论文标题
端到端多语言核心分辨率，并提及头部预测
End-to-end Multilingual Coreference Resolution with Mention Head Prediction
论文作者
论文摘要
本文介绍了我们对CRAC 2022关于多语言核心分辨率的共享任务的方法。我们的模型基于最先进的端到端核心分辨率系统。除了加入多语言培训外，我们还通过提到头部预测提高了结果。我们还试图将依赖性信息集成到我们的模型中。我们的系统最终以$ 3^{rd} $ plot。此外，我们在13个数据集中达到了最佳性能。
This paper describes our approach to the CRAC 2022 Shared Task on Multilingual Coreference Resolution. Our model is based on a state-of-the-art end-to-end coreference resolution system. Apart from joined multilingual training, we improved our results with mention head prediction. We also tried to integrate dependency information into our model. Our system ended up in $3^{rd}$ place. Moreover, we reached the best performance on two datasets out of 13.
