End-to-end Multilingual Coreference Resolution with Mention Head Prediction
This work addresses coreference resolution for multiple languages, but it is incremental as it builds on existing methods with minor improvements.
The paper tackled multilingual coreference resolution by adapting an end-to-end system with mention head prediction and dependency integration, achieving third place overall and best performance on two out of 13 datasets in the CRAC 2022 Shared Task.
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.