# A Unified Neural Coherence Model

arXiv: https://arxiv.org/abs/1909.00349 · HTML: https://mlsanity.com/paper/1909.00349/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2019.

## Built on by (most cited first, 19 of 19)

- [DiscoScore: Evaluating Text Generation with BERT and Discourse Coherence](https://mlsanity.com/paper/2201.11176/) (2022)
- [SNaC: Coherence Error Detection for Narrative Summarization](https://mlsanity.com/paper/2205.09641/) (2022)
- [Can Transformer Models Measure Coherence In Text? Re-Thinking the Shuffle Test](https://mlsanity.com/paper/2107.03448/) (2021)
- [Is Incoherence Surprising? Targeted Evaluation of Coherence Prediction from Language Models](https://mlsanity.com/paper/2105.03495/) (2021)
- [Towards Document-Level Paraphrase Generation with Sentence Rewriting and Reordering](https://mlsanity.com/paper/2109.07095/) (2021)
- [SubeventWriter: Iterative Sub-event Sequence Generation with Coherence Controller](https://mlsanity.com/paper/2210.06694/) (2022)
- [Reference-free Evaluation Metrics for Text Generation: A Survey](https://mlsanity.com/paper/2501.12011/) (2025)
- [Rethinking Self-Supervision Objectives for Generalizable Coherence Modeling](https://mlsanity.com/paper/2110.07198/) (2021)
- [Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings](https://mlsanity.com/paper/2110.06446/) (2021)
- [How to Find Strong Summary Coherence Measures? A Toolbox and a Comparative Study for Summary Coherence Measure Evaluation](https://mlsanity.com/paper/2209.06517/) (2022)
- [Modeling Structural Similarities between Documents for Coherence Assessment with Graph Convolutional Networks](https://mlsanity.com/paper/2306.06472/) (2023)
- [A Novel Computational and Modeling Foundation for Automatic Coherence Assessment](https://mlsanity.com/paper/2310.00598/) (2023)
- [Generating Lode Runner Levels by Learning Player Paths with LSTMs](https://mlsanity.com/paper/2107.12532/) (2021)
- [Transformer Models for Text Coherence Assessment](https://mlsanity.com/paper/2109.02176/) (2021)
- [Enhancing Coherence of Extractive Summarization with Multitask Learning](https://mlsanity.com/paper/2305.12851/) (2023)
- [Data Augmentation for Fake Reviews Detection in Multiple Languages and Multiple Domains](https://mlsanity.com/paper/2504.06917/) (2025)
- [CoUDA: Coherence Evaluation via Unified Data Augmentation](https://mlsanity.com/paper/2404.00681/) (2024)
- [BBScoreV2: Learning Time-Evolution and Latent Alignment from Stochastic Representation](https://mlsanity.com/paper/2405.17764/) (2024)
- [Neural Coherence : Find higher performance to out-of-distribution tasks from few samples](https://mlsanity.com/paper/2512.05880/) (2025)

## Abstract (arXiv)

Recently, neural approaches to coherence modeling have achieved state-of-the-art results in several evaluation tasks. However, we show that most of these models often fail on harder tasks with more realistic application scenarios. In particular, the existing models underperform on tasks that require the model to be sensitive to local contexts such as candidate ranking in conversational dialogue and in machine translation. In this paper, we propose a unified coherence model that incorporates sentence grammar, inter-sentence coherence relations, and global coherence patterns into a common neural framework. With extensive experiments on local and global discrimination tasks, we demonstrate that our proposed model outperforms existing models by a good margin, and establish a new state-of-the-art.