Joint Modeling of Entities and Discourse Relations for Coherence Assessment

2025-09-04

AI Summary

Existing coherence modeling approaches treat entity consistency and discourse relations in isolation, failing to capture their interdependence. Method: We propose the first joint modeling framework that unifies entity linking—including coreference resolution—with explicit and implicit discourse relation classification, enabling neural cross-sentence entity tracking and discourse structure representation in a single, end-to-end trainable architecture. Results: Evaluated on three standard coherence assessment benchmarks, our model achieves significant improvements over single-feature baselines (average +3.2% F1), demonstrating strong complementarity between entity-level and discourse-level information. Contribution: This work introduces a novel, unified paradigm for discourse coherence modeling, establishing a principled foundation for jointly learning entity dynamics and rhetorical structure within cohesive text.

Technology Category

• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Reasoning under Uncertainty: Relational Probabilistic Models
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

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.

Problem

• Jointly modeling entities and discourse relations
• Combining entity and discourse features for coherence
• Enhancing coherence assessment through integrated modeling

Innovation

• Jointly models entities and discourse relations
• Integrates both entity and discourse features
• Enhances coherence assessment performance significantly