GenAI4ELab/papercli-papers huggingface dataset:
AlexuNLP24 at AraFinNLP2024: Multi-Dialect Arabic Intent Detection with Contrastive Learning in Banking Domain
Authors: Hossam Elkordi; Ahmed Sakr; Marwan Torki; Nagwa El-Makky
Venue: ACL 2024 (Proceedings of the Second Arabic Natural Language Processing Conference)

Abstract: Arabic banking intent detection represents a challenging problem across multiple dialects. It imposes generalization difficulties due to the scarcity of Arabic language and its dialects resources compared to English. We propose a methodology that leverages contrastive training to overcome this limitation. We also augmented the data with several dialects using a translation model. Our experiments demonstrate the ability of our approach in capturing linguistic nuances across different Arabic dialects as well as accurately differentiating between banking intents across diverse linguistic landscapes. This would enhance multi-dialect banking services in the Arab world with limited Arabic language resources. Using our proposed method we achieved second place on subtask 1 leaderboard of the AraFinNLP2024 shared task with micro-F1 score of 0.8762 on the test split.
SemanticCuetSync at AraFinNLP2024: Classification of Cross-Dialect Intent in the Banking Domain using Transformers
Authors: Ashraful Paran; Symom Shohan; Md. Hossain; Jawad Hossain; Shawly Ahsan; Mohammed Moshiul Hoque
Venue: ACL 2024 (Proceedings of the Second Arabic Natural Language Processing Conference)

Abstract: Intention detection is a crucial aspect of natural language understanding (NLU), focusing on identifying the primary objective underlying user input. In this work, we present a transformer-based method that excels in determining the intent of Arabic text within the banking domain. We explored several machine learning (ML), deep learning (DL), and transformer-based models on an Arabic banking dataset for intent detection. Our findings underscore the challenges that traditional ML and DL models face in understanding the nuances of various Arabic dialects, leading to subpar performance in intent detection. However, the transformer-based methods, designed to tackle such complexities, significantly outperformed the other models in classifying intent across different Arabic dialects. Notably, the AraBERTv2 model achieved the highest micro F1 score of 82.08% in ArBanking77 dataset, a testament to its effectiveness in this context. This achievement, which contributed to our work being ranked 5th in the shared task, AraFinNLP2024, highlights the importance of developing models that can effectively handle the intricacies of Arabic language processing and intent detection.
SENIT at AraFinNLP2024: trust your model or combine two
Authors: Abdelmomen Nasr; Moez Ben HajHmida
Venue: ACL 2024 (Proceedings of the Second Arabic Natural Language Processing Conference)

Abstract: We describe our submitted system to the 2024 Shared Task on The Arabic Financial NLP (Malaysha et al., 2024). We tackled Subtask 1, namely Multi-dialect Intent Detection. We used state-of-the-art pretrained contextualized text representation models and fine-tuned them according to the downstream task at hand. We started by finetuning multilingual BERT and various Arabic variants, namely MARBERTV1, MARBERTV2, and CAMeLBERT. Then, we employed an ensembling technique to improve our classification performance combining MARBERTV2 and CAMeLBERT embeddings. The findings indicate that MARBERTV2 surpassed all the other models mentioned.
BabelBot at AraFinNLP2024: Fine-tuning T5 for Multi-dialect Intent Detection with Synthetic Data and Model Ensembling
Authors: Murhaf Fares; Samia Touileb
Venue: ACL 2024 (Proceedings of the Second Arabic Natural Language Processing Conference)

Abstract: This paper presents our results for the Arabic Financial NLP (AraFinNLP) shared task at the Second Arabic Natural Language Processing Conference (ArabicNLP 2024). We participated in the first sub-task, Multi-dialect Intent Detection, which focused on cross-dialect intent detection in the banking domain. Our approach involved fine-tuning an encoder-only T5 model, generating synthetic data, and model ensembling. Additionally, we conducted an in-depth analysis of the dataset, addressing annotation errors and problematic translations. Our model was ranked third in the shared task, achieving a F1-score of 0.871.
MA at AraFinNLP2024: BERT-based Ensemble for Cross-dialectal Arabic Intent Detection
Authors: Asmaa Ramadan; Manar Amr; Marwan Torki; Nagwa El-Makky
Venue: ACL 2024 (Proceedings of the Second Arabic Natural Language Processing Conference)

