milIE: Modular & Iterative Multilingual Open Information Extraction
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 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 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.
Keywords
Cite
@article{arxiv.2110.08144,
title = {milIE: Modular & Iterative Multilingual Open Information Extraction},
author = {Bhushan Kotnis and Kiril Gashteovski and Daniel Oñoro Rubio and Vanesa Rodriguez-Tembras and Ammar Shaker and Makoto Takamoto and Mathias Niepert and Carolin Lawrence},
journal= {arXiv preprint arXiv:2110.08144},
year = {2022}
}