25 citations · 77 across the 74 of their papers we have counts for
8 papers · 1 filter
SERENGETI: Massively Multilingual Language Models for Africa
Ife Adebara, AbdelRahim Elmadany, Muhammad Abdul-Mageed +1
Multilingual pretrained language models (mPLMs) acquire valuable, generalizable linguistic information during pretraining and have advanced the state of the art on task-specific fi…
ORCA: A Challenging Benchmark for Arabic Language Understanding
AbdelRahim Elmadany, El Moatez Billah Nagoudi, Muhammad Abdul-Mageed
Due to their crucial role in all NLP, several benchmarks have been proposed to evaluate pretrained language models. In spite of these efforts, no public benchmark of diverse nature…
JASMINE: Arabic GPT Models for Few-Shot Learning
El Moatez Billah Nagoudi, Muhammad Abdul-Mageed, AbdelRahim Elmadany +2
Scholarship on generative pretraining (GPT) remains acutely Anglocentric, leaving serious gaps in our understanding of the whole class of autoregressive models. For example, we hav…
A Benchmark Study of Contrastive Learning for Arabic Social Meaning
Md Tawkat Islam Khondaker, El Moatez Billah Nagoudi, AbdelRahim Elmadany +2
Contrastive learning (CL) brought significant progress to various NLP tasks. Despite this progress, CL has not been applied to Arabic NLP to date. Nor is it clear how much benefits…
AfroLID: A Neural Language Identification Tool for African Languages
Ife Adebara, AbdelRahim Elmadany, Muhammad Abdul-Mageed +1
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 tech…
TURJUMAN: A Public Toolkit for Neural Arabic Machine Translation
El Moatez Billah Nagoudi, AbdelRahim Elmadany, Muhammad Abdul-Mageed
We present TURJUMAN, a neural toolkit for translating from 20 languages into Modern Standard Arabic (MSA). TURJUMAN exploits the recently-introduced text-to-text Transformer AraT5…
Improving Neural Machine Translation of Indigenous Languages with Multilingual Transfer Learning
Wei-Rui Chen, Muhammad Abdul-Mageed
Machine translation (MT) involving Indigenous languages, including those possibly endangered, is challenging due to lack of sufficient parallel data. We describe an approach exploi…
Improving Automatic Speech Recognition for Non-Native English with Transfer Learning and Language Model Decoding
Peter Sullivan, Toshiko Shibano, Muhammad Abdul-Mageed
ASR systems designed for native English (L1) usually underperform on non-native English (L2). To address this performance gap, \textbf{(i)} we extend our previous work to investiga…