# Findings of the Shared Task on Multilingual Coreference Resolution

arXiv: https://arxiv.org/abs/2209.07841 · HTML: https://mlsanity.com/paper/2209.07841/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 48 references that are in the map.
- First posted on arXiv 16 Sep 2022.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Rethinking embedding coupling in pre-trained language models](https://mlsanity.com/paper/2010.12821/) (2020)
- [Neural Architectures for Nested NER through Linearization](https://mlsanity.com/paper/1908.06926/) (2019)
- [Coreference Resolution without Span Representations](https://mlsanity.com/paper/2101.00434/) (2021)
- [Multilingual Coreference Resolution with Harmonized Annotations](https://mlsanity.com/paper/2107.12088/) (2021)
- [Prague Dependency Treebank -- Consolidated 1.0](https://mlsanity.com/paper/2006.03679/) (2020)
- ['UFAL CorPipe at CRAC 2022: Effectivity of Multilingual Models for Coreference Resolution](https://mlsanity.com/paper/2209.07278/) (2022)

## Built on by (most cited first, 13 of 13)

- [Findings of the Third Shared Task on Multilingual Coreference Resolution](https://mlsanity.com/paper/2410.15949/) (2024)
- [mOKB6: A Multilingual Open Knowledge Base Completion Benchmark](https://mlsanity.com/paper/2211.06959/) (2022)
- [Challenges to Evaluating the Generalization of Coreference Resolution Models: A Measurement Modeling Perspective](https://mlsanity.com/paper/2303.09092/) (2023)
- [CorPipe at CRAC 2024: Predicting Zero Mentions from Raw Text](https://mlsanity.com/paper/2410.02756/) (2024)
- [Findings of the Fourth Shared Task on Multilingual Coreference Resolution: Can LLMs Dethrone Traditional Approaches?](https://mlsanity.com/paper/2509.17796/) (2025)
- [End-to-end Multilingual Coreference Resolution with Mention Head Prediction](https://mlsanity.com/paper/2209.12516/) (2022)
- [A Controlled Reevaluation of Coreference Resolution Models](https://mlsanity.com/paper/2404.00727/) (2024)
- [Exploring Multiple Strategies to Improve Multilingual Coreference Resolution in CorefUD](https://mlsanity.com/paper/2408.16893/) (2024)
- [CorPipe at CRAC 2025: Evaluating Multilingual Encoders for Multilingual Coreference Resolution](https://mlsanity.com/paper/2509.17858/) (2025)
- [Findings of the Fifth Shared Task on Multilingual Coreference Resolution: Expanding Datasets for Long-Range Entities](https://mlsanity.com/paper/2605.21369/) (2026)
- [Towards Generating Automatic Anaphora Annotations](https://mlsanity.com/paper/2503.09417/) (2025)
- [Closing the Gap at CRAC 2026: Two-Stage Adaptation for LLM-Based Multilingual Coreference Resolution](https://mlsanity.com/paper/2605.16984/) (2026)
- [CorPipe at CRAC 2026: Empty Nodes and Cross-Lingual Transfer in Multilingual Coreference Resolution](https://mlsanity.com/paper/2605.30133/) (2026)

## Abstract (arXiv)

This paper presents an overview of the shared task on multilingual coreference resolution associated with the CRAC 2022 workshop. Shared task participants were supposed to develop trainable systems capable of identifying mentions and clustering them according to identity coreference. The public edition of CorefUD 1.0, which contains 13 datasets for 10 languages, was used as the source of training and evaluation data. The CoNLL score used in previous coreference-oriented shared tasks was used as the main evaluation metric. There were 8 coreference prediction systems submitted by 5 participating teams; in addition, there was a competitive Transformer-based baseline system provided by the organizers at the beginning of the shared task. The winner system outperformed the baseline by 12 percentage points (in terms of the CoNLL scores averaged across all datasets for individual languages).