论文标题
CRAC 2022上的fal copipe：多语言模型的核心分辨率有效性
ÚFAL CorPipe at CRAC 2022: Effectivity of Multilingual Models for Coreference Resolution
论文作者
论文摘要
我们描述了关于多语言核心分辨率的CRAC 2022共享任务的获胜提交。我们的系统首先解决了提及检测，然后使用先进的最大化方法在检索到的跨度上链接，并且这两个任务均与共享变压器的权重进行微调。我们报告了微调各种预告片模型的结果。此贡献的中心是微调的多语言模型。我们发现了一个具有足够大的编码器的大型多语言模型，可以全面提高所有数据集的性能，其好处不仅限于代表性不足的语言或类型上相对语言的群体。源代码可在https://github.com/ufal/crac2022-corpipe上找到。
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 https://github.com/ufal/crac2022-corpipe.
