We present CorPipe, the winning entry to the CRAC 2023 Shared Task on Multilingual Coreference Resolution. Our system is an improved version of our earlier multilingual coreference pipeline, and it surpasses other participants by a large margin of 4.5 percent points. CorPipe first performs mention detection, followed by coreference linking via an antecedent-maximization approach on the retrieved spans. Both tasks are trained jointly on all available corpora using a shared pretrained language model. Our main improvements comprise inputs larger than 512 subwords and changing the mention decoding to support ensembling. The source code is available at https://github.com/ufal/crac2023-corpipe.

翻译：本文提出CorPipe，该系统在CRAC 2023多语言共指消解共享任务中获得冠军。我们的系统是早期多语言共指流水线的改进版本，以4.5个百分点的显著优势超越其他参赛者。CorPipe首先执行指称检测，随后通过基于前驱最大化的方法对检索到的片段进行共指链接。两项任务均使用共享预训练语言模型在所有可用语料库上联合训练。主要改进包括采用超过512子词的输入，以及调整指称解码策略以支持集成学习。源代码已发布于https://github.com/ufal/crac2023-corpipe。