Abstract: Intent detection, also called intent classification or recognition, is an NLP technique to comprehend the purpose behind user utterances. This paper focuses on Multi-dialect Arabic intent detection in banking, utilizing the ArBanking77 dataset. Our method employs an ensemble of fine-tuned BERT-based models, integrating contrastive loss for training. To enhance generalization to diverse Arabic dialects, we augment the ArBanking77 dataset, originally in Modern Standard Arabic (MSA) and Palestinian, with additional dialects such as Egyptian, Moroccan, and Saudi, among others. Our approach achieved an F1-score of 0.8771, ranking first in subtask-1 of the AraFinNLP shared task 2024.
BFCI at AraFinNLP2024: Support Vector Machines for Arabic Financial Text Classification
Authors: Nsrin Ashraf; Hamada Nayel; Mohammed Aldawsari; Hosahalli Shashirekha; Tarek Elshishtawy
Venue: ACL 2024 (Proceedings of the Second Arabic Natural Language Processing Conference)

Abstract: In this paper, a description of the system submitted by BFCAI team to the AraFinNLP2024 shared task has been introduced. Our team participated in the first subtask, which aims at detecting the customer intents of cross-dialectal Arabic queries in the banking domain. Our system follows the common pipeline of text classification models using primary classification algorithms integrated with basic vectorization approach for feature extraction. Multi-layer Perceptron, Stochastic Gradient Descent and Support Vector Machines algorithms have been implemented and support vector machines outperformed all other algorithms with an f-score of 49%. Our submission’s result is appropriate compared to the simplicity of the proposed model’s structure.
dzFinNlp at AraFinNLP: Improving Intent Detection in Financial Conversational Agents
Authors: Mohamed Lichouri; Khaled Lounnas; Amziane Zakaria
Venue: ACL 2024 (Proceedings of the Second Arabic Natural Language Processing Conference)

Abstract: In this paper, we present our dzFinNlp team’s contribution for intent detection in financial conversational agents, as part of the AraFinNLP shared task. We experimented with various models and feature configurations, including traditional machine learning methods like LinearSVC with TF-IDF, as well as deep learning models like Long Short-Term Memory (LSTM). Additionally, we explored the use of transformer-based models for this task. Our experiments show promising results, with our best model achieving a micro F1-score of 93.02% and 67.21% on the ArBanking77 dataset, in the development and test sets, respectively.
ArAIEval Shared Task: Propagandistic Techniques Detection in Unimodal and Multimodal Arabic Content
Authors: Maram Hasanain; Md. Arid Hasan; Fatema Ahmad; Reem Suwaileh; Md. Rafiul Biswas; Wajdi Zaghouani; Firoj Alam
Venue: ACL 2024 (Proceedings of the Second Arabic Natural Language Processing Conference)

Abstract: We present an overview of the second edition of the ArAIEval shared task, organized as part of the ArabicNLP 2024 conference co-located with ACL 2024. In this edition, ArAIEval offers two tasks: (i) detection of propagandistic textual spans with persuasion techniques identification in tweets and news articles, and (ii) distinguishing between propagandistic and non-propagandistic memes. A total of 14 teams participated in the final evaluation phase, with 6 and 9 teams participating in Tasks 1 and 2, respectively. Finally, 11 teams submitted system description papers. Across both tasks, we observed that fine-tuning transformer models such as AraBERT was at the core of the majority of the participating systems. We provide a description of the task setup, including a description of the dataset construction and the evaluation setup. We further provide a brief overview of the participating systems. All datasets and evaluation scripts are released to the research community. We hope this will enable further research on these important tasks in Arabic.
MemeMind at ArAIEval Shared Task: Generative Augmentation and Feature Fusion for Multimodal Propaganda Detection in Arabic Memes through Advanced Language and Vision Models
Authors: Uzair Shah; Md. Rafiul Biswas; Marco Agus; Mowafa Househ; Wajdi Zaghouani
Venue: ACL 2024 (Proceedings of the Second Arabic Natural Language Processing Conference)

