27 citations · 55 across the 3 of their papers we have counts for
4 papers
Empathetic BERT2BERT Conversational Model: Learning Arabic Language Generation with Little Data
Tarek Naous, Wissam Antoun, Reem A. Mahmoud +1
Enabling empathetic behavior in Arabic dialogue agents is an important aspect of building human-like conversational models. While Arabic Natural Language Processing has seen signif…
AraGPT2: Pre-Trained Transformer for Arabic Language Generation
Wissam Antoun, Fady Baly, Hazem Hajj
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 corpus.…
AraELECTRA: Pre-Training Text Discriminators for Arabic Language Understanding
Wissam Antoun, Fady Baly, Hazem Hajj
Advances in English language representation enabled a more sample-efficient pre-training task by Efficiently Learning an Encoder that Classifies Token Replacements Accurately (ELEC…
AraBERT: Transformer-based Model for Arabic Language Understanding
Wissam Antoun, Fady Baly, Hazem Hajj
The Arabic language is a morphologically rich language with relatively few resources and a less explored syntax compared to English. Given these limitations, Arabic Natural Languag…