| Challenge: | Existing approaches to local coherence modeling capture text relatedness at the level of sentence-to-sentence transitions. |
| Approach: | They propose a local coherence model that captures the flow of what connects adjacent sentences . they represent the semantics of a sentence by a vector and capture its state at each word . |
| Outcome: | The proposed model is beneficial for readability assessment and essay scoring tasks. |
Similar Papers
A Neural Local Coherence Analysis Model for Clarity Text Scoring (2020.coling-main)
Copied to clipboard
| Challenge: | Existing methods for scoring text clarity use local coherence between adjacent sentences . local cohesion is one of the main properties to identify whether a text is well-structured or not. |
| Approach: | They propose a method for scoring text clarity by utilizing local coherence between adjacent sentences. |
| Outcome: | The proposed method improves on the PeerRead benchmark dataset. |
Centering-based Neural Coherence Modeling with Hierarchical Discourse Segments (2020.emnlp-main)
Copied to clipboard
| Challenge: | Prior studies of coherence focused on identifying semantic relations between adjacent sentences. |
| Approach: | They propose a coherence model which takes discourse structural information into account without relying on human annotations. |
| Outcome: | The proposed model performs state-of-the-art on automated essay scoring and assessing writing quality tasks. |
A Cross-Domain Transferable Neural Coherence Model (P19-1)
Copied to clipboard
Peng Xu, Hamidreza Saghir, Jin Sung Kang, Teng Long, Avishek Joey Bose, Yanshuai Cao, Jackie Chi Kit Cheung
| Challenge: | Existing coherence models do not generalize to unseen categories of text . previous work advocates for generative models for cross-domain generalization . |
| Approach: | They propose a local discriminative neural model with a smaller negative sampling space that can discriminate against incorrect orderings. |
| Outcome: | The proposed model outperforms state-of-the-art methods on a standard benchmark dataset on the Wall Street Journal corpus and multiple challenging settings on Wikipedia articles. |
How coherent are neural models of coherence? (2020.coling-main)
Copied to clipboard
| Challenge: | Existing approaches to model coherence are limited to small newswire corpora . evaluators need to be trained on lexical and document levels to perform evaluations . |
| Approach: | They propose four generic evaluation tasks that capture coherence-specific properties . they aim at capturing correct use of discourse connectives and lexical cohesion . |
| Outcome: | The proposed tasks capture coherence-specific properties, including correct use of discourse connectives, lexical cohesion, temporal consistency among events and participants in a story. |
A Novel Computational Modeling Foundation for Automatic Coherence Assessment (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing models for text coherence assessment rely on a proxy task . however, this approach does not capture the full range of factors contributing to coherency. |
| Approach: | They propose a formal linguistic definition of what makes a discourse coherent and formalize these conditions as respective computational tasks that are jointly trained. |
| Outcome: | The proposed model improves on two human-rated coherence benchmarks. |
A Unified Neural Coherence Model (D19-1)
Copied to clipboard
| Challenge: | Existing models for coherence modeling fail on harder tasks with more realistic application scenarios. |
| Approach: | They propose a unified coherence model that incorporates sentence grammar, inter-sentence coherent relations, and global coherency patterns into a common neural framework. |
| Outcome: | The proposed model outperforms existing models on local and global discrimination tasks and outperformed existing models by a good margin. |
Multi-Task Learning for Coherence Modeling (P19-1)
Copied to clipboard
| Challenge: | Existing models for assessing discourse coherence have been developed for summarization and language assessment. |
| Approach: | They propose a hierarchical neural network that learns to predict a document-level coherence score along with word-level grammatical roles, taking advantage of inductive transfer between the two tasks. |
| Outcome: | The proposed framework can predict document-level coherence score and word-level grammatical roles using inductive transfer between the two tasks. |
Discourse Relation-Enhanced Neural Coherence Modeling (2025.acl-long)
Copied to clipboard
| Challenge: | Existing work on coherence modeling has focused on integrating entity-based models. |
| Approach: | They propose a model that integrates text- and relation-based features for coherence assessment using position-aware attention and a visible matrix. |
| Outcome: | The proposed model improves baselines on two benchmarks and shows that relation features are important for coherence modeling. |
Entity-based Neural Local Coherence Modeling (2022.acl-long)
Copied to clipboard
| Challenge: | Recent neural coherence models encode the input document using large-scale pretrained language models. |
| Approach: | They propose an entity-based neural local coherence model which is linguistically more sound than previous models. |
| Outcome: | The proposed model outperforms existing models on three downstream tasks. |
Evaluating Text Coherence at Sentence and Paragraph Levels (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing text ordering models have been used to test coherence in NLP for a long time. |
| Approach: | They propose to perform paragraph ordering task and sentence ordering by using four corpora from different domains. |
| Outcome: | The proposed model performs better under certain extreme conditions than the most prevalent metric used before. |