Coreference Resolution for P olish: Improvements within the CRAC 2022 Shared Task (2022)

Abstract: The paper presents our system for coreference resolution in Polish. We compare the system with previous works for the Polish language as well as with the multilingual approach in the CRAC 2022 Shared Task on Multilingual Coreference Resolution thanks to a universal, multilingual data format and evaluation tool. We discuss the accuracy, computational performance, and evaluation approach of the new System which is a faster, end-to-end solution.

## introduction the paper describes our approach to coreference resolution in the polish language submitted to the crac 2022 shared task on multilingual coreference resolution. the scope of the shared task was multilingual systems for 10 languages included in corefud 1.0 @xcite . however, here we focus mainly on the improvements for the polish language within this task and present end-to-end coreference resolution for polish. ## related work there are two important types of references for our work: the evaluation methods for coreference resolution and previous solutions for the polish language. ## results we compare metrics speed for the system with the corneferencer and other submissions. ## further work longformer there is a longformer model for polish available on hugging face models foot_3 . it could improve results for longer texts (which are included in the polish test set). however, it is not popular yet and was not tested. multilingual comparison 4 shared task submissions achieved above 60 f1 score, all of which gained more than 70 f1 for the polish test subset. a comparison of these methods should help to answer questions: (1) is there still a need for a language-specific solution, (2) whether there are issues with data quality between corpora for different languages that could be improved by using guidelines from top-scored datasets. ## summary for polish, the system is faster, end-to-end, and has comparable performance to the previous solution. there is a need to analyze other submissions to assess the state of language-specific systems' performance, however, we see that there is a capability to build a high-performing multilingual system. the presence of a multilingual dataset and evaluation tool provides the infrastructure to build such a system efficiently and track progress.