# Multilingual Coreference Resolution with Harmonized Annotations

arXiv: https://arxiv.org/abs/2107.12088 · HTML: https://mlsanity.com/paper/2107.12088/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 26 Jul 2021.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [End-to-end Neural Coreference Resolution](https://mlsanity.com/paper/1707.07045/) (2017)
- [BERT for Coreference Resolution: Baselines and Analysis](https://mlsanity.com/paper/1908.09091/) (2019)
- [Revealing the Myth of Higher-Order Inference in Coreference Resolution](https://mlsanity.com/paper/2009.12013/) (2020)
- [Anaphora and Coreference Resolution: A Review](https://mlsanity.com/paper/1805.11824/) (2018)
- [Neural Cross-Lingual Coreference Resolution and its Application to Entity Linking](https://mlsanity.com/paper/1806.10201/) (2018)

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

- [Findings of the Shared Task on Multilingual Coreference Resolution](https://mlsanity.com/paper/2209.07841/) (2022)
- ['UFAL CorPipe at CRAC 2022: Effectivity of Multilingual Models for Coreference Resolution](https://mlsanity.com/paper/2209.07278/) (2022)
- [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)
- [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)
- [Ensemble Transfer Learning for Multilingual Coreference Resolution](https://mlsanity.com/paper/2301.09175/) (2023)
- [SMDDH: Singleton Mention detection using Deep Learning in Hindi Text](https://mlsanity.com/paper/2301.09361/) (2023)
- [Investigating Multilingual Coreference Resolution by Universal Annotations](https://mlsanity.com/paper/2310.17734/) (2023)
- [Just Use XML: Revisiting Joint Translation and Label Projection](https://mlsanity.com/paper/2603.12021/) (2026)
- [CorPipe at CRAC 2026: Empty Nodes and Cross-Lingual Transfer in Multilingual Coreference Resolution](https://mlsanity.com/paper/2605.30133/) (2026)

## Abstract (arXiv)

In this paper, we present coreference resolution experiments with a newly created multilingual corpus CorefUD. We focus on the following languages: Czech, Russian, Polish, German, Spanish, and Catalan. In addition to monolingual experiments, we combine the training data in multilingual experiments and train two joined models -- for Slavic languages and for all the languages together. We rely on an end-to-end deep learning model that we slightly adapted for the CorefUD corpus. Our results show that we can profit from harmonized annotations, and using joined models helps significantly for the languages with smaller training data.