Shall We Trust All Relational Tuples by Open Information Extraction? A Study on Speculation Detection
Not on the map. Why?
Abstract (arXiv)
Open Information Extraction (OIE) aims to extract factual relational tuples from open-domain sentences. Downstream tasks use the extracted OIE tuples as facts, without examining the certainty of these facts. However, uncertainty/speculation is a common linguistic phenomenon. Existing studies on speculation detection are defined at sentence level, but even if a sentence is determined to be speculative, not all tuples extracted from it may be speculative. In this paper, we propose to study speculations in OIE and aim to determine whether an extracted tuple is speculative. We formally define the research problem of tuple-level speculation detection and conduct a detailed data analysis on the LSOIE dataset which contains labels for speculative tuples. Lastly, we propose a baseline model OIE-Spec for this new research task.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 35 references that are in the map. First posted on arXiv 7 May 2023.
Builds on
14 of 35 references.
References · 14
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019Composition-based Multi-Relational Graph Convolutional Networkscited by 396
- 2018Neural Open Information Extractioncited by 50
- 2018Large-Scale QA-SRL Parsingcited by 45
- 2019Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputscited by 38
- 2020IMoJIE: Iterative Memory-Based Joint Open Information Extractioncited by 34
- 2022A Survey on Neural Open Information Extraction: Current Status and Future Directionscited by 27
- 2017Answering Complex Questions Using Open Information Extractioncited by 25
- 2021LSOIE: A Large-Scale Dataset for Supervised Open Information Extractioncited by 15
- 2022DetIE: Multilingual Open Information Extraction Inspired by Object Detectioncited by 11
- 2021DocOIE: A Document-level Context-Aware Dataset for OpenIEcited by 10
- 2016Exploring Different Dimensions of Attention for Uncertainty Detectioncited by 7
2 more
Built on by
Not cited yet.