---
title: "OntoGUM: Evaluating Contextualized SOTA — Zhu 2021"
description: "Neural coreference systems score highly on OntoNotes, but there has been little data in the same annotation scheme to test whether they work on other…"
authors: ["Yilun Zhu", "Sameer Pradhan", "Amir Zeldes"]
year: 2021
venue: "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)"
source: "https://aclanthology.org/2021.acl-short.59/"
doi: "10.18653/v1/2021.acl-short.59"
arxiv: "2106.00933"
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/zhu-2021-ontogum-evaluating-contextualized-sota-coreference/"
---
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# OntoGUM: Evaluating Contextualized SOTA Coreference Resolution on 12 More Genres

**Authors:** [Yilun Zhu](https://md.research.elamur.ai/free/en/authors/yilun-zhu/), [Sameer Pradhan](https://md.research.elamur.ai/free/en/authors/sameer-pradhan/), [Amir Zeldes](https://md.research.elamur.ai/free/en/authors/amir-zeldes/) · **Year:** 2021 · **Venue:** Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (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/2021.acl-short.59/) · [PDF](https://aclanthology.org/2021.acl-short.59.pdf) · [DOI 10.18653/v1/2021.acl-short.59](https://doi.org/10.18653/v1/2021.acl-short.59) · [arXiv 2106.00933](https://arxiv.org/abs/2106.00933) · [OpenAlex](https://openalex.org/W3173522113)

**Citations:** 5 citations (openalex-snapshot · 2026-10-14) · [20](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Neural coreference systems score highly on OntoNotes, but there has been little data in the same annotation scheme to test whether they work on other kinds of text. Earlier out-of-domain tests used WikiCoref and GAP, which are small, limited to Wikipedia, and in GAP's case cover only name–pronoun links. The authors built OntoGUM by automatically converting the 12-genre GUM corpus into the OntoNotes scheme. The conversion uses deterministic rules that draw on GUM's gold syntax and other annotation layers. It removes relation and markable types that OntoNotes excludes, such as singletons, bridging, compounds and indefinite anaphors, and it adjusts appositive and verbal spans. An agreement study found the converted data about as consistent as human annotation in OntoNotes. Two systems were trained or developed on OntoNotes and tested on OntoGUM: the rule-based Stanford dcoref and e2e+SpanBERT-large. Both lost substantial accuracy. Performance depended strongly on genre, with pronoun-heavy texts that have gold speaker information scoring best. This suggests contextualized LMs do not remove covert lexical overfitting, and OntoGUM offers a more realistic benchmark.

## Key points
- OntoGUM contains 168 documents (~150K tokens) in 12 genres, with 19,378 mentions and 4,471 chains. It is the largest open gold dataset that follows OntoNotes, and the data and code are on GitHub (yilunzhu/ontogum).
- Conversion quality was checked on 3 documents (2,500 tokens, 371 mentions):
  - span detection scored ~96 and the CoNLL score was ~92;
  - only 8 conversion errors were found, plus 2 inherited from GUM.
- e2e+SpanBERT dropped about 15 CoNLL points (19%) on OntoGUM, reaching an average of 64.6. dcoref dropped by more than 18 points (30%).
- Spoken and pronoun-rich genres with gold speakers (vl, it, cn, sp, fc) scored highest. Even the SOTA model struggled with nw, wh and tx, so recent Wikinews articles were hard for it despite the newswire content in OntoNotes.
- Results use the official CoNLL-2012 scorer, which averages MUC, B3 and CEAFφ4 F1.

*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/en/shelves/linguistics/)

Topics: [Coreference resolution](https://md.research.elamur.ai/free/en/topics/coreference-resolution/)

## Cited by

Cited by

20 papers of our library cite it (of the 140673 whose bibliographies we read); 11 of them have a page here, 9 not yet.

- [GUMBridge: A Corpus for Varieties of Bridging Anaphora](https://md.research.elamur.ai/free/en/papers/levine-2026-gumbridge-corpus-varieties-bridging-anaphora/)
- [GUMsley: Evaluating Entity Salience in Summarization for 12 English Genres](https://md.research.elamur.ai/free/en/papers/lin-2024-gumsley-evaluating-entity-salience-summarization/)
- [GUM-SAGE: A Novel Dataset and Approach for Graded Entity Salience Prediction](https://md.research.elamur.ai/free/en/papers/lin-2025-gum-sage-novel-dataset-approach/)
- [Why Can't Discourse Parsing Generalize? A Thorough Investigation of the Impact of Data Diversity](https://md.research.elamur.ai/free/en/papers/liu-2023-why-can-t-discourse-parsing/)
- [Major Entity Identification: A Generalizable Alternative to Coreference Resolution](https://md.research.elamur.ai/free/en/papers/manikantan-2024-major-entity-identification-generalizable-alternative/)
- [Challenges to Evaluating the Generalization of Coreference Resolution Models: A Measurement Modeling Perspective](https://md.research.elamur.ai/free/en/papers/porada-2024-challenges-evaluating-generalization-coreference-resolution/)
- [A Controlled Reevaluation of Coreference Resolution Models](https://md.research.elamur.ai/free/en/papers/porada-2024-controlled-reevaluation-coreference-resolution-models/)
- [On Generalization in Coreference Resolution](https://md.research.elamur.ai/free/en/papers/toshniwal-2021-generalization-coreference-resolution/)
- [GENTLE: A Genre-Diverse Multilayer Challenge Set for English NLP and Linguistic Evaluation](https://md.research.elamur.ai/free/en/papers/treebanks-2023-aoyama-gentle-genre-diverse-multilayer-challenge/)
- [The CODI-CRAC 2022 Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue](https://md.research.elamur.ai/free/en/papers/yu-2022-codi-crac-2022-shared-task/)
- [Anatomy of OntoGUM—Adapting GUM to the OntoNotes Scheme to Evaluate Robustness of SOTA Coreference Algorithms](https://md.research.elamur.ai/free/en/papers/zhu-2021-anatomy-ontogum-adapting-gum-ontonotes/)

## Cite

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

```bibtex
@inproceedings{zhu2021ontogum,
  title = {{OntoGUM: Evaluating Contextualized SOTA Coreference Resolution on 12 More Genres}},
  author = {Zhu, Yilun and Pradhan, Sameer and Zeldes, Amir},
  booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  year = {2021},
  doi = {10.18653/v1/2021.acl-short.59},
  eprint = {2106.00933},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2021.acl-short.59}
}
```

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## References

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