Kiril Gashteovski, Daniel Rubio, Ammar Shaker, Vanesa Rodriguez-Tembras, Makoto Takamoto, Mathias Niepert, Carolin Lawrence


Abstract
Open Information Extraction (OpenIE) is the task of extracting (subject, predicate, object) triples from natural language sentences. Current OpenIE systems extract all triple slots independently. In contrast, we explore the hypothesis that it may be beneficial to extract triple slots iteratively: first extract easy slots, followed by the difficult ones by conditioning on the easy slots, and therefore achieve a better overall extraction.Based on this hypothesis, we propose a neural OpenIE system, MILIE, that operates in an iterative fashion. Due to the iterative nature, the system is also modularit is possible to seamlessly integrate rule based extraction systems with a neural end-to-end system, thereby allowing rule based systems to supply extraction slots which MILIE can leverage for extracting the remaining slots. We confirm our hypothesis empirically: MILIE outperforms SOTA systems on multiple languages ranging from Chinese to Arabic. Additionally, we are the first to provide an OpenIE test dataset for Arabic and Galician.
Anthology ID:
2022.acl-long.478
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6939–6950
Language:
URL:
https://aclanthology.org/2022.acl-long.478
DOI:
10.18653/v1/2022.acl-long.478
Bibkey:
Cite (ACL):
Bhushan Kotnis, Kiril Gashteovski, Daniel Rubio, Ammar Shaker, Vanesa Rodriguez-Tembras, Makoto Takamoto, Mathias Niepert, and Carolin Lawrence. 2022. MILIE: Modular & Iterative Multilingual Open Information Extraction. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6939–6950, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
MILIE: Modular & Iterative Multilingual Open Information Extraction (Kotnis et al., ACL 2022)
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PDF:
https://aclanthology.org/2022.acl-long.478.pdf
Software:
 2022.acl-long.478.software.zip