# HIN: Hierarchical Inference Network for Document-Level Relation Extraction

arXiv: https://arxiv.org/abs/2003.12754 · HTML: https://mlsanity.com/paper/2003.12754/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2020.

## Built on by (most cited first, 30 of 42)

- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [Document-level Relation Extraction as Semantic Segmentation](https://mlsanity.com/paper/2106.03618/) (2021)
- [Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction](https://mlsanity.com/paper/2102.10249/) (2021)
- [Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation](https://mlsanity.com/paper/2203.10900/) (2022)
- [An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning](https://mlsanity.com/paper/2102.05980/) (2021)
- [SIRE: Separate Intra- and Inter-sentential Reasoning for Document-level Relation Extraction](https://mlsanity.com/paper/2106.01709/) (2021)
- [Eider: Empowering Document-level Relation Extraction with Efficient Evidence Extraction and Inference-stage Fusion](https://mlsanity.com/paper/2106.08657/) (2021)
- [Graph Based Network with Contextualized Representations of Turns in Dialogue](https://mlsanity.com/paper/2109.04008/) (2021)
- [SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction](https://mlsanity.com/paper/2109.12093/) (2021)
- [DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population](https://mlsanity.com/paper/2201.03335/) (2022)
- [Learning Logic Rules for Document-level Relation Extraction](https://mlsanity.com/paper/2111.05407/) (2021)
- [Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey](https://mlsanity.com/paper/2103.16929/) (2021)
- [Can Transformers Capture Spatial Relations between Objects?](https://mlsanity.com/paper/2403.00729/) (2024)
- [Document-level Relation Extraction with Cross-sentence Reasoning Graph](https://mlsanity.com/paper/2303.03912/) (2023)
- [None Class Ranking Loss for Document-Level Relation Extraction](https://mlsanity.com/paper/2205.00476/) (2022)
- [BioREx: Improving Biomedical Relation Extraction by Leveraging Heterogeneous Datasets](https://mlsanity.com/paper/2306.11189/) (2023)
- [LLM with Relation Classifier for Document-Level Relation Extraction](https://mlsanity.com/paper/2408.13889/) (2024)
- [Towards Better Document-level Relation Extraction via Iterative Inference](https://mlsanity.com/paper/2211.14470/) (2022)
- [FCDS: Fusing Constituency and Dependency Syntax into Document-Level Relation Extraction](https://mlsanity.com/paper/2403.01886/) (2024)
- [Document-level Relation Extraction with Context Guided Mention Integration and Inter-pair Reasoning](https://mlsanity.com/paper/2201.04826/) (2022)
- [Not Just Plain Text! Fuel Document-Level Relation Extraction with Explicit Syntax Refinement and Subsentence Modeling](https://mlsanity.com/paper/2211.05343/) (2022)
- [A Comprehensive Survey of Document-level Relation Extraction (2016-2023)](https://mlsanity.com/paper/2309.16396/) (2023)
- [PromptRE: Weakly-Supervised Document-Level Relation Extraction via Prompting-Based Data Programming](https://mlsanity.com/paper/2310.09265/) (2023)
- [An Overview of Distant Supervision for Relation Extraction with a Focus on Denoising and Pre-training Methods](https://mlsanity.com/paper/2207.08286/) (2022)
- [Enhancing Document-level Relation Extraction by Entity Knowledge Injection](https://mlsanity.com/paper/2207.11433/) (2022)
- [A Survey of Document-Level Information Extraction](https://mlsanity.com/paper/2309.13249/) (2023)
- [TTM-RE: Memory-Augmented Document-Level Relation Extraction](https://mlsanity.com/paper/2406.05906/) (2024)
- [Consistent Document-Level Relation Extraction via Counterfactuals](https://mlsanity.com/paper/2407.06699/) (2024)
- [Mention-centered Graph Neural Network for Document-level Relation Extraction](https://mlsanity.com/paper/2103.08200/) (2021)
- [A Neural Edge-Editing Approach for Document-Level Relation Graph Extraction](https://mlsanity.com/paper/2106.09900/) (2021)

## Abstract (arXiv)

Document-level RE requires reading, inferring and aggregating over multiple sentences. From our point of view, it is necessary for document-level RE to take advantage of multi-granularity inference information: entity level, sentence level and document level. Thus, how to obtain and aggregate the inference information with different granularity is challenging for document-level RE, which has not been considered by previous work. In this paper, we propose a Hierarchical Inference Network (HIN) to make full use of the abundant information from entity level, sentence level and document level. Translation constraint and bilinear transformation are applied to target entity pair in multiple subspaces to get entity-level inference information. Next, we model the inference between entity-level information and sentence representation to achieve sentence-level inference information. Finally, a hierarchical aggregation approach is adopted to obtain the document-level inference information. In this way, our model can effectively aggregate inference information from these three different granularities. Experimental results show that our method achieves state-of-the-art performance on the large-scale DocRED dataset. We also demonstrate that using BERT representations can further substantially boost the performance.