Entity-based Neural Local Coherence Modeling
Sungho Jeon, Michael Strube
2022Year
4Top-tier citations
Abstract
In this paper, we propose an entity-based neu-001 ral local coherence model which is linguis-002 tically more sound than previously proposed 003 neural coherence models. Recent neural co-004 herence models encode the input document 005 using large-scale pretrained language models.
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Cited by top-tier papers4
- BBScore: A Brownian Bridge Based Metric for Assessing Text CoherenceZhecheng Sheng, Tianhao Zhang, Chen Jiang, Dongyeop KangAAAI 2024 · 8 citations
- Discourse Relation-Enhanced Neural Coherence ModelingWei Liu, Michael StrubeACL 2025
- BBScoreV2: Learning Time-Evolution and Latent Alignment from Stochastic RepresentationTianhao Zhang, Zhecheng Sheng, Zhexiao Lin, Chen Jiang et al.EMNLP 2025
- Joint Modeling of Entities and Discourse Relations for Coherence AssessmentWei Liu, Michael StrubeEMNLP 2025
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