The rise of propaganda and disinformation in the digital age has necessitated the development of effective detection methods to combat the spread of deceptive information. In this paper we present our approach proposed for ArAIEval shared task : propaganda and disinformation detection in Arabic text. Our system utilised different pre-trained BERT based models, that makes use of prompt-learning based on knowledgeable expansion and prefix-tuning. The proposed approach secured third place in subtask-1A with 0.7555 F1-micro score, second place in subtask-1B with 0.5658 F1-micro score. However, for subtask-2A & 2B, the proposed system achieved fourth place with an F1-micro score of 0.9040, 0.8219 respectively. Our findings suggest that prompt-tuning-based & prefix-tuning based models performed better than conventional fine-tuning. Furthermore, using loss aware class imbalance, improved performance.

Dialect identification systems play a significant role in various fields and applications as in speech and language technologies, facilitating language education, supporting sociolinguistic research, preserving linguistic diversity, enhancing text-to-speech systems. In this paper, we provide our findings and results in NADI 2023 shared task for country-level dialect identification and machine translation (MT) from dialect to MSA. The proposed models achieved an F1-score of 86.18 at the dialect identification task, securing second place in first subtask. Whereas for the machine translation task, the submitted model achieved a BLEU score of 11.37 securing fourth and third place in second and third subtask. The proposed model utilizes parameter efficient training methods which achieves better performance when compared to conventional fine-tuning during the experimentation phase.

Dialect Identification is important to improve the performance of various application as translation, speech recognition, etc. In this paper, we present our findings and results in the Nuanced Arabic Dialect Identification Shared Task (NADI 2022) for country-level dialect identification and sentiment identification for dialectical Arabic. The proposed model is an ensemble between fine-tuned BERT-based models and various approaches of prompt-tuning. Our model secured first place on the leaderboard for subtask 1 with an 27.06 F1-macro score, and subtask 2 secured first place with 75.15 F1-PN score. Our findings show that prompt-tuning-based models achieved better performance when compared to fine-tuning and Multi-task based methods. Moreover, using an ensemble of different loss functions might improve model performance.

This paper describes the system used in SemEval-2022 Task 6: Intended Sarcasm Detection in English and Arabic. Achieving 20th,3rd places with 34& 47 F1-Sarcastic score for task A, 16th place for task B with 0.0560 F1-macro score, and 10, 6th places for task C with72% and 80% accuracy on the leaderboard. A voting classifier between either multiple different BERT-based models or machine learningmodels is proposed, as our final model. Multiple key points has been extensively examined to overcome the problem of the unbalance ofthe dataset as: type of models, suitable architecture, augmentation, loss function, etc. In addition to that, we present an analysis of ourresults in this work, highlighting its strengths and shortcomings.

Irony and Sentiment detection is important to understand people’s behavior and thoughts. Thus it has become a popular task in natural language processing (NLP). This paper presents results and main findings in WANLP 2021 shared tasks one and two. The task was based on the ArSarcasm-v2 dataset (Abu Farha et al., 2021). In this paper, we describe our system Multi-headed-LSTM-CNN-GRU and also MARBERT (Abdul-Mageed et al., 2021) submitted for the shared task, ranked 10 out of 27 in shared task one achieving 0.5662 F1-Sarcasm and ranked 3 out of 22 in shared task two achieving 0.7321 F1-PN under CodaLab username “rematchka”. We experimented with various models and the two best performing models are a Multi-headed CNN-LSTM-GRU in which we used prepossessed text and emoji presented from tweets and MARBERT.