# Eider: Empowering Document-level Relation Extraction with Efficient Evidence Extraction and Inference-stage Fusion

arXiv: https://arxiv.org/abs/2106.08657 · HTML: https://mlsanity.com/paper/2106.08657/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 36 references that are in the map.
- First posted on arXiv 16 Jun 2021.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [DocRED: A Large-Scale Document-Level Relation Extraction Dataset](https://mlsanity.com/paper/1906.06127/) (2019)
- [Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling](https://mlsanity.com/paper/2010.11304/) (2020)
- [Cross-Sentence N-ary Relation Extraction with Graph LSTMs](https://mlsanity.com/paper/1708.03743/) (2017)
- [Reasoning with Latent Structure Refinement for Document-Level Relation Extraction](https://mlsanity.com/paper/2005.06312/) (2020)
- [Double Graph Based Reasoning for Document-level Relation Extraction](https://mlsanity.com/paper/2009.13752/) (2020)
- [Coreferential Reasoning Learning for Language Representation](https://mlsanity.com/paper/2004.06870/) (2020)
- [Document-level Relation Extraction as Semantic Segmentation](https://mlsanity.com/paper/2106.03618/) (2021)
- [Distant Supervision for Relation Extraction beyond the Sentence Boundary](https://mlsanity.com/paper/1609.04873/) (2016)
- [Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks](https://mlsanity.com/paper/1903.01306/) (2019)
- [Document-Level N-ary Relation Extraction with Multiscale Representation Learning](https://mlsanity.com/paper/1904.02347/) (2019)
- [Revealing the Myth of Higher-Order Inference in Coreference Resolution](https://mlsanity.com/paper/2009.12013/) (2020)
- [Improved Neural Relation Detection for Knowledge Base Question Answering](https://mlsanity.com/paper/1704.06194/) (2017)
- [Global-to-Local Neural Networks for Document-Level Relation Extraction](https://mlsanity.com/paper/2009.10359/) (2020)
- [Fine-tune Bert for DocRED with Two-step Process](https://mlsanity.com/paper/1909.11898/) (2019)
- [HIN: Hierarchical Inference Network for Document-Level Relation Extraction](https://mlsanity.com/paper/2003.12754/) (2020)
- [Document-Level Relation Extraction with Reconstruction](https://mlsanity.com/paper/2012.11384/) (2020)
- [SIRE: Separate Intra- and Inter-sentential Reasoning for Document-level Relation Extraction](https://mlsanity.com/paper/2106.01709/) (2021)
- [Relation Adversarial Network for Low Resource Knowledge Graph Completion](https://mlsanity.com/paper/1911.03091/) (2019)
- [Three Sentences Are All You Need: Local Path Enhanced Document Relation Extraction](https://mlsanity.com/paper/2106.01793/) (2021)
- [Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction](https://mlsanity.com/paper/1812.11321/) (2018)
- [Discovering Hypernymy in Text-Rich Heterogeneous Information Network by Exploiting Context Granularity](https://mlsanity.com/paper/1909.01584/) (2019)

