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Found 10 papers, 2 papers with code
ALUE: Arabic Language Understanding Evaluation
The emergence of Multi-task learning (MTL)models in recent years has helped push thestate of the art in Natural Language Un-derstanding (NLU).
WojoodNER 2023: The First Arabic Named Entity Recognition Shared Task
The winning teams achieved F1 scores of 91. 96 and 93. 73 in FlatNER and NestedNER, respectively.
VoxArabica: A Robust Dialect-Aware Arabic Speech Recognition System
We train a wide range of models such as HuBERT (DID), Whisper, and XLS-R (ASR) in a supervised setting for Arabic DID and ASR tasks.
N-Shot Benchmarking of Whisper on Diverse Arabic Speech Recognition
Whisper, the recently developed multilingual weakly supervised model, is reported to perform well on multiple speech recognition benchmarks in both monolingual and multilingual settings.
Bench-Marking And Improving Arabic Automatic Image Captioning Through The Use Of Multi-Task Learning Paradigm
The results showed that the use of multi-task learning and pre-trained word embeddings noticeably enhanced the quality of image captioning, however the presented results shows that Arabic captioning still lags behind when compared to the English language.
sarcasm detection and quantification in arabic tweets
The role of predicting sarcasm in the text is known as automatic sarcasm detection.
Multi-Dialect Arabic BERT for Country-Level Dialect Identification
Our winning solution itself came in the form of an ensemble of different training iterations of our pre-trained BERT model, which achieved a micro-averaged F1-score of 26. 78% on the subtask at hand.
Mawdoo3 AI at MADAR Shared Task: Arabic Fine-Grained Dialect Identification with Ensemble Learning
In this paper we discuss several models we used to classify 25 city-level Arabic dialects in addition to Modern Standard Arabic (MSA) as part of MADAR shared task (sub-task 1).
Mawdoo3 AI at MADAR Shared Task: Arabic Tweet Dialect Identification
Arabic dialect identification is an inherently complex problem, as Arabic dialect taxonomy is convoluted and aims to dissect a continuous space rather than a discrete one.
Team JUST at the MADAR Shared Task on Arabic Fine-Grained Dialect Identification
In this paper, we describe our team{'}s effort on the MADAR Shared Task on Arabic Fine-Grained Dialect Identification.
