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Found 6 papers, 1 papers with code
BERT-based Multi-Task Model for Country and Province Level MSA and Dialectal Arabic Identification
Dialect and standard language identification are crucial tasks for many Arabic natural language processing applications.
CS-UM6P at SemEval-2022 Task 6: Transformer-based Models for Intended Sarcasm Detection in English and Arabic
Our system\footnote{The source code of our system is available at \url{https://github. com/AbdelkaderMH/iSarcasmEval}} consists of three deep learning-based models leveraging two existing pre-trained language models for Arabic and English.
CS-UM6P at SemEval-2021 Task 1: A Deep Learning Model-based Pre-trained Transformer Encoder for Lexical Complexity
The proposed system consists of a deep learning model, based on pre-trained transformer encoder, for word and Multi-Word Expression (MWE) complexity prediction.
CS-UM6P at SemEval-2021 Task 7: Deep Multi-Task Learning Model for Detecting and Rating Humor and Offense
Humor detection has become a topic of interest for several research teams, especially those involved in socio-psychological studies, with the aim to detect the humor and the temper of a targeted population (e. g. a community, a city, a country, the employees of a given company).
BERT-based Multi-Task Model for Country and Province Level Modern Standard Arabic and Dialectal Arabic Identification
Dialect and standard language identification are crucial tasks for many Arabic natural language processing applications.
Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in Arabic Language
The prominence of figurative language devices, such as sarcasm and irony, poses serious challenges for Arabic Sentiment Analysis (SA).
