2025
EMNLP
EMNLP 2025
Joint Modeling of Entities and Discourse Relations for Coherence Assessment
Abstract
AbstractIn 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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Interdisciplinary Bridge
— Artificial Intelligence and Interdisciplinary and Natural Language Processing
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Keyword Pioneer
— entity feature
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio
Authors
Topics
Natural Language Processing > Understanding > Coreference Resolution
Natural Language Processing > Understanding > Semantic Analysis
Natural Language Processing > Applications > Text Classification
Interdisciplinary > Linguistics > Computational Linguistics
Artificial Intelligence > Core AI > Natural Language Processing