This paper presents an overview of the shared task on multilingual coreference resolution associated with the CRAC 2022 workshop. Shared task participants were supposed to develop trainable systems capable of identifying mentions and clustering them according to identity coreference. The public edition of CorefUD 1.0, which contains 13 datasets for 10 languages, was used as the source of training and evaluation data. The CoNLL score used in previous coreference-oriented shared tasks was used as the main evaluation metric. There were 8 coreference prediction systems submitted by 5 participating teams; in addition, there was a competitive Transformer-based baseline system provided by the organizers at the beginning of the shared task. The winner system outperformed the baseline by 12 percentage points (in terms of the CoNLL scores averaged across all datasets for individual languages).

翻译：本文件概述了与CRAC 2022 研讨会相关的多语种共同参照决议的共同任务;共同任务参与者应开发能够根据身份共同参照确定提及和分组的可培训系统;使用CorfUD 1.0公开版,其中载有10种语文的13个数据集,作为培训和评价数据的来源;使用先前共同参照的共有任务中所用的CoNLL分数作为主要评价指标;5个参与小组提交了8个共同参照预测系统;此外,在共同任务开始时,组织者提供了具有竞争力的以变换器为基础的基线系统;胜者系统比基线多出12个百分点(按CoNLL平均分数计算,每个语文的所有数据集均分)。