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 …

Neural abstractive summarization systems have gained significant progress in recent years.
However, abstractive summarization often produce inconsisitent statements or false facts …

Accurate fact verification depends on performing fine-grained reasoning over crucial entities
by capturing their latent logical relations hidden in multiple evidence clues, which is …

Existing fact verification methods employ pre-trained language models such as BERT for the
contextual representation of evidence sentences. However, such representations do not …

Fact verification is a challenging task of identifying the truthfulness of given claims based on
the retrieval of relevant evidence texts. Many claims require understanding and reasoning …

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 …

In the recent years, we have seen tremendous resurgence of neural networks, applied with
great success in highly diverse domains, ranging from speech recognition to game playing …

Deep Learning with Relational Logic Representations Page 1 DEEP LEARNING WITH
RELATIONAL LOGIC REPRESENTATIONS gustav šír Department of Computer Science Faculty …

In the recent years, we have seen tremendous resurgence of neural networks, applied with
great success in highly diverse domains, ranging from speech recognition to game playing …