A current problem in NLP is massaging and processing low-resource languages which lack
useful training attributes such as supervised data, number of native speakers or experts, etc …

The last two decades have witnessed major disruptions to the traditional media industry as a
result of technological breakthroughs. New opportunities and challenges continue to arise …

In this multicultural age, language translation is one of the most performed tasks, and it is
becoming increasingly AI-moderated and automated. As a novel AI system, ChatGPT claims …

Deep neural networks and huge language models are becoming omnipresent in natural
language applications. As they are known for requiring large amounts of training data, there …

Research in NLP lacks geographic diversity, and the question of how NLP can be scaled to
low-resourced languages has not yet been adequately solved." Low-resourced"-ness is a …

Vehicle electrification has emerged as a global strategy to address climate change and
emissions externalities from the transportation sector. Deployment of charging infrastructure …

Over the past few years, researchers have been focusing on the identification of offensive
language on social networks. In places where English is not the primary language, social …

A" bigger is better" explosion in the number of parameters in deep neural networks has
made it increasingly challenging to make state-of-the-art networks accessible in compute …

The paper presents a new corpus for fake news detection in the Urdu language along with
the baseline classification and its evaluation. With the escalating use of the Internet …

We train several language models for Icelandic, including IceBERT, that achieve state-of-the-
art performance in a variety of downstream tasks, including part-of-speech tagging, named …