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 …

We introduce a FEVER-like dataset COVID-Fact of $4,086 $ claims concerning the COVID-
19 pandemic. The dataset contains claims, evidence for the claims, and contradictory claims …

We introduce HoVer (HOppy VERification), a dataset for many-hop evidence extraction and
fact verification. It challenges models to extract facts from several Wikipedia articles that are …

Misinformation is now a major problem due to its potential high risks to our core democratic
and societal values and orders. Out-of-context misinformation is one of the easiest and …

Fact-checking is an essential tool to mitigate the spread of misinformation and
disinformation. We introduce the task of fact-checking in dialogue, which is a relatively …

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 …

Models pre-trained with a language modeling objective possess ample world knowledge
and language skills, but are known to struggle in tasks that require reasoning. In this work …

Researchers in NLP often frame and discuss research results in ways that serve to
deemphasize the field's successes, often in response to the field's widespread hype. Though …

Adversarial attacks reveal important vulnerabilities and flaws of trained models. One potent
type of attack are universal adversarial triggers, which are individual n-grams that, when …

Fake news and deceptive machine-generated text are serious problems threatening modern
societies, including in the Arab world. This motivates work on detecting false and …