A Novel Computational and Modeling Foundation for Automatic Coherence Assessment

TL;DR

This paper introduces a formal, computational framework for assessing text coherence based on linguistic principles, enabling large-scale automatic evaluation crucial for NLP tasks like summarization and generation.

Contribution

It formalizes coherence into three computational tasks grounded in linguistic theory and demonstrates that joint training on these tasks improves coherence assessment performance.

Findings

Joint training on coherence tasks outperforms task-specific models

The approach achieves better correlation with human judgments

Provides a scalable foundation for automatic coherence evaluation

Abstract

Coherence is an essential property of well-written texts, that refers to the way textual units relate to one another. In the era of generative AI, coherence assessment is essential for many NLP tasks; summarization, generation, long-form question-answering, and more. However, in NLP {coherence} is an ill-defined notion, not having a formal definition or evaluation metrics, that would allow for large-scale automatic and systematic coherence assessment. To bridge this gap, in this work we employ the formal linguistic definition of \citet{Reinhart:1980} of what makes a discourse coherent, consisting of three conditions -- {\em cohesion, consistency} and {\em relevance} -- and formalize these conditions as respective computational tasks. We hypothesize that (i) a model trained on all of these tasks will learn the features required for coherence detection, and that (ii) a joint model for all tasks will exceed the performance of models trained on each task individually. On two benchmarks for coherence scoring rated by humans, one containing 500 automatically-generated short stories and another containing 4k real-world texts, our experiments confirm that jointly training on the proposed tasks leads to better performance on each task compared with task-specific models, and to better performance on assessing coherence overall, compared with strong baselines. We conclude that the formal and computational setup of coherence as proposed here provides a solid foundation for advanced methods of large-scale automatic assessment of coherence.