Zero-Shot Fact-Checking with Semantic Triples and Knowledge Graphs
Zhangdie Yuan Andreas Vlachos
Despite progress in automated fact-checking, most systems require a
significant amount of labeled training data, which is expensive. In this paper,
we propose a novel zero-shot method, which instead of operating directly on the
claim and evidence sentences, decomposes them into semantic triples augmented
using external knowledge graphs, and uses large language models trained for
natural language inference. This allows it to generalize to adversarial
datasets and domains that supervised models require specific training data for.
Our empirical results show that our approach outperforms previous zero-shot
approaches on FEVER, FEVER-Symmetric, FEVER 2.0, and Climate-FEVER, while being
comparable or better than supervised models on the adversarial and the
out-of-domain datasets.