Explainable NLP (ExNLP) has increasingly focused on collecting human-annotated textual
explanations. These explanations are used downstream in three ways: as data …

As online false information continues to grow, automated fact‐checking has gained an
increasing amount of attention in recent years. Researchers in the field of Natural Language …

Fact-checking has become increasingly important due to the speed with which both
information and misinformation can spread in the modern media ecosystem. Therefore …

Existing datasets for automated fact-checking have substantial limitations, such as relying on
artificial claims, lacking annotations for evidence and intermediate reasoning, or including …

We introduce scientific claim verification, a new task to select abstracts from the research
literature containing evidence that SUPPORTS or REFUTES a given scientific claim, and to …

Fact verification has attracted a lot of attention in the machine learning and natural language
processing communities, as it is one of the key methods for detecting misinformation …

Misinformation threatens modern society by promoting distrust in science, changing
narratives in public health, heightening social polarization, and disrupting democratic …

Fact-checking is the task of verifying the veracity of claims by assessing their assertions
against credible evidence. The vast majority of fact-checking studies focus exclusively on …

We propose end-to-end multimodal fact-checking and explanation generation, where the
input is a claim and a large collection of web sources, including articles, images, videos, and …

Understanding attitudes expressed in texts, also known as stance detection, plays an
important role in systems for detecting false information online, be it misinformation …