  Coreference resolution is the task of identifying and grouping mentions
referring to the same real-world entity. Previous neural models have mainly
focused on learning span representations and pairwise scores for coreference
decisions. However, current methods do not explicitly capture the referential
choice in the hierarchical discourse, an important factor in coreference
resolution. In this study, we propose a new approach that incorporates
rhetorical information into neural coreference resolution models. We collect
rhetorical features from automated discourse parses and examine their impact.
As a base model, we implement an end-to-end span-based coreference resolver
using a partially fine-tuned multilingual entity-aware language model LUKE. We
evaluate our method on the RuCoCo-23 Shared Task for coreference resolution in
Russian. Our best model employing rhetorical distance between mentions has
ranked 1st on the development set (74.6% F1) and 2nd on the test set (73.3% F1)
of the Shared Task. We hope that our work will inspire further research on
incorporating discourse information in neural coreference resolution models.
