# Exploring Multiple Strategies to Improve Multilingual Coreference Resolution in CorefUD

arXiv: https://arxiv.org/abs/2408.16893 · HTML: https://mlsanity.com/paper/2408.16893/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 37 references that are in the map.
- First posted on arXiv 29 Aug 2024.

## Builds on (most cited first, 23 of 23)

- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019)
- [End-to-end Neural Coreference Resolution](https://mlsanity.com/paper/1707.07045/) (2017)
- [Higher-order Coreference Resolution with Coarse-to-fine Inference](https://mlsanity.com/paper/1804.05392/) (2018)
- [German's Next Language Model](https://mlsanity.com/paper/2010.10906/) (2020)
- [BERT for Coreference Resolution: Baselines and Analysis](https://mlsanity.com/paper/1908.09091/) (2019)
- [Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language](https://mlsanity.com/paper/1905.07213/) (2019)
- [HerBERT: Efficiently Pretrained Transformer-based Language Model for Polish](https://mlsanity.com/paper/2105.01735/) (2021)
- [Revealing the Myth of Higher-Order Inference in Coreference Resolution](https://mlsanity.com/paper/2009.12013/) (2020)
- [Word-Level Coreference Resolution](https://mlsanity.com/paper/2109.04127/) (2021)
- [MarIA: Spanish Language Models](https://mlsanity.com/paper/2107.07253/) (2021)
- [Anaphora and Coreference Resolution: A Review](https://mlsanity.com/paper/1805.11824/) (2018)
- [Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan](https://mlsanity.com/paper/2107.07903/) (2021)
- [NorBench -- A Benchmark for Norwegian Language Models](https://mlsanity.com/paper/2305.03880/) (2023)
- [Czert -- Czech BERT-like Model for Language Representation](https://mlsanity.com/paper/2103.13031/) (2021)
- [Multilingual Coreference Resolution with Harmonized Annotations](https://mlsanity.com/paper/2107.12088/) (2021)
- [Findings of the Shared Task on Multilingual Coreference Resolution](https://mlsanity.com/paper/2209.07841/) (2022)
- [CAW-coref: Conjunction-Aware Word-level Coreference Resolution](https://mlsanity.com/paper/2310.06165/) (2023)
- ['UFAL CorPipe at CRAC 2022: Effectivity of Multilingual Models for Coreference Resolution](https://mlsanity.com/paper/2209.07278/) (2022)
- [Training dataset and dictionary sizes matter in BERT models: the case of Baltic languages](https://mlsanity.com/paper/2112.10553/) (2021)
- ['UFAL CorPipe at CRAC 2023: Larger Context Improves Multilingual Coreference Resolution](https://mlsanity.com/paper/2311.14391/) (2023)
- [Neural Cross-Lingual Coreference Resolution and its Application to Entity Linking](https://mlsanity.com/paper/1806.10201/) (2018)
- [End-to-end Multilingual Coreference Resolution with Mention Head Prediction](https://mlsanity.com/paper/2209.12516/) (2022)

## Built on by (most cited first, 3 of 3)

- [Cross-Document Contextual Coreference Resolution in Knowledge Graphs](https://mlsanity.com/paper/2504.05767/) (2025)
- [Enhancing Coreference Resolution with Pretrained Language Models: Bridging the Gap Between Syntax and Semantics](https://mlsanity.com/paper/2504.05855/) (2025)
- [End-to-End Dialog Neural Coreference Resolution: Balancing Efficiency and Accuracy in Large-Scale Systems](https://mlsanity.com/paper/2504.05824/) (2025)

## Abstract (arXiv)

Coreference resolution, the task of identifying expressions in text that refer to the same entity, is a critical component in various natural language processing applications. This paper presents a novel end-to-end neural coreference resolution system utilizing the CorefUD 1.1 dataset, which spans 17 datasets across 12 languages. The proposed model is based on the standard end-to-end neural coreference resolution system. We first establish baseline models, including monolingual and cross-lingual variations, and then propose several extensions to enhance performance across diverse linguistic contexts. These extensions include cross-lingual training, incorporation of syntactic information, a Span2Head model for optimized headword prediction, and advanced singleton modeling. We also experiment with headword span representation and long-documents modeling through overlapping segments. The proposed extensions, particularly the heads-only approach, singleton modeling, and long document prediction, significantly improve performance across most datasets. We also perform zero-shot cross-lingual experiments, highlighting the potential and limitations of cross-lingual transfer in coreference resolution. Our findings contribute to the development of robust and scalable coreference systems for multilingual coreference resolution. Finally, we evaluate our model on the CorefUD 1.1 test set and surpass the best model from the CRAC 2023 shared task of comparable size by a large margin.