We study the fact-checking problem, which aims to identify the veracity of a given claim.
Specifically, we focus on the task of Fact Extraction and VERification (FEVER) and its …

Fact verification (FV) is a challenging task which aims to verify a claim using multiple
evidential sentences from trustworthy corpora, eg, Wikipedia. Most existing approaches …

Few-shot learning has drawn researchers' attention to overcome the problem of data
scarcity. Recently, large pre-trained language models have shown great performance in few …

The development of social media enables fake news to be expressed in a multi-modal form,
which is disseminated on various social platforms and brings harmful social impacts. To …

The increased focus on misinformation has spurred development of data and systems for
detecting the veracity of a claim as well as retrieving authoritative evidence. The Fact …

Finding counterevidence to statements is key to many tasks, including counterargument
generation. We build a system that, given a statement, retrieves counterevidence from …

Training machine learning models to successfully perform scientific fact-checking tasks is
challenging due to the expertise bottleneck that limits the availability of appropriate training …

The alarming spread of fake news in social media, together with the impossibility of scaling
manual fact verification, motivated the development of natural language processing …

With the continuing spread of misinformation and disinformation online, it is of increasing
importance to develop combating mechanisms at scale in the form of automated systems …

With the evolution of social media technology, the mode of information exchange has
transitioned from a singular text format to a multifaceted blend of text, images, audio, and …