---
title: "Simultaneously Self-Attending to All Mentions for Full-Abstract"
description: "The authors propose Bi-affine Relation Attention Networks (BRANs), which encode… 90.9. accuracy. 58 references, cited by 67 papers here."
authors: ["Patrick Verga", "Emma Strubell", "Andrew McCallum"]
year: 2018
venue: "Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)"
source: "https://aclanthology.org/N18-1080/"
doi: "10.18653/v1/n18-1080"
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/verga-2018-simultaneously-self-attending-all-mentions/"
---
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# Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction

**Authors:** [Patrick Verga](https://md.research.elamur.ai/free/en/authors/patrick-verga/), [Emma Strubell](https://md.research.elamur.ai/free/en/authors/emma-strubell/) and [Andrew McCallum](https://md.research.elamur.ai/free/en/authors/andrew-mccallum/) · **Year:** 2018 · **Venue:** [Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)](https://md.research.elamur.ai/free/en/venues/naacl/) ([NAACL 2018](https://md.research.elamur.ai/free/en/venues/naacl/2018/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/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/N18-1080/) · [PDF](https://aclanthology.org/N18-1080.pdf) · [DOI 10.18653/v1/n18-1080](https://doi.org/10.18653/v1/n18-1080) · [OpenAlex](https://openalex.org/W2788914818)

**Citations:** 284 citations (OpenAlex, 2026-10-14) · [67](https://md.research.elamur.ai/free/en/papers/verga-2018-simultaneously-self-attending-all-mentions/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Most relation extraction systems classify one entity pair mention at a time within a single sentence. This misses relations that span sentences, repeats encoding work for every candidate pair, and fits poorly with biological corpora, which are usually annotated at the document level. The authors propose Bi-affine Relation Attention Networks (BRANs), which encode an entire abstract once and score every mention pair at the same time. The encoder is a modified Transformer that adds convolutions to self-attention and runs over byte pair encoding (BPE) sub-word tokens. Each token gets separate head and tail representations, which a bi-affine operator scores against relation embeddings. A LogSumExp pooling step then combines mention scores into entity-pair scores, giving multi-instance learning. The model is also trained jointly on named entity recognition, which helps when mention-level labels are missing. BRAN reaches state of the art on Biocreative V CDR among models that use no external knowledge base, and it beats comparable baselines on Biocreative VI ChemProt. The authors also release a new large dataset (CTD-based) as a benchmark.

## Key points
- About 30% of relations in Biocreative V CDR cross sentence boundaries. The CDR training set has 5,318 candidate mention pairs but only 500 abstracts, so encoding each abstract once saves a lot of computation.
- The architecture has three main parts:
  - Transformer blocks with multi-head attention and a convolution stack that includes a width-5 kernel for local n-gram cues.
  - Head and tail MLPs feeding a bi-affine scorer.
  - LogSumExp pooling over mentions for each relation type.
- The new dataset is roughly ten times the size of the human-annotated biological IE datasets currently available. It is more accurate than distantly supervised alternatives and covers chemical, disease and gene relations. BRAN reaches 47.3 micro F1 on it, with precision 44.8 and recall 50.2.
- BPE outperforms word-level tokenization on CTD in both tasks:
  - relation extraction F1: 47.3 vs 46.7;
  - NER F1: 92.6 vs 90.9.
- Accuracy rises as more distant entity pairs are allowed, which shows the model uses long-range context. Code and data are released at github.com/patverga/bran.

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

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## Cite

Cite as: Patrick Verga, Emma Strubell and Andrew McCallum. 2018. Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers).

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

```bibtex
@inproceedings{verga2018simultaneously,
  title = {{Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction}},
  author = {Patrick Verga, Emma Strubell and Andrew McCallum},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)},
  year = {2018},
  doi = {10.18653/v1/n18-1080},
  url = {https://doi.org/10.18653/v1/n18-1080}
}
```

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*Updated 24 October 2026*