Automatic Claim Review for Climate Science via Explanation Generation
arXiv:2107.14740
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.
References in corpus (10)
- Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
- Language Models are Few-Shot Learners
- BERTScore: Evaluating Text Generation with BERT
- SQuAD: 100,000+ Questions for Machine Comprehension of Text
- REALM: Retrieval-Augmented Language Model Pre-Training
- Language Models as Knowledge Bases?
- Distilling Knowledge from Reader to Retriever for Question Answering
- CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims
- Efficient Passage Retrieval with Hashing for Open-domain Question Answering