Coherence Evaluation via Unified Data Augmentation: A Lightweight Model Outperforming Large Language Models

Core Concepts

A lightweight coherence evaluation model, COUDA, achieves state-of-the-art performance by unifying global and local aspects of discourse coherence through a unified data augmentation framework.

Abstract

The paper proposes a coherence evaluation framework called COUDA that unifies both global and local aspects of discourse coherence.
Global Augmentation:

COUDA constructs negative samples by shuffling the order of sentences in the original discourse, disrupting its global coherence.
Local Augmentation:

COUDA introduces a novel generative augmentation strategy to construct negative samples with poor local coherence.
This involves post-pretraining a generative model and applying two controlling mechanisms (context truncation and coherence filtering) to manipulate the difficulty of generated samples.
Unified Scoring:

During inference, COUDA jointly evaluates both global and local coherence aspects to provide a comprehensive assessment of the overall discourse coherence.
Experiments show that with only 233M parameters, COUDA outperforms previous methods, including recent large language model-based metrics like GPT-4, on both pointwise scoring and pairwise ranking tasks for coherence evaluation.

Stats

The cities of Annecy, Munich and Pyeongchang will battle it out to host the 2018 Winter Olympics.
The International Olympic Committee have confirmed they have received applications from France, Germany and South Korea ahead of this week's deadline.

Quotes

"Coherence is a vital aspect of communication that evaluates the structure and organization of discourse (Halliday and Hasan, 1976; Grosz and Sidner, 1986)."
"Due to the scarcity of human-annotated data, data augmentation techniques are commonly employed in training a coherence evaluation model (Li and Jurafsky, 2017; Jwalapuram et al., 2022)."

Key Insights Distilled From

CoUDA

by Dawei Zhu,We... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00681.pdf

Deeper Inquiries

What other linguistic theories or frameworks could be leveraged to further improve coherence evaluation models?

In addition to the linguistic theory of discourse structure, other frameworks that could be leveraged to enhance coherence evaluation models include Centering Theory and Rhetorical Structure Theory. Centering Theory focuses on the entities mentioned in a text and how they relate to each other, which can provide insights into the coherence of a discourse. Rhetorical Structure Theory, on the other hand, examines the hierarchical structure of discourse and how different parts of a text are connected. By incorporating these frameworks into coherence evaluation models, a more comprehensive understanding of discourse coherence can be achieved.

How could the generative augmentation strategy be extended to handle longer discourses more efficiently?

To handle longer discourses more efficiently, the generative augmentation strategy could be extended by implementing a hierarchical approach. Instead of generating the entire discourse at once, the model could generate coherent segments of the discourse sequentially and then combine them to form the complete discourse. This hierarchical generation process would allow the model to focus on smaller sections at a time, making it more manageable and efficient for longer discourses. Additionally, techniques such as beam search or nucleus sampling could be employed to improve the diversity and quality of the generated samples.

What are the potential applications of a robust coherence evaluation model beyond text generation and assessment?

A robust coherence evaluation model has various potential applications beyond text generation and assessment. One application could be in educational settings, where the model could be used to provide feedback on students' writing to improve the coherence and structure of their essays or reports. In the field of natural language processing, the model could be utilized in information retrieval systems to rank and retrieve documents based on their coherence. Furthermore, in content creation platforms, the model could assist content creators in ensuring that their articles, blogs, or social media posts are coherent and engaging for the readers. Overall, a robust coherence evaluation model has the potential to enhance communication, understanding, and engagement in various domains.