Generating Fluent Fact Checking Explanations with Unsupervised Post-Editing
Shailza Jolly, Pepa Atanasova, Isabelle Augenstein

TL;DR
This paper introduces an unsupervised post-editing method that improves the fluency and coherence of fact-checking explanations generated from journalist comments, enhancing their readability and informativeness.
Contribution
It proposes a novel iterative phrase-level editing algorithm for unsupervised enhancement of fact-checking explanations, addressing fluency and coherence issues in prior extractive methods.
Findings
Generated explanations are fluent and readable.
The approach covers important information effectively.
Works well on LIAR-PLUS and PubHealth datasets.
Abstract
Fact-checking systems have become important tools to verify fake and misguiding news. These systems become more trustworthy when human-readable explanations accompany the veracity labels. However, manual collection of such explanations is expensive and time-consuming. Recent works frame explanation generation as extractive summarization, and propose to automatically select a sufficient subset of the most important facts from the ruling comments (RCs) of a professional journalist to obtain fact-checking explanations. However, these explanations lack fluency and sentence coherence. In this work, we present an iterative edit-based algorithm that uses only phrase-level edits to perform unsupervised post-editing of disconnected RCs. To regulate our editing algorithm, we use a scoring function with components including fluency and semantic preservation. In addition, we show the applicability…
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Taxonomy
TopicsTopic Modeling · Explainable Artificial Intelligence (XAI) · Misinformation and Its Impacts