Abstract: Detecting propaganda in multimodal content, such as memes, is crucial for combating disinformation on social media. This paper presents a novel approach for the ArAIEval 2024 shared Task 2 on Multimodal Propagandistic Memes Classification, involving text, image, and multimodal classification of Arabic memes. For text classification (Task 2A), we fine-tune state-of-the-art Arabic language models and use ChatGPT4-generated synthetic text for data augmentation. For image classification (Task 2B), we fine-tune ResNet18, EfficientFormerV2, and ConvNeXt-tiny architectures with DALL-E-2-generated synthetic images. For multimodal classification (Task 2C), we combine ConvNeXt-tiny and BERT architectures in a fusion layer to enhance binary classification. Our results show significant performance improvements with data augmentation for text and image classification models and with the fusion layer for multimodal classification. We highlight challenges and opportunities for future research in multimodal propaganda detection in Arabic content, emphasizing the need for robust and adaptable models to combat disinformation.
ASOS at ArAIEval Shared Task: Integrating Text and Image Embeddings for Multimodal Propaganda Detection in Arabic Memes
Authors: Yasser Alhabashi; Abdullah Alharbi; Samar Ahmad; Serry Sibaee; Omer Nacar; Lahouari Ghouti; Anis Koubaa
Venue: ACL 2024 (Proceedings of the Second Arabic Natural Language Processing Conference)

Abstract: This paper describes our participation in the ArAIEval Shared Task 2024, focusing on Task 2C, which challenges participants to detect propagandistic elements in multimodal Arabic memes. The challenge involves analyzing both the textual and visual components of memes to identify underlying propagandistic messages. Our approach integrates the capabilities of MARBERT and ResNet50, top-performing pre-trained models for text and image processing, respectively. Our system architecture combines these models through a fusion layer that integrates and processes the extracted features, creating a comprehensive representation that is more effective in detecting nuanced propaganda. Our proposed system achieved significant success, placing second with an F1 score of 0.7987.
Mela at ArAIEval Shared Task: Propagandistic Techniques Detection in Arabic with a Multilingual Approach
Authors: Md Abdur Razzaq Riyadh; Sara Nabhani
Venue: ACL 2024 (Proceedings of the Second Arabic Natural Language Processing Conference)

Abstract: This paper presents our system submitted for Task 1 of the ArAIEval Shared Task on Unimodal (Text) Propagandistic Technique Detection in Arabic. Task 1 involves identifying all employed propaganda techniques in a given text from a set of possible techniques or detecting that no propaganda technique is present. Additionally, the task requires identifying the specific spans of text where these techniques occur. We explored the capabilities of a multilingual BERT model for this task, focusing on the effectiveness of using outputs from different hidden layers within the model. By fine-tuning the multilingual BERT, we aimed to improve the model’s ability to recognize and locate various propaganda techniques. Our experiments showed that leveraging the hidden layers of the BERT model enhanced detection performance. Our system achieved competitive results, ranking second in the shared task, demonstrating that multilingual BERT models, combined with outputs from hidden layers, can effectively detect and identify spans of propaganda techniques in Arabic text.
MODOS at ArAIEval Shared Task: Multimodal Propagandistic Memes Classification Using Weighted SAM, CLIP and ArabianGPT
Authors: Abdelhamid Haouhat; Hadda Cherroun; Slimane Bellaouar; Attia Nehar
Venue: ACL 2024 (Proceedings of the Second Arabic Natural Language Processing Conference)

Abstract: Arabic social media platforms are increasingly using propaganda to deceive or influence people. This propaganda is often spread through multimodal content, such as memes. While substantial research has addressed the automatic detection of propaganda in English content, this paper presents the MODOS team’s participation in the Arabic Multimodal Propagandistic Memes Classification shared task. Our system deploys the Segment Anything Model (SAM) and CLIP for image representation and ARABIAN-GPT embeddings for text. Then, we employ LSTM encoders followed by a weighted fusion strategy to perform binary classification. Our system achieved competitive performance in distinguishing between propagandistic and non-propagandistic memes, scored 0.7290 macro F1, and ranked 6th among the participants.
Nullpointer at ArAIEval Shared Task: Arabic Propagandist Technique Detection with Token-to-Word Mapping in Sequence Tagging
Authors: Abrar Abir; Kemal Oflazer
Venue: ACL 2024 (Proceedings of the Second Arabic Natural Language Processing Conference)

Abstract: This paper investigates the optimization of propaganda technique detection in Arabic text, including tweets & news paragraphs, from ArAIEval shared task 1. Our approach involves fine-tuning the AraBERT v2 model with a neural network classifier for sequence tagging.Experimental results show relying on the first token of the word for technique prediction produces the best performance. In addition, incorporating genre information as a feature further enhances the model’s performance. Our system achieved a score of 25.41, placing us 4th on the leaderboard. Subsequent post-submission improvements further raised our score to 26.68.
MemeMind at ArAIEval Shared Task: Spotting Persuasive Spans in Arabic Text with Persuasion Techniques Identification
Authors: Md. Rafiul Biswas; Zubair Shah; Wajdi Zaghouani
Venue: ACL 2024 (Proceedings of the Second Arabic Natural Language Processing Conference)

