milIE: Modular & Iterative Multilingual Open Information Extraction
This addresses the challenge of improving extraction accuracy in multilingual OpenIE, though it is incremental as it builds on existing neural and rule-based methods.
The paper tackled the problem of extracting triples in Open Information Extraction by proposing an iterative approach that extracts easy slots first and conditions on them for difficult ones, resulting in milIE outperforming state-of-the-art systems across multiple languages including Chinese and Arabic.
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.