## Built on by (most cited first, 28 of 28)

- [Structured information extraction from complex scientific text with fine-tuned large language models](https://mlsanity.com/paper/2212.05238/) (2022)
- [DREEAM: Guiding Attention with Evidence for Improving Document-Level Relation Extraction](https://mlsanity.com/paper/2302.08675/) (2023)
- [Multi-hop Evidence Retrieval for Cross-document Relation Extraction](https://mlsanity.com/paper/2212.10786/) (2022)
- [RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction](https://mlsanity.com/paper/2310.15743/) (2023)
- [A Survey on Retrieval And Structuring Augmented Generation with Large Language Models](https://mlsanity.com/paper/2509.10697/) (2025)
- [HistRED: A Historical Document-Level Relation Extraction Dataset](https://mlsanity.com/paper/2307.04285/) (2023)
- [FCDS: Fusing Constituency and Dependency Syntax into Document-Level Relation Extraction](https://mlsanity.com/paper/2403.01886/) (2024)
- [Not Just Plain Text! Fuel Document-Level Relation Extraction with Explicit Syntax Refinement and Subsentence Modeling](https://mlsanity.com/paper/2211.05343/) (2022)
- [Towards Integration of Discriminability and Robustness for Document-Level Relation Extraction](https://mlsanity.com/paper/2304.00824/) (2023)
- [Rethinking Document-Level Relation Extraction: A Reality Check](https://mlsanity.com/paper/2306.08953/) (2023)
- [A Comprehensive Survey of Document-level Relation Extraction (2016-2023)](https://mlsanity.com/paper/2309.16396/) (2023)
- [Did the Models Understand Documents? Benchmarking Models for Language Understanding in Document-Level Relation Extraction](https://mlsanity.com/paper/2306.11386/) (2023)
- [A Survey of Document-Level Information Extraction](https://mlsanity.com/paper/2309.13249/) (2023)
- [PRiSM: Enhancing Low-Resource Document-Level Relation Extraction with Relation-Aware Score Calibration](https://mlsanity.com/paper/2309.13869/) (2023)
- [Anaphor Assisted Document-Level Relation Extraction](https://mlsanity.com/paper/2310.18604/) (2023)
- [Document-level Relation Extraction with Relation Correlations](https://mlsanity.com/paper/2212.10171/) (2022)
- [Recall Them All: Retrieval-Augmented Language Models for Long Object List Extraction from Long Documents](https://mlsanity.com/paper/2405.02732/) (2024)
- [On the Robustness of Document-Level Relation Extraction Models to Entity Name Variations](https://mlsanity.com/paper/2406.07444/) (2024)
- [GEGA: Graph Convolutional Networks and Evidence Retrieval Guided Attention for Enhanced Document-level Relation Extraction](https://mlsanity.com/paper/2407.21384/) (2024)
- [U3E: Unsupervised and Erasure-based Evidence Extraction for Machine Reading Comprehension](https://mlsanity.com/paper/2210.02621/) (2022)
- [Do the Benefits of Joint Models for Relation Extraction Extend to Document-level Tasks?](https://mlsanity.com/paper/2310.00696/) (2023)
- [A Two Dimensional Feature Engineering Method for Relation Extraction](https://mlsanity.com/paper/2404.04959/) (2024)
- [Building a Japanese Document-Level Relation Extraction Dataset Assisted by Cross-Lingual Transfer](https://mlsanity.com/paper/2404.16506/) (2024)
- [CDER: Collaborative Evidence Retrieval for Document-level Relation Extraction](https://mlsanity.com/paper/2504.06529/) (2025)
- [Multi-Relation Extraction in Entity Pairs using Global Context](https://mlsanity.com/paper/2507.22926/) (2025)
- [Relation as a Prior: A Novel Paradigm for LLM-based Document-level Relation Extraction](https://mlsanity.com/paper/2511.08143/) (2025)
- [DOREMI: Optimizing Long Tail Predictions in Document-Level Relation Extraction](https://mlsanity.com/paper/2601.11190/) (2026)
- [SA-RSQ: A Versatile Sparse Representation Framework for Multi-modal Recommender Systems](https://mlsanity.com/paper/2608.22979/) (2026)

## Abstract (arXiv)

Document-level relation extraction (DocRE) aims to extract semantic relations among entity pairs in a document. Typical DocRE methods blindly take the full document as input, while a subset of the sentences in the document, noted as the evidence, are often sufficient for humans to predict the relation of an entity pair. In this paper, we propose an evidence-enhanced framework, Eider, that empowers DocRE by efficiently extracting evidence and effectively fusing the extracted evidence in inference. We first jointly train an RE model with a lightweight evidence extraction model, which is efficient in both memory and runtime. Empirically, even training the evidence model on silver labels constructed by our heuristic rules can lead to better RE performance. We further design a simple yet effective inference process that makes RE predictions on both extracted evidence and the full document, then fuses the predictions through a blending layer. This allows Eider to focus on important sentences while still having access to the complete information in the document. Extensive experiments show that Eider outperforms state-of-the-art methods on three benchmark datasets (e.g., by 1.37/1.26 Ign F1/F1 on DocRED).