Generating Fluent Fact Checking Explanations with Unsupervised Post-Editing

• URL: http://arxiv.org/abs/2112.06924v1
• Date: Mon, 13 Dec 2021 15:31:07 GMT
• Title: Generating Fluent Fact Checking Explanations with Unsupervised Post-Editing
• Authors: Shailza Jolly, Pepa Atanasova, Isabelle Augenstein
• Abstract summary: We present an iterative edit-based algorithm that uses only phrase-level edits to perform unsupervised post-editing of ruling comments. We show that our model generates explanations that are fluent, readable, non-redundant, and cover important information for the fact check.
• Score: 22.5444107755288
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 of our approach in a completely unsupervised setting. We experiment with two benchmark datasets, LIAR-PLUS and PubHealth. We show that our model generates explanations that are fluent, readable, non-redundant, and cover important information for the fact check.

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