Coreference resolution, the task of identifying expressions in text that refer to the same entity, is a critical component in various natural language processing (NLP) applications. This paper presents our end-to-end neural coreference resolution system, utilizing the CorefUD 1.1 dataset, which spans 17 datasets across 12 languages. We first establish strong 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 CorefUD 1.1 test set and surpass the best model from CRAC 2023 shared task of a comparable size by a large margin. Our nodel is available on GitHub: \url{https://github.com/ondfa/coref-multiling}

本研究解决了多语言文本中共指消解的性能不足问题。我们提出了一种端到端的神经网络系统，并通过强基线模型和多种增强策略，如跨语言训练和单例建模，显著提升了消解准确率。研究发现，所提方案在多个数据集上都取得了优异表现，推动了多语言共指消解系统的开发。

探索多种策略以改善多语言共指消解