论文标题
关于多语言核心分辨率的共享任务的发现
Findings of the Shared Task on Multilingual Coreference Resolution
论文作者
论文摘要
本文概述了与CRAC 2022研讨会相关的多语言核心分辨率的共享任务。共同的任务参与者应该开发能够识别提及并根据身份核心聚集的训练系统。 Corefud 1.0的公共版本包含10种语言的13个数据集，被用作培训和评估数据的来源。以前面向核心共享任务中使用的串联分数用作主要评估度量。 5个参与团队提交了8个核心预测系统；此外，在共享任务开始时，组织者提供了一个基于竞争变压器的基线系统。获胜者系统的表现优于基线（就在所有语言的所有数据集中平均而言，汇得分）。
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).
