# Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers

arXiv: https://arxiv.org/abs/1902.01030 · HTML: https://mlsanity.com/paper/1902.01030/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2019.

## Built on by (most cited first, 19 of 19)

- [Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction](https://mlsanity.com/paper/2102.10249/) (2021)
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- [A Survey on Extraction of Causal Relations from Natural Language Text](https://mlsanity.com/paper/2101.06426/) (2021)
- [Few-Shot Document-Level Relation Extraction](https://mlsanity.com/paper/2205.02048/) (2022)
- [Can Transformers Capture Spatial Relations between Objects?](https://mlsanity.com/paper/2403.00729/) (2024)
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- [AIFB-WebScience at SemEval-2022 Task 12: Relation Extraction First -- Using Relation Extraction to Identify Entities](https://mlsanity.com/paper/2203.05325/) (2022)
- [Query-based Instance Discrimination Network for Relational Triple Extraction](https://mlsanity.com/paper/2211.01797/) (2022)
- [INFINITY: A Simple Yet Effective Unsupervised Framework for Graph-Text Mutual Conversion](https://mlsanity.com/paper/2209.10754/) (2022)
- [Do Models Learn the Directionality of Relations? A New Evaluation: Relation Direction Recognition](https://mlsanity.com/paper/2105.09045/) (2021)
- [A Neural Edge-Editing Approach for Document-Level Relation Graph Extraction](https://mlsanity.com/paper/2106.09900/) (2021)
- [Modeling Multi-Granularity Hierarchical Features for Relation Extraction](https://mlsanity.com/paper/2204.04437/) (2022)
- [Modeling Instance Interactions for Joint Information Extraction with Neural High-Order Conditional Random Field](https://mlsanity.com/paper/2212.08929/) (2022)
- [Using Scene and Semantic Features for Multi-modal Emotion Recognition](https://mlsanity.com/paper/2308.00228/) (2023)
- [Multiple Relations Classification using Imbalanced Predictions Adaptation](https://mlsanity.com/paper/2309.13718/) (2023)
- [Context-Dependent Semantic Parsing for Temporal Relation Extraction](https://mlsanity.com/paper/2112.00894/) (2021)
- [Temporal Relation Extraction with a Graph-Based Deep Biaffine Attention Model](https://mlsanity.com/paper/2201.06125/) (2022)
- [Evaluating BERT-based Scientific Relation Classifiers for Scholarly Knowledge Graph Construction on Digital Library Collections](https://mlsanity.com/paper/2305.02291/) (2023)

## Abstract (arXiv)

Most approaches to extraction multiple relations from a paragraph require multiple passes over the paragraph. In practice, multiple passes are computationally expensive and this makes difficult to scale to longer paragraphs and larger text corpora. In this work, we focus on the task of multiple relation extraction by encoding the paragraph only once (one-pass). We build our solution on the pre-trained self-attentive (Transformer) models, where we first add a structured prediction layer to handle extraction between multiple entity pairs, then enhance the paragraph embedding to capture multiple relational information associated with each entity with an entity-aware attention technique. We show that our approach is not only scalable but can also perform state-of-the-art on the standard benchmark ACE 2005.