# Investigating Multilingual Coreference Resolution by Universal Annotations

arXiv: https://arxiv.org/abs/2310.17734 · HTML: https://mlsanity.com/paper/2310.17734/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 53 references that are in the map.
- First posted on arXiv 26 Oct 2023.

## Builds on (most cited first, 12 of 12)

- [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)
- [Higher-order Coreference Resolution with Coarse-to-fine Inference](https://mlsanity.com/paper/1804.05392/) (2018)
- [BERT for Coreference Resolution: Baselines and Analysis](https://mlsanity.com/paper/1908.09091/) (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)
- [End-to-end Multilingual Coreference Resolution with Mention Head Prediction](https://mlsanity.com/paper/2209.12516/) (2022)
- [Data Augmentation Methods for Anaphoric Zero Pronouns](https://mlsanity.com/paper/2109.09825/) (2021)
- [Quantifying Discourse Support for Omitted Pronouns](https://mlsanity.com/paper/2209.07961/) (2022)

## Abstract (arXiv)

Multilingual coreference resolution (MCR) has been a long-standing and challenging task. With the newly proposed multilingual coreference dataset, CorefUD (Nedoluzhko et al., 2022), we conduct an investigation into the task by using its harmonized universal morphosyntactic and coreference annotations. First, we study coreference by examining the ground truth data at different linguistic levels, namely mention, entity and document levels, and across different genres, to gain insights into the characteristics of coreference across multiple languages. Second, we perform an error analysis of the most challenging cases that the SotA system fails to resolve in the CRAC 2022 shared task using the universal annotations. Last, based on this analysis, we extract features from universal morphosyntactic annotations and integrate these features into a baseline system to assess their potential benefits for the MCR task. Our results show that our best configuration of features improves the baseline by 0.9% F1 score.