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.

基于 CorefUD 数据集和通用形态句法和共指注释，我们通过研究核心指代在不同语言、不同语体和不同层次上的特点，对 SotA 系统无法解决的最具挑战性情况进行错误分析，然后从通用形态句法注释中提取特征，并将这些特征集成到基线系统中，结果显示我们的最佳配置特征提高了 0.9% 的 F1 得分。