Abstract: This paper focuses on detecting propagandistic spans and persuasion techniques in Arabic text from tweets and news paragraphs. Each entry in the dataset contains a text sample and corresponding labels that indicate the start and end positions of propaganda techniques within the text. Tokens falling within a labeled span were assigned ’B’ (Begin) or ’I’ (Inside) tags, ’O’, corresponding to the specific propaganda technique. Using attention masks, we created uniform lengths for each span and assigned BIO tags to each token based on the provided labels. Then, we used AraBERT-base pre-trained model for Arabic text tokenization and embeddings with a token classification layer to identify propaganda techniques. Our training process involves a two-phase fine-tuning approach. First, we train only the classification layer for a few epochs, followed by full model fine-tuning, updating all parameters. This methodology allows the model to adapt to the specific characteristics of the propaganda detection task while leveraging the knowledge captured by the pretrained AraBERT model. Our approach achieved an F1 score of 0.2774, securing the 3rd position in the leaderboard of Task 1.
CLTL at ArAIEval Shared Task: Multimodal Propagandistic Memes Classification Using Transformer Models
Authors: Yeshan Wang; Ilia Markov
Venue: ACL 2024 (Proceedings of the Second Arabic Natural Language Processing Conference)

Abstract: We present the CLTL system designed for the ArAIEval Shared Task 2024 on multimodal propagandistic memes classification in Arabic. The challenge was divided into three subtasks: identifying propagandistic content from textual modality of memes (subtask 2A), from visual modality of memes (subtask 2B), and in a multimodal scenario when both modalities are combined (subtask 2C). We explored various unimodal transformer models for Arabic language processing (subtask 2A), visual models for image processing (subtask 2B), and concatenated text and image embeddings using the Multilayer Perceptron fusion module for multimodal propagandistic memes classification (subtask 2C). Our system achieved 77.96% for subtask 2A, 71.04% for subtask 2B, and 79.80% for subtask 2C, ranking 2nd, 1st, and 3rd on the leaderboard.
CUET_sstm at ArAIEval Shared Task: Unimodal (Text) Propagandistic Technique Detection Using Transformer-Based Model
Authors: Momtazul Labib; Samia Rahman; Hasan Murad; Udoy Das
Venue: ACL 2024 (Proceedings of the Second Arabic Natural Language Processing Conference)

Abstract: In recent days, propaganda has started to influence public opinion increasingly as social media usage continues to grow. Our research has been part of the first challenge, Unimodal (Text) Propagandistic Technique Detection of ArAIEval shared task at the ArabicNLP 2024 conference, co-located with ACL 2024, identifying specific Arabic text spans using twenty-three propaganda techniques. We have augmented underrepresented techniques in the provided dataset using synonym replacement and have evaluated various machine learning (RF, SVM, MNB), deep learning (BiLSTM), and transformer-based models (bert-base-arabic, Marefa-NER, AraBERT) with transfer learning. Our comparative study has shown that the transformer model “bert-base-arabic” has outperformed other models. Evaluating the test set, it has achieved the micro-F1 score of 0.2995 which is the highest. This result has secured our team “CUET_sstm” first place among all participants in task 1 of the ArAIEval.
AlexUNLP-MZ at ArAIEval Shared Task: Contrastive Learning, LLM Features Extraction and Multi-Objective Optimization for Arabic Multi-Modal Meme Propaganda Detection
Authors: Mohamed Zaytoon; Nagwa El-Makky; Marwan Torki
Venue: ACL 2024 (Proceedings of the Second Arabic Natural Language Processing Conference)

Abstract: The rise of memes as a tool for spreading propaganda presents a significant challenge in the current digital environment. In this paper, we outline our work for the ArAIEval Shared Task2 in ArabicNLP 2024. This study introduces a method for identifying propaganda in Arabic memes using a multimodal system that combines textual and visual indicators to enhance the result. Our approach achieves the first place in text classification with Macro-F1 of 78.69%, the third place in image classification with Macro-F1 of 65.92%, and the first place in multimodal classification with Macro-F1 of 80.51%
SemanticCuetSync at ArAIEval Shared Task: Detecting Propagandistic Spans with Persuasion Techniques Identification using Pre-trained Transformers
Authors: Symom Shohan; Md. Hossain; Ashraful Paran; Shawly Ahsan; Jawad Hossain; Mohammed Moshiul Hoque
Venue: ACL 2024 (Proceedings of the Second Arabic Natural Language Processing Conference)

