---
title: "Investigating Multilingual Coreference — Chai 2023"
description: "The paper asks whether the harmonized annotations in CorefUD can reveal properties of coreference shared across languages, and whether such properties…"
authors: ["Haixia Chai", "Michael Strube"]
year: 2023
venue: "Findings of the Association for Computational Linguistics: EMNLP 2023"
source: "https://aclanthology.org/2023.findings-emnlp.671/"
doi: "10.18653/v1/2023.findings-emnlp.671"
arxiv: "2310.17734"
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/chai-2023-investigating-multilingual-coreference-resolution-universal/"
---
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# Investigating Multilingual Coreference Resolution by Universal Annotations

**Authors:** [Haixia Chai](https://md.research.elamur.ai/free/authors/haixia-chai/), [Michael Strube](https://md.research.elamur.ai/free/authors/michael-strube/) · **Year:** 2023 · **Venue:** Findings of the Association for Computational Linguistics: EMNLP 2023 · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2023.findings-emnlp.671/) · [PDF](https://aclanthology.org/2023.findings-emnlp.671.pdf) · [DOI 10.18653/v1/2023.findings-emnlp.671](https://doi.org/10.18653/v1/2023.findings-emnlp.671) · [arXiv 2310.17734](https://arxiv.org/abs/2310.17734) · [OpenAlex](https://openalex.org/W4389520289)

**Citations:** 1 citations (openalex-snapshot · 2026-10-14) · [5](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The paper asks whether the harmonized annotations in CorefUD can reveal properties of coreference shared across languages, and whether such properties help a multilingual coreference resolution (MCR) system. The authors first study the gold training data of CorefUD 1.1 (17 datasets, 12 languages) at three levels: mentions, entities and documents. They also compare genres, using UPOS tags and UD relations to describe mentions and their antecedents. They then analyse the hardest cases left unresolved by the best CRAC 2022 shared task system.

Guided by these findings, they derive features from the universal morphosyntactic annotations and add them to the joint-training baseline of Pražák et al. (2021). The best feature set (ours_wf) gains 0.9 CoNLL F1 on average, while a lemma-based variant (ours_lem) underperforms the baseline.

The work matters because it is presented as the first use of universal annotations for MCR. It shows that cross-lingual regularities exist, though phenomena such as definiteness remain hard to treat uniformly across languages.

## Key points
- **Shared patterns:**
  - Across languages, the closest antecedent of an overt pronoun is mostly in subject or object position.
  - Hungarian, Lithuanian and Turkish have the most pre-modified mentions, at 51–52%.
- **Mention types by dataset:**
  - EN_PARCORFULL, DE_PARCORFULL and FR_DEMOCRAT contain the most overt pronouns, about 46% of mentions.
  - In the Czech datasets, zero pronouns outnumber overt ones.
- **Main common error:** In all languages, systems struggle most with two-mention entities, particularly in detecting their nominal noun mentions.
- **Experimental setup:**
  - Experiments run on CorefUD 1.0, with roughly 10% of the training data held out as a test set.
  - Results are reported as macro-averaged CoNLL F1 (MUC, B3, CEAFe).
  - LT_LCC and RU_RUCOR are the hardest datasets.
- **Error rates and availability:**
  - ours_wf leaves fewer gold entities with zero recall than the baseline (39.19% vs 39.77%).
  - ours_wf also misses fewer mentions than the baseline.
  - Code and model are released at github.com/HaixiaChai/multi-coref.

*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

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

- [Challenges to Evaluating the Generalization of Coreference Resolution Models: A Measurement Modeling Perspective](https://md.research.elamur.ai/free/papers/porada-2024-challenges-evaluating-generalization-coreference-resolution/)

## Cite

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

```bibtex
@inproceedings{chai2023investigating,
  title = {{Investigating Multilingual Coreference Resolution by Universal Annotations}},
  author = {Chai, Haixia and Strube, Michael},
  booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2023},
  year = {2023},
  doi = {10.18653/v1/2023.findings-emnlp.671},
  eprint = {2310.17734},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2023.findings-emnlp.671}
}
```

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57 references, 33 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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