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.

翻译：共指消解是识别并聚合指向同一真实世界实体的提及项的任务。以往的神经模型主要关注学习提及跨度的表示及其两两配对得分以完成共指决策。然而，现有方法未能显式捕捉层级化篇章结构中的指称选择——这一共指消解的重要影响因素。在本研究中，我们提出一种将修辞信息融入神经共指消解模型的新方法。我们从自动篇章解析中提取修辞特征，并考察其影响。作为基础模型，我们采用部分微调的多语言实体感知语言模型LUKE，实现了一个端到端基于跨度表示的共指消解器。我们在俄语共指消解竞赛RuCoCo-23上评估了该方法。采用提及项间修辞距离的最佳模型在该竞赛开发集上排名第一（F1值为74.6%），在测试集上排名第二（F1值为73.3%）。我们希望本研究能够激发更多将篇章信息融入神经共指消解模型的探索。