Generating Scientific Claims for Zero-Shot Fact Checking

In this article, we explore how to improve fact-checking by leveraging semantic triples and knowledge graphs. These techniques allow us to train models without relying on specific data from the domain we’re checking. This is useful since most fact-checking approaches rely heavily on training data from a single domain, which can lead to poor generalization when applied to other domains.
The article starts by defining the concept of semantic triples, which consist of three entities (subject, object, and relation) that describe a specific relationship between them. Open Information Extraction (Open IE) is then used to extract these triples from text. This process involves identifying the relevant information in plain text and organizing it into triples.
The next step is triple-level verification, which involves ensuring that the extracted triples are accurate and reliable. To do this, we use various techniques, such as back-tracing to trace the origin of each evidence triple, and leveraging unbiased datasets to train and evaluate our models. This helps us address ethical concerns related to bias in fact-checking models.
The article also discusses the importance of using pretrained Natural Language Inference (NLI) models to improve accuracy when dealing with out-of-domain contexts. These models are trained on large datasets and can provide valuable insights into the meaning of text, even when the domain is unfamiliar.
Overall, this article presents a promising approach to zero-shot fact-checking that leverages semantic triples and knowledge graphs to improve accuracy and generalization. By using pretrained NLI models and avoiding reliance on specific training data, we can develop more robust and reliable fact-checking systems.