Bidirectional Encoder Representations from Transformers (BERT) has gained increasing
attention from researchers and practitioners as it has proven to be an invaluable technique …

Transfer learning with a unified Transformer framework (T5) that converts all language
problems into a text-to-text format was recently proposed as a simple and effective transfer …

The rapid progress in deep learning has propelled transformer-based models to the
forefront, establishing them as leading solutions for a multiple NLP tasks. These tasks span …

Advances in English language representation enabled a more sample-efficient pre-training
task by Efficiently Learning an Encoder that Classifies Token Replacements Accurately …

Recently, pre-trained transformer-based architectures have proven to be very efficient at
language modeling and understanding, given that they are trained on a large enough …

We describe findings of the third Nuanced Arabic Dialect Identification Shared Task (NADI
2022). NADI aims at advancing state of the art Arabic NLP, including on Arabic dialects. It …

Transfer learning with a unified Transformer framework (T5) that converts all language
problems into a text-to-text format was recently proposed as a simple and effective transfer …

Despite the purported multilingual proficiency of instruction-finetuned large language
models (LLMs) such as ChatGPT and Bard, the linguistic inclusivity of these models remains …

Language identification (LID) is a crucial precursor for NLP, especially for mining web data.
Problematically, most of the world's 7000+ languages today are not covered by LID …

Automatic Arabic Dialect Identification (ADI) of text has gained great popularity since it was
introduced in the early 2010s. Multiple datasets were developed, and yearly shared tasks …