Modular and Iterative Multilingual Open Information Extraction

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

Proc. of the 60th Annual Meeting of the Association for Computational Linguistics (ACL), 2022.

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 investigate 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, MILLIE, that operates in an iterative fashion. Due to the iterative nature, the system is also modular: it 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 MILLIE can leverage for extracting the remaining slots. We confirm our hypothesis empirically: MILLIE outperforms SOTA systems on multiple languages ranging from Chinese to Arabic. Additionally, we are the first to provide an OpenIE test dataset for Arabic.

Links

doi:

Paper: https://openreview.net/pdf?id=KNqKOUnl_3F

BibTeX

@inproceedings{kotnis22_acl, title = {Modular and Iterative Multilingual Open Information Extraction}, author = {Kotnis, Bhushan and Gashteovski, Kiril and Rubio, Daniel Onoro and Shaker, Ammar and Rodriguez-Tembras, Vanesa and Takamoto, Makoto and Niepert, Mathias and Lawrence, Carolin}, year = {2022}, booktitle = {Proc. of the 60th Annual Meeting of the Association for Computational Linguistics (ACL)}, doi = {}, url = {https://openreview.net/pdf?id=KNqKOUnl_3F} }