# milIE: Modular & Iterative Multilingual Open Information Extraction

arXiv: https://arxiv.org/abs/2110.08144 · HTML: https://mlsanity.com/paper/2110.08144/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 15 Oct 2021.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [IMoJIE: Iterative Memory-Based Joint Open Information Extraction](https://mlsanity.com/paper/2005.08178/) (2020)
- [Span Model for Open Information Extraction on Accurate Corpus](https://mlsanity.com/paper/1901.10879/) (2019)
- [Generating Query Focused Summaries from Query-Free Resources](https://mlsanity.com/paper/2012.14774/) (2020)
- [Multi2OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERT](https://mlsanity.com/paper/2009.08128/) (2020)
- [Answering Complex Questions Using Open Information Extraction](https://mlsanity.com/paper/1704.05572/) (2017)
- [Logician: A Unified End-to-End Neural Approach for Open-Domain Information Extraction](https://mlsanity.com/paper/1904.12535/) (2019)
- [Assertion-based QA with Question-Aware Open Information Extraction](https://mlsanity.com/paper/1801.07414/) (2018)
- [AnnIE: An Annotation Platform for Constructing Complete Open Information Extraction Benchmark](https://mlsanity.com/paper/2109.07464/) (2021)
- [Relation Schema Induction using Tensor Factorization with Side Information](https://mlsanity.com/paper/1605.04227/) (2016)

## Built on by (most cited first, 3 of 3)

- [Schema-adaptable Knowledge Graph Construction](https://mlsanity.com/paper/2305.08703/) (2023)
- [Syntactic Multi-view Learning for Open Information Extraction](https://mlsanity.com/paper/2212.02068/) (2022)
- [Open Information Extraction via Chunks](https://mlsanity.com/paper/2305.03299/) (2023)

## Abstract (arXiv)

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.