---
title: "Reasoning with Latent Structure Refinement for — Nan 2020"
description: "This ACL 2020 paper addresses document-level relation extraction, where a relation between two entities often can only be inferred by chaining evidence…"
authors: ["Guoshun Nan", "Zhijiang Guo", "Ivan Sekulic", "Wei Lu"]
year: 2020
venue: "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics"
source: "https://aclanthology.org/2020.acl-main.141/"
doi: "10.18653/v1/2020.acl-main.141"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/nan-2020-reasoning-latent-structure-refinement-document/"
---
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# Reasoning with Latent Structure Refinement for Document-Level Relation Extraction

**Authors:** Guoshun Nan, [Zhijiang Guo](https://md.research.elamur.ai/free/en/authors/zhijiang-guo/), Ivan Sekulic, [Wei Lu](https://md.research.elamur.ai/free/en/authors/wei-lu/) · **Year:** 2020 · **Venue:** Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2020.acl-main.141/) · [PDF](https://aclanthology.org/2020.acl-main.141.pdf) · [DOI 10.18653/v1/2020.acl-main.141](https://doi.org/10.18653/v1/2020.acl-main.141) · [OpenAlex](https://openalex.org/W3035053871)

**Citations:** 305 citations (openalex-snapshot · 2026-10-14) · [74](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
This ACL 2020 paper addresses document-level relation extraction, where a relation between two entities often can only be inferred by chaining evidence across several sentences. Earlier systems built fixed document graphs from syntactic trees, co-reference links or hand-crafted rules, and such graphs can miss useful long-range connections. The authors propose Latent Structure Refinement (LSR), which learns the document graph as a latent variable during end-to-end training. A node constructor encodes sentences with a BiLSTM or BERT and creates mention, entity and meta dependency path (MDP) nodes. A dynamic reasoner then induces a weighted graph using structured attention and a variant of the Matrix-Tree Theorem, and propagates information over it with densely connected GCNs. This step is repeated across N stacked blocks, so the graph is refined iteratively to support multi-hop reasoning. LSR reaches 59.05 F1 on DocRED and sets new state-of-the-art results on the biomedical CDR and GDA datasets. Its analyses suggest it recovers inter-sentence relations more accurately than prior models.

## Key points
- **Architecture:** LSR has three parts: a node constructor, a dynamic reasoner (structure induction plus multi-hop reasoning) and a classifier.
  - Entity nodes are averages of their mention nodes.
  - MDP nodes are tokens lying on shortest dependency paths between mentions in a sentence.
- **Results:** LSR scores 59.05 F1 on DocRED and achieves new state-of-the-art results on CDR and GDA.
- **Ablation:** Removing the structure induction module costs 3.26 F1 on DocRED, the largest drop of any component.
- **Case study:** For the pair ⟨Japan, World War II⟩, attention on non-local mentions grows in the second refinement step, letting the model infer "participant of" (P1344).
  - The attention scores of AGGCN are much flatter by comparison.
  - LSR still wrongly links New Ireland to World War II.
- **Resources:** The code and pretrained model are public at github.com/nanguoshun/LSR (PyTorch). Figure 1 uses a Ukrainian-politics example from DocRED (Lutsenko, Yulia Tymoshenko).

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

74 papers of our library cite it (of the 140884 whose bibliographies we read); 2 of them have a page here, 72 not yet.

- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://md.research.elamur.ai/free/en/papers/pan-2023-unifying-large-language-models-knowledge/)
- [Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders](https://md.research.elamur.ai/free/en/papers/wang-2020-two-are-better-than-one/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{nan2020reasoning,
  title = {{Reasoning with Latent Structure Refinement for Document-Level Relation Extraction}},
  author = {Nan, Guoshun and Guo, Zhijiang and Sekulic, Ivan and Lu, Wei},
  booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
  year = {2020},
  doi = {10.18653/v1/2020.acl-main.141},
  url = {https://doi.org/10.18653/v1/2020.acl-main.141}
}
```

- [← previous Reasoning Requirements for Indirect Speech Act Interpretation](https://md.research.elamur.ai/free/en/papers/sarathy-2020-reasoning-requirements-indirect-speech-act/)
- [next → Reasoning with Multimodal Sarcastic Tweets via Modeling Cross-Modality Contrast and Semantic Association](https://md.research.elamur.ai/free/en/papers/xu-2020-reasoning-multimodal-sarcastic-tweets-modeling/)
- [↑ back to papers of 2020](https://md.research.elamur.ai/free/en/papers/2020/page/5/)

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