---
title: "Multilingual coreference resolution as text — Skachkova 2024"
description: "The paper tests whether multilingual coreference resolution can be handled as plain text generation with a fairly small model instead of a very large LLM."
authors: ["Natalia Skachkova"]
year: 2024
venue: "Proceedings of the Seventh Workshop on Computational Models of Reference, Anaphora and Coreference"
source: "https://aclanthology.org/2024.crac-1.11/"
doi: "10.18653/v1/2024.crac-1.11"
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/skachkova-2024-multilingual-coreference-resolution-as-text/"
---
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# Multilingual coreference resolution as text generation

**Authors:** [Natalia Skachkova](https://md.research.elamur.ai/free/authors/natalia-skachkova/) · **Year:** 2024 · **Venue:** Proceedings of the Seventh Workshop on Computational Models of Reference, Anaphora and Coreference · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2024.crac-1.11/) · [PDF](https://aclanthology.org/2024.crac-1.11.pdf) · [DOI 10.18653/v1/2024.crac-1.11](https://doi.org/10.18653/v1/2024.crac-1.11) · [OpenAlex](https://openalex.org/W4404784120)

**Citations:** 2 citations (openalex-snapshot · 2026-10-14) · [4](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The paper tests whether multilingual coreference resolution can be handled as plain text generation with a fairly small model instead of a very large LLM. The system, DFKI-CorefGen, was submitted to the CRAC Shared Task 2024. It takes raw text and rewrites it with mentions wrapped in square brackets and tagged with cluster IDs. The backbone is mT5-base, kept frozen, with only a short trainable prefix learned through OpenPrompt. Documents are processed step by step: each new sentence is annotated, and labels on earlier sentences can be revised. To teach the model this revision behavior, the author built training examples by deliberately damaging gold annotations, for instance by dropping, merging, splitting or mixing clusters, shifting mention boundaries, and adding typical generation errors such as duplicated mentions and stray brackets. The approach ranked sixth of seven and scored well below the baseline. The author attributes this mainly to the small model, limited training data and errors that compound across sentences. The work is still a useful negative result for simple generative pipelines, and it identifies concrete failure modes.

## Key points
- DFKI-CorefGen reached an average F1 of 33.38, while the shared-task baseline (Pražák et al., 2021) scored 53.16. It placed sixth among seven systems. On 1,996 isolated dev sentences it scored only 42.59 F1.
- mT5-base (580M parameters) stays frozen, and only a prefix of 100 randomly initialized tokens is trained via prefix tuning in OpenPrompt. Training ran on a single 12 GB GTX TITAN X for 5 epochs, with batch size 1, AdamW and a learning rate of 5e-5.
- One model covers all languages and uses only the official data. The author sampled 2,000 training and 70 dev examples per dataset, giving 42,000 training and 1,470 dev examples. About 87% of training examples carry corrupted annotations, about 12% are correct and 0.4% contain no clusters.
- Inputs are capped at five sentences and 512 sub-tokens. At inference time a five-sentence window moves forward two sentences at a time. Generated output is then aligned back to the source text, and clusters are merged across windows based on overlapping mentions.
- Discontinuous mentions are discarded, and zero-anaphora tokens ("_") are handled like ordinary tokens. Persistent hallucinations and repetitions remain a major problem, and the author suggests moving to a much larger LLM in future work.

*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/)

## Cited by

Cited by

4 papers of our library cite it (of the 140673 whose bibliographies we read); 1 of them have a page here, 3 not yet.

- [CorefInst: Leveraging LLMs for Multilingual Coreference Resolution](https://md.research.elamur.ai/free/papers/arslan-2026-corefinst-leveraging-llms-multilingual-coreference/)

## Cite

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

```bibtex
@inproceedings{skachkova2024multilingual,
  title = {{Multilingual coreference resolution as text generation}},
  author = {Skachkova, Natalia},
  booktitle = {Proceedings of the Seventh Workshop on Computational Models of Reference, Anaphora and Coreference},
  year = {2024},
  doi = {10.18653/v1/2024.crac-1.11},
  url = {https://doi.org/10.18653/v1/2024.crac-1.11}
}
```

- [← previous Multi-modal Stance Detection: New Datasets and Model](https://md.research.elamur.ai/free/papers/liang-2024-multi-modal-stance-detection-new/)
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- [↑ back to papers of 2024](https://md.research.elamur.ai/free/papers/2024/page/3/)

## References

27 references, 14 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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