---
title: "Neural Coreference Resolution with Deep — Zhang 2018"
description: "This short paper extends the end-to-end neural coreference resolver of Lee et al."
authors: ["Rui Zhang", "Cícero Nogueira dos Santos", "Michihiro Yasunaga", "Bing Xiang", "Dragomir Radev"]
year: 2018
venue: "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)"
source: "https://aclanthology.org/P18-2017/"
doi: "10.18653/v1/p18-2017"
arxiv: "1805.04893"
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/papers/zhang-2018-neural-coreference-resolution-deep-biaffine/"
---
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# Neural Coreference Resolution with Deep Biaffine Attention by Joint Mention Detection and Mention Clustering

**Authors:** [Rui Zhang](https://md.research.elamur.ai/free/authors/rui-zhang/), [Cícero Nogueira dos Santos](https://md.research.elamur.ai/free/authors/cicero-nogueira-dos-santos/), [Michihiro Yasunaga](https://md.research.elamur.ai/free/authors/michihiro-yasunaga/), [Bing Xiang](https://md.research.elamur.ai/free/authors/bing-xiang/), [Dragomir Radev](https://md.research.elamur.ai/free/authors/dragomir-radev/) · **Year:** 2018 · **Venue:** Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/P18-2017/) · [PDF](https://aclanthology.org/P18-2017.pdf) · [DOI 10.18653/v1/p18-2017](https://doi.org/10.18653/v1/p18-2017) · [arXiv 1805.04893](https://arxiv.org/abs/1805.04893) · [OpenAlex](https://openalex.org/W2963003781)

**Citations:** 58 citations (openalex-snapshot · 2026-10-14) · [25](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
This short paper extends the end-to-end neural coreference resolver of Lee et al. (2017). That system treats every text span as a candidate mention and learns to choose an antecedent for each one. The authors identify two weaknesses. First, antecedent scores come from plain feed-forward networks. Second, training uses only the marginal likelihood of gold clusters, so mention detection gets only indirect supervision.

They propose two changes:
- **Biaffine attention scorer** (after Dozat and Manning, 2017). It separately models how compatible two spans are and how likely a span is to have any antecedent at all.
- **Joint training objective.** An explicit binary mention-detection loss is added to the clustering log-likelihood, so the model learns directly which spans are real entity mentions.

Span representations are the same as in the baseline: BiLSTMs plus head-finding attention.

When evaluated on the English test data from the CoNLL-2012 Shared Task (drawn from OntoNotes), the model achieves a new state of the art. Most of the gain comes from higher precision. The model also finds many correct mentions that never appear in the training data. This suggests it generalizes rather than memorizes, which matters for adapting coreference systems to new domains.

## Key points
- **Data and metrics:** CoNLL-2012 English contains 2,802 training, 343 development and 348 test documents. Evaluation uses MUC, B³ and CEAFφ4, with their average F1 serving as the CoNLL score.
- **Main results:** The single model reaches 67.8 average F1, against 67.2 for Lee et al. (2017). A 5-model ensemble reaches 69.2, against 68.8. The gains are significant at p \< 0.05 under a paired bootstrap test.
- **Ablation (dev set):** Removing the detection loss lowers the score to 67.5. Removing biaffine attention lowers it to 67.4. The baseline scores 67.3.
- **Setup:**
  - Hyperparameters follow Lee et al. (2017).
  - Spans are at most 10 words, with up to 250 antecedents considered per span.
  - Embeddings are fixed GloVe (300-d) concatenated with Turian (50-d).
  - The detection-loss weight is λ_detect = 0.1.
  - Training converges in about 200K updates, faster than the baseline.
- **Mention detection:** The model correctly detects 1,048 mentions that the baseline misses. Of these, 662 do not occur in the training data. The advantage is largest for spans of 5 or more words.

*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: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/)

Topics: [Coreference resolution](https://md.research.elamur.ai/free/topics/coreference-resolution/) · [Dependency parsing](https://md.research.elamur.ai/free/topics/dependency-parsing/)

## Cited by

Cited by

25 papers of our library cite it (of the 140673 whose bibliographies we read); 11 of them have a page here, 14 not yet.

- [Tackling Zero Pronoun Resolution and Non-Zero Coreference Resolution Jointly](https://md.research.elamur.ai/free/papers/chen-2021-tackling-zero-pronoun-resolution-non/)
- [ChEMU-Ref: A Corpus for Modeling Anaphora Resolution in the Chemical Domain](https://md.research.elamur.ai/free/papers/fang-2021-chemu-ref-corpus-modeling-anaphora/)
- [What does it take to bake a cake? The RecipeRef corpus and anaphora resolution in procedural text](https://md.research.elamur.ai/free/papers/fang-2022-what-does-it-take-bake/)
- [End-to-end Deep Reinforcement Learning Based Coreference Resolution](https://md.research.elamur.ai/free/papers/fei-2019-end-end-deep-reinforcement-learning/)
- [Conundrums in Entity Coreference Resolution: Making Sense of the State of the Art](https://md.research.elamur.ai/free/papers/lu-2020-conundrums-entity-coreference-resolution-making/)
- [Maverick: Efficient and Accurate Coreference Resolution Defying Recent Trends](https://md.research.elamur.ai/free/papers/martinelli-2024-maverick-efficient-accurate-coreference-resolution/)
- [On Generalization in Coreference Resolution](https://md.research.elamur.ai/free/papers/toshniwal-2021-generalization-coreference-resolution/)
- [CorefQA: Coreference Resolution as Query-based Span Prediction](https://md.research.elamur.ai/free/papers/wu-2020-corefqa-coreference-resolution-as-query/)
- [Understanding Mention Detector-Linker Interaction in Neural Coreference Resolution](https://md.research.elamur.ai/free/papers/wu-2021-understanding-mention-detector-linker-interaction/)
- [A Cluster Ranking Model for Full Anaphora Resolution](https://md.research.elamur.ai/free/papers/yu-2020-cluster-ranking-model-full-anaphora/)
- [Opinion Piece: Can we Fix the Scope for Coreference? Problems and Solutions for Benchmarks beyond OntoNotes](https://md.research.elamur.ai/free/papers/zeldes-2022-opinion-piece-can-we-fix/)

## Cite

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

```bibtex
@inproceedings{zhang2018neural,
  title = {{Neural Coreference Resolution with Deep Biaffine Attention by Joint Mention Detection and Mention Clustering}},
  author = {Zhang, Rui and Santos, Cícero Nogueira dos and Yasunaga, Michihiro and Xiang, Bing and Radev, Dragomir},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  year = {2018},
  doi = {10.18653/v1/p18-2017},
  eprint = {1805.04893},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/p18-2017}
}
```

- [← previous Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph](https://md.research.elamur.ai/free/papers/zadeh-2018-multimodal-language-analysis-wild-cmu/)
- [next → Neural Cross-Lingual Coreference Resolution And Its Application To Entity Linking](https://md.research.elamur.ai/free/papers/kundu-2018-neural-cross-lingual-coreference-resolution/)
- [↑ back to papers of 2018](https://md.research.elamur.ai/free/papers/2018/page/2/)

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