Argument mining for Arabic remains underexplored, largely due to the scarcity of annotated corpora. To address this gap, we examine the effectiveness of cross-lingual transfer from English. Using the English Persuasive Essays (PE) corpus, annotated with argumentative components (Major Claim, Claim, and Premise), we explore several transfer strategies: training encoder-based multilingual and monolingual models on English data, machine-translated Arabic data, and their combination. We further assess the impact of annotation noise introduced during translation by manually correcting portions of the projected training data. In addition, we investigate the potential of prompting large language models (LLMs) for the task. Experiments on a manually corrected Arabic test set show that monolingual models trained on translated data achieve the strongest performance, with further improvements from small-scale manual correction of training examples.

Psychological research has long suggested that storytelling can shape beliefs and behaviors by fostering emotional engagement and narrative transportation. However, it remains unclear whether these effects extend to online argumentative discourse. In this paper, we examine the role of narrative in real-world argumentation using discussions from the ChangeMyView subreddit. Leveraging an automatic story detection model, we analyze how narrative use varies across persuasive comments, user types, discussion outcomes, and the kinds of change being sought. While narrative appears more frequently in some contexts, it is not consistently linked to successful persuasion. Notably, highly persuasive users tend to use narrative less, and storytelling does not demonstrate increased effectiveness for any specific type of persuasive goals. These findings suggest that narrative may play a limited and context-dependent role in online discussions, highlighting the need for computational models of argumentation to account for rhetorical diversity.

This paper presents a new system for generating critical questions in debates, developed for the Critical Questions Generation shared task. Our two-stage approach, combining generation and classification, utilizes LLaMA 3.1 Instruct models (8B, 70B, 405B) with zero-/few-shot prompting. Evaluations on annotated debate data reveal several key insights: few-shot generation with 405B yielded relatively high-quality questions, achieving a maximum possible punctuation score of 73.5. The 70B model outperformed both smaller and larger variants on the classification part. The classifiers showed a strong bias toward labeling generated questions as Useful, despite limited validation. Further, our system, ranked 6 extsuperscriptth, out-performed baselines by 3%. These findings stress the effectiveness of large-sized models for question generation and medium-sized models for classification, and suggest the need for clearer task definitions within prompts to improve classification accuracy.

Propaganda significantly shapes public opinion, especially in conflict-driven contexts like the Israeli-Palestinian conflict. This study explores the integration of argumentation features, such as claims, premises, and major claims, into machine learning models to enhance the detection of propaganda techniques in Arabic media. By leveraging datasets annotated with fine-grained propaganda techniques and employing crosslingual and multilingual NLP methods, along with GPT-4-based annotations, we demonstrate consistent performance improvements. A qualitative analysis of Arabic media narratives on the Israeli war on Gaza further reveals the model’s capability to identify diverse rhetorical strategies, offering insights into the dynamics of propaganda. These findings emphasize the potential of combining NLP with argumentation features to foster transparency and informed discourse in politically charged settings.

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.

The IWSLT low-resource track encourages innovation in the field of speech translation, particularly in data-scarce conditions. This paper details our submission for the IWSLT 2024 low-resource track shared task for Maltese-English and North Levantine Arabic-English spoken language translation using an unconstrained pipeline approach. Using language models, we improve ASR performance by correcting the produced output. We present a 2 step approach for MT using data from external sources showing improvements over baseline systems. We also explore transliteration as a means to further augment MT data and exploit the cross-lingual similarities between Maltese and Arabic.