# Analyzing Neural Discourse Coherence Models

arXiv: https://arxiv.org/abs/2011.06306 · HTML: https://mlsanity.com/paper/2011.06306/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2020.

## Built on by (most cited first, 1 of 1)

- [Transformer Models for Text Coherence Assessment](https://mlsanity.com/paper/2109.02176/) (2021)

## Abstract (arXiv)

In this work, we systematically investigate how well current models of coherence can capture aspects of text implicated in discourse organisation. We devise two datasets of various linguistic alterations that undermine coherence and test model sensitivity to changes in syntax and semantics. We furthermore probe discourse embedding space and examine the knowledge that is encoded in representations of coherence. We hope this study shall provide further insight into how to frame the task and improve models of coherence assessment further. Finally, we make our datasets publicly available as a resource for researchers to use to test discourse coherence models.