This paper presents the CIC system for ArGuard 2026 Subtask B2, which classifies unsafe Arabic prompts into one of seven harm domains. Our system combines four complementary components: a fine-tuned CAMeLBERT-Mix model, a modified-Huber stochastic gradient descent classifier, a character-level nearest-neighbor model, and a character class-centroid similarity model. On the development set, we achieved a Macro-F1 score of 0.916 and an accuracy of 0.923. On the test set, the proposed model achieved an accuracy of 0.766, a macro precision of 0.760, a macro recall of 0.763, and a Macro-F1 of 0.751. The results show that combining contextual transformer representations, lexical features, and retrieval-based similarity signals can improve fine-grained harmful-prompt classification.

We present our approach in this submission for ArGuard 2026 Subtask B1, which classifies Arabic prompts directed at large language models as Safe or Unsafe. Our system combines four sparse TF-IDF feature representations with dialect indicators and surface-level features. We trained logistic regression and Naive-Bayes SVM (NBSVM) classifiers and combined their predictions using late fusion. The system achieved a Macro-F1 score of 0.94 and an accuracy of 0.96 on the development set. On the test set, our submission achieved 0.79 accuracy, 0.85 macro precision, 0.71 macro recall, and 0.72 Macro-F1.

The classification task for ArGuard 2026 Track A, Subtask A1, identifies whether an Arabic meme is Hateful or Not Hateful. Our system combines three separately trained models. In the first experiment, we use word-level and character-level features with balanced linear classifiers. In the second experiment, we fine- tune MARBERT for text classification using class-balanced focal loss. In the third experi- ment, we use a frozen SigLIP image encoder with balanced logistic regression. We combine the Class-1 probabilities from the three mod- els using late fusion. On the test set, the fused model achieved a Macro-F1 score of 0.76 and an accuracy of 0.79

The classification of Arabic prompts as Safe or Unsafe is the focus of this submission to ArGuard 2026 Subtask B1. We developed a lightweight hybrid classifier that combines lexical and semantic information. The lexical branch uses normalized word-level and character-level features with a class-balanced LinearSVC classifier. The semantic branch uses frozen multilingual E5 embeddings with a class-balanced logistic regression classifier. We combined the outputs of both branches using late fusion. On the development set, LinearSVC achieved a Macro-F1 score of 0.93, while the frozen E5 branch achieved 0.88. The final hybrid system achieved a Macro-F1 score of 0.93. On the test set, the submitted system achieved 0.78 accuracy, 0.83 precision, 0.70 recall, and 0.72 Macro-F1.

This paper presents a multi-step approach for multi-label emotion classification as our system description paper for the SEMEVAL-2025 workshop Task A using machine learning and deep learning models. We test our methodology on English, Spanish, and low-resource Yoruba datasets, with each dataset labeled with five emotion categories: anger, fear, joy, sadness, and surprise. Our preprocessing involves text cleaning and feature extraction using bigrams and TF-IDF. We employ logistic regression for baseline classification and fine-tune Transformer models, such as BERT and XLM-RoBERTa, for improved performance. The Transformer-based models outperformed the logistic regression model, achieving micro-F1 scores of 0.7061, 0.7321, and 0.2825 for English, Spanish, and Yoruba, respectively. Notably, our Yoruba fine-tuned model outperformed the baseline model of the task organizers with micro-F1 score of 0.092, demonstrating the effectiveness of Transformer models in handling emotion classification tasks across diverse languages.

As machine-generated texts (MGT) become increasingly similar to human writing, these dis- tinctions are harder to identify. In this paper, we as the CIC-NLP team present our submission to the Gen-AI Content Detection Workshop at COLING 2025 for Task 1 Subtask A, which involves distinguishing between text generated by LLMs and text authored by humans, with an emphasis on detecting English-only MGT. We applied the DistilBERT model to this binary classification task using the dataset provided by the organizers. Fine-tuning the model effectively differentiated between the classes, resulting in a micro-average F1-score of 0.70 on the evaluation test set. We provide a detailed explanation of the fine-tuning parameters and steps involved in our analysis.

Machine-written texts are gradually becoming indistinguishable from human-generated texts, leading to the need to use sophisticated methods to detect them. Team CIC-NLP presents work in the Gen-AI Content Detection Task 1 at COLING 2025 Workshop: the focus of our work is on Subtask B of Task 1, which is the classification of text written by machines and human authors, with particular attention paid to identifying multilingual binary classification problem. Usng mBERT, we addressed the binary classification task using the dataset provided by the GenAI Detection Task team. mBERT acchieved a macro-average F1-score of 0.72 as well as an accuracy score of 0.73.

Euphemisms are found across the world’s languages, making them a universal linguistic phenomenon. As such, euphemistic data may have useful properties for computational tasks across languages. In this study, we explore this premise by training a multilingual transformer model (XLM-RoBERTa) to disambiguate potentially euphemistic terms (PETs) in multilingual and cross-lingual settings. In line with current trends, we demonstrate that zero-shot learning across languages takes place. We also show cases where multilingual models perform better on the task compared to monolingual models by a statistically significant margin, indicating that multilingual data presents additional opportunities for models to learn about cross-lingual, computational properties of euphemisms. In a follow-up analysis, we focus on universal euphemistic “categories” such as death and bodily functions among others. We test to see whether cross-lingual data of the same domain is more important than within-language data of other domains to further understand the nature of the cross-lingual transfer.

Transformers have been shown to work well for the task of English euphemism disambiguation, in which a potentially euphemistic term (PET) is classified as euphemistic or non-euphemistic in a particular context. In this study, we expand on the task in two ways. First, we annotate PETs for vagueness, a linguistic property associated with euphemisms, and find that transformers are generally better at classifying vague PETs, suggesting linguistic differences in the data that impact performance. Second, we present novel euphemism corpora in three different languages: Yoruba, Spanish, and Mandarin Chinese. We perform euphemism disambiguation experiments in each language using multilingual transformer models mBERT and XLM-RoBERTa, establishing preliminary results from which to launch future work.

In this paper, we share our best performing submission to the Arabic AI Tasks Evaluation Challenge (ArAIEval) at ArabicNLP 2023. Our focus was on Task 1, which involves identifying persuasion techniques in excerpts from tweets and news