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.

本研究提出了一种新方法，通过引入修辞信息到神经共指消解模型中，来明确捕获层次化语篇中的指代选择， 并使用多语言实体感知语言模型 LUKE，在 RuCoCo-23 共指消解任务上评估了该方法的性能。实验表明，使用修辞距离的最佳模型在开发集 (74.6 % F1) 上排名第一，在测试集 (73.3 % F1) 上排名第二。希望我们的研究能够激发对于神经共指消解模型中引入语篇信息的进一步研究。

俄语轻量级核指代消解：基于分层语篇特征的方法