# LSOIE: A Large-Scale Dataset for Supervised Open Information Extraction

arXiv: https://arxiv.org/abs/2101.11177 · HTML: https://mlsanity.com/paper/2101.11177/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 27 Jan 2021.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [AllenNLP: A Deep Semantic Natural Language Processing Platform](https://mlsanity.com/paper/1803.07640/) (2018)
- [Simple BERT Models for Relation Extraction and Semantic Role Labeling](https://mlsanity.com/paper/1904.05255/) (2019)
- [Neural Open Information Extraction](https://mlsanity.com/paper/1805.04270/) (2018)
- [Large-Scale QA-SRL Parsing](https://mlsanity.com/paper/1805.05377/) (2018)
- [OPIEC: An Open Information Extraction Corpus](https://mlsanity.com/paper/1904.12324/) (2019)
- [WiRe57 : A Fine-Grained Benchmark for Open Information Extraction](https://mlsanity.com/paper/1809.08962/) (2018)
- [OpenKI: Integrating Open Information Extraction and Knowledge Bases with Relation Inference](https://mlsanity.com/paper/1904.12606/) (2019)
- [Improving Open Information Extraction via Iterative Rank-Aware Learning](https://mlsanity.com/paper/1905.13413/) (2019)

## Built on by (most cited first, 15 of 15)

- [A Survey of Intent Classification and Slot-Filling Datasets for Task-Oriented Dialog](https://mlsanity.com/paper/2207.13211/) (2022)
- [DetIE: Multilingual Open Information Extraction Inspired by Object Detection](https://mlsanity.com/paper/2206.12514/) (2022)
- [A Survey on Open Information Extraction from Rule-based Model to Large Language Model](https://mlsanity.com/paper/2208.08690/) (2022)
- [Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction](https://mlsanity.com/paper/2305.13981/) (2023)
- [Redwood: Using Collision Detection to Grow a Large-Scale Intent Classification Dataset](https://mlsanity.com/paper/2204.05483/) (2022)
- [When to Use What: An In-Depth Comparative Empirical Analysis of OpenIE Systems for Downstream Applications](https://mlsanity.com/paper/2211.08228/) (2022)
- [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)
- [Towards Generalized Open Information Extraction](https://mlsanity.com/paper/2211.15987/) (2022)
- [Shall We Trust All Relational Tuples by Open Information Extraction? A Study on Speculation Detection](https://mlsanity.com/paper/2305.04181/) (2023)
- [Learning Semantic Role Labeling from Compatible Label Sequences](https://mlsanity.com/paper/2305.14600/) (2023)
- [Leveraging Linguistically Enhanced Embeddings for Open Information Extraction](https://mlsanity.com/paper/2403.13903/) (2024)
- [textitBenchIEFL : A Manually Re-Annotated Fact-Based Open Information Extraction Benchmark](https://mlsanity.com/paper/2407.16860/) (2024)
- [Enoki: Efficient Multi-Level Hallucination Detection](https://mlsanity.com/paper/2609.00581/) (2026)
- [DiffIE: Diffusion-based Open Information Extraction](https://mlsanity.com/paper/2609.02315/) (2026)

## Abstract (arXiv)

Open Information Extraction (OIE) systems seek to compress the factual propositions of a sentence into a series of n-ary tuples. These tuples are useful for downstream tasks in natural language processing like knowledge base creation, textual entailment, and natural language understanding. However, current OIE datasets are limited in both size and diversity. We introduce a new dataset by converting the QA-SRL 2.0 dataset to a large-scale OIE dataset (LSOIE). Our LSOIE dataset is 20 times larger than the next largest human-annotated OIE dataset. We construct and evaluate several benchmark OIE models on LSOIE, providing baselines for future improvements on the task. Our LSOIE data, models, and code are made publicly available