同时建模实体与语篇关系，提升文本连贯性评估效果
Joint Modeling of Entities and Discourse Relations for Coherence Assessment
- 联合建模句子间的实体指代与语篇关系
- 在三个基准数据集上性能显著提升
- 适合需要精准连贯性分析的NLP任务
语言学中，连贯性可通过维持句子间相同实体指代及建立语篇关系实现。然而，现有大多数连贯性建模研究仅关注实体特征或语篇关系特征之一，缺乏对二者协同作用的关注。本研究探索了两种联合建模实体与语篇关系的方法，用于连贯性评估。在三个基准数据集上的实验表明，整合两类特征能显著提升模型性能，凸显了同步建模实体与语篇关系在连贯性评价中的优势。
原文摘要 · Abstract (English)
In linguistics, coherence can be achieved by different means, such as by maintaining reference to the same set of entities across sentences and by establishing discourse relations between them. However, most existing work on coherence modeling focuses exclusively on either entity features or discourse relation features, with little attention given to combining the two. In this study, we explore two methods for jointly modeling entities and discourse relations for coherence assessment. Experiments on three benchmark datasets show that integrating both types of features significantly enhances the performance of coherence models, highlighting the benefits of modeling both simultaneously for coherence evaluation.
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