Data derived from the realm of the social sciences is often produced in digital text form,
which motivates its use as a source for natural language processing methods. Researchers …

The neural network model is having a significant impact on many real-world applications.
Unfortunately, the increasing popularity and complexity of these models also amplifies their …

Misinformation poses a significant challenge in the digital age, requiring robust methods to
detect fake news. This study investigates the effectiveness of using Back Translation (BT) …

Introduction Understanding the neural code has been one of the central aims of
neuroscience research for decades. Spikes are commonly referred to as the units of …

Large-scale, high-quality data are considered an essential factor for the successful
application of many deep learning techniques. Meanwhile, numerous real-world deep …

A main goal of Argument Mining (AM) is to analyze an author's stance. Unlike previous AM
datasets focusing only on text, the shared task at the 10th Workshop on Argument Mining …

Abstract Few-Shot Text Classification (FSTC) is a fundamental natural language processing
problem that aims to classify small amounts of text with high accuracy. Mainstream methods …

Technical systems generate an increasing amount of data as integrated sensors become
more available. Even so, data are still often scarce because of technical limitations of …

The rise of social networks and the increasing amount of time people spend on them have
created a perfect place for the dissemination of false narratives, propaganda, and …

With this work, we hypothesize that semantically enriching a user's text corpus using
backtranslation and expansion modules can improve performance for author profiling tasks …