Automatic Claim Review for Climate Science via Explanation Generation

TL;DR

This paper presents an automated method to generate explanations for climate change claims by leveraging open domain question answering techniques, aiding fact-checking with external knowledge sources.

Contribution

It introduces an approach that automates explanation generation for climate claims using a fusion decoder with retrieved supporting passages, improving interpretability.

Findings

High-quality explanations can be generated with minimal manually written support

Different knowledge sources and retriever depths impact explanation quality

The method effectively supports climate claim veracity assessment

Abstract

There is unison is the scientific community about human induced climate change. Despite this, we see the web awash with claims around climate change scepticism, thus driving the need for fact checking them but at the same time providing an explanation and justification for the fact check. Scientists and experts have been trying to address it by providing manually written feedback for these claims. In this paper, we try to aid them by automating generating explanation for a predicted veracity label for a claim by deploying the approach used in open domain question answering of a fusion in decoder augmented with retrieved supporting passages from an external knowledge. We experiment with different knowledge sources, retrievers, retriever depths and demonstrate that even a small number of high quality manually written explanations can help us in generating good explanations.