# Multi-Task Learning for Coherence Modeling

arXiv: https://arxiv.org/abs/1907.02427 · HTML: https://mlsanity.com/paper/1907.02427/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2019.

## Built on by (most cited first, 6 of 6)

- [State-of-the-art generalisation research in NLP: A taxonomy and review](https://mlsanity.com/paper/2210.03050/) (2022)
- [Multi-Task Learning in Natural Language Processing: An Overview](https://mlsanity.com/paper/2109.09138/) (2021)
- [Modeling Structural Similarities between Documents for Coherence Assessment with Graph Convolutional Networks](https://mlsanity.com/paper/2306.06472/) (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)
- [Hierarchical Multi-Task Learning Framework for Session-based Recommendations](https://mlsanity.com/paper/2309.06533/) (2023)

## Abstract (arXiv)

We address the task of assessing discourse coherence, an aspect of text quality that is essential for many NLP tasks, such as summarization and language assessment. We propose a hierarchical neural network trained in a multi-task fashion that learns to predict a document-level coherence score (at the network's top layers) along with word-level grammatical roles (at the bottom layers), taking advantage of inductive transfer between the two tasks. We assess the extent to which our framework generalizes to different domains and prediction tasks, and demonstrate its effectiveness not only on standard binary evaluation coherence tasks, but also on real-world tasks involving the prediction of varying degrees of coherence, achieving a new state of the art.