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
this https URL

我们提供的 CorPipe 是 2023 年 CRAC 共享任务多语言共指解析比赛的获胜系统，它是我们之前多语言共指流水线的改进版本，并以 4.5 个百分点的巨大优势超过其他参赛者。CorPipe 首先通过检测提及实体，然后通过在提取的范围上采用先行最大化方法进行共指链接。这两个任务是在所有可用语料库上使用共享的预训练语言模型进行联合训练的。我们的主要改进包括使用大于 512 个子词的输入和更改提及解码以支持组合解。源代码可以在此 https URL 中找到。

ÚFAL CorPipe 在 CRAC 2023：更大的上下文改善多语种共指消解

ÚFAL CorPipe at CRAC 2023: Larger Context Improves Multilingual  Coreference Resolution

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。

探索通过通用注释进行多语言指代消解

Investigating Multilingual Coreference Resolution by Universal  Annotations

We describe the winning submission to the CRAC 2022 Shared Task on
Multilingual Coreference Resolution. Our system first solves mention detection
and then coreference linking on the retrieved spans with an
antecedent-maximization approach, and both tasks are fine-tuned jointly with
shared Transformer weights. We report results of fine-tuning a wide range of
pretrained models. The center of this contribution are fine-tuned multilingual
models. We found one large multilingual model with sufficiently large encoder
to increase performance on all datasets across the board, with the benefit not
limited only to the underrepresented languages or groups of typologically
relative languages. The source code is available at
this https URL.

我们使用联合微调的方法，结合先进的预训练模型，成功地解决了 CRAC 2022 共享任务中的多语言指代消解问题，找到了一种足够大的编码器，提高了各数据集的表现，并提供了源代码。