# A Cross-Domain Transferable Neural Coherence Model

arXiv: https://arxiv.org/abs/1905.11912 · HTML: https://mlsanity.com/paper/1905.11912/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2019.

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

- [State-of-the-art generalisation research in NLP: A taxonomy and review](https://mlsanity.com/paper/2210.03050/) (2022)
- [Measuring Conversational Uptake: A Case Study on Student-Teacher Interactions](https://mlsanity.com/paper/2106.03873/) (2021)
- [PLANET: Dynamic Content Planning in Autoregressive Transformers for Long-form Text Generation](https://mlsanity.com/paper/2203.09100/) (2022)
- [Improving Unsupervised Dialogue Topic Segmentation with Utterance-Pair Coherence Scoring](https://mlsanity.com/paper/2106.06719/) (2021)
- [Is Incoherence Surprising? Targeted Evaluation of Coherence Prediction from Language Models](https://mlsanity.com/paper/2105.03495/) (2021)
- [Evaluating Document Coherence Modelling](https://mlsanity.com/paper/2103.10133/) (2021)
- [Rethinking Self-Supervision Objectives for Generalizable Coherence Modeling](https://mlsanity.com/paper/2110.07198/) (2021)
- [MAWSEO: Adversarial Wiki Search Poisoning for Illicit Online Promotion](https://mlsanity.com/paper/2304.11300/) (2023)
- [Revisiting Cross-Lingual Summarization: A Corpus-based Study and A New Benchmark with Improved Annotation](https://mlsanity.com/paper/2307.04018/) (2023)
- [LLM Based Multi-Document Summarization Exploiting Main-Event Biased Monotone Submodular Content Extraction](https://mlsanity.com/paper/2310.03414/) (2023)
- [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)
- [A Digital Language Coherence Marker for Monitoring Dementia](https://mlsanity.com/paper/2310.09623/) (2023)
- [Enhanced Coherence-Aware Network with Hierarchical Disentanglement for Aspect-Category Sentiment Analysis](https://mlsanity.com/paper/2403.10214/) (2024)
- [Transformer Models for Text Coherence Assessment](https://mlsanity.com/paper/2109.02176/) (2021)
- [Controllable Multi-document Summarization: Coverage & Coherence Intuitive Policy with Large Language Model Based Rewards](https://mlsanity.com/paper/2310.03473/) (2023)
- [Improving the Generalization Ability in Essay Coherence Evaluation through Monotonic Constraints](https://mlsanity.com/paper/2308.02506/) (2023)
- [Neural Coherence : Find higher performance to out-of-distribution tasks from few samples](https://mlsanity.com/paper/2512.05880/) (2025)
- [CobSeg: Coherence Boundary Modeling for Dialogue Topic Segmentation](https://mlsanity.com/paper/2605.30668/) (2026)

## Abstract (arXiv)

Coherence is an important aspect of text quality and is crucial for ensuring its readability. One important limitation of existing coherence models is that training on one domain does not easily generalize to unseen categories of text. Previous work advocates for generative models for cross-domain generalization, because for discriminative models, the space of incoherent sentence orderings to discriminate against during training is prohibitively large. In this work, we propose a local discriminative neural model with a much smaller negative sampling space that can efficiently learn against incorrect orderings. The proposed coherence model is simple in structure, yet it significantly outperforms previous state-of-art methods on a standard benchmark dataset on the Wall Street Journal corpus, as well as in multiple new challenging settings of transfer to unseen categories of discourse on Wikipedia articles.