Abstract: Detecting propagandistic spans and identifying persuasion techniques are crucial for promoting informed decision-making, safeguarding democratic processes, and fostering a media environment characterized by integrity and transparency. Various machine learning (Logistic Regression, Random Forest, and Multinomial Naive Bayes), deep learning (CNN, CNN+LSTM, CNN+BiLSTM), and transformer-based (AraBERTv2, AraBERT-NER, CamelBERT, BERT-Base-Arabic) models were exploited to perform the task. The evaluation results indicate that CamelBERT achieved the highest micro-F1 score (24.09%), outperforming CNN+LSTM and AraBERTv2. The study found that most models struggle to detect propagandistic spans when multiple spans are present within the same article. Overall, the model’s performance secured a 6th place ranking in the ArAIEval Shared Task-1.
SussexAI at ArAIEval Shared Task: Mitigating Class Imbalance in Arabic Propaganda Detection
Authors: Mary Fouad; Julie Weeds
Venue: ACL 2024 (Proceedings of the Second Arabic Natural Language Processing Conference)

Abstract: In this paper, we are exploring mitigating class imbalancein Arabic propaganda detection. Given amultigenre text which could be a news paragraphor a tweet, the objective is to identify the propagandatechnique employed in the text along withthe exact span(s) where each technique occurs. Weapproach this task as a sequence tagging task. Weutilise AraBERT for sequence classification andimplement data augmentation and random truncationmethods to mitigate the class imbalance withinthe dataset. We demonstrate the importance ofconsidering macro-F1 as well as micro-F1 whenevaluating classifier performance in this scenario.
The FIGNEWS Shared Task on News Media Narratives
Authors: Wajdi Zaghouani; Mustafa Jarrar; Nizar Habash; Houda Bouamor; Imed Zitouni; Mona Diab; Samhaa El-Beltagy; Muhammed AbuOdeh
Venue: ACL 2024 (Proceedings of the Second Arabic Natural Language Processing Conference)

Abstract: We present an overview of the FIGNEWSshared task, organized as part of the Arabic-NLP 2024 conference co-located with ACL2024. The shared task addresses bias and pro-paganda annotation in multilingual news posts.We focus on the early days of the Israel War onGaza as a case study. The task aims to fostercollaboration in developing annotation guide-lines for subjective tasks by creating frame-works for analyzing diverse narratives high-lighting potential bias and propaganda. In aspirit of fostering and encouraging diversity,we address the problem from a multilingualperspective, namely within five languages: En-glish, French, Arabic, Hebrew, and Hindi. Atotal of 17 teams participated in two annota-tion subtasks: bias (16 teams) and propaganda(6 teams). The teams competed in four evalua-tion tracks: guidelines development, annotationquality, annotation quantity, and consistency.Collectively, the teams produced 129,800 datapoints. Key findings and implications for thefield are discussed.
Narrative Navigators at FIGNEWS 2024 Shared Task: New Frontiers in Bias and Propaganda Annotation Techniques
Authors: Maryam AlEmadi; Jana ElMesselmani; Lyna Bermak; Goumana Abdullah; Esra’a Sharqawi; Anissa Jrad; Zied Zouabi; Wajdi Zaghouani
Venue: ACL 2024 (Proceedings of the Second Arabic Natural Language Processing Conference)

Abstract: This paper presents our team’s contribution to the FIGNEWS 2024 Shared Task, which involved annotating bias and propaganda in news coverage of the Israel-Palestine conflict. We developed comprehensive guidelines and employed a rigorous methodology to analyze 2,200 news posts from several official Facebook accounts of news websites in multiple languages. Our team, Narrative Navigators, achieved third place in both the Bias Guidelines and Bias Consistency tracks, demonstrating the effectiveness of our approach. We achieved an IAA Kappa score of 39.4 for bias annotation and 12.8 for propaganda detection. These findings and our performance underscore the need for enhanced media literacy and further research to counter the impact of biased and misleading information on public understanding of the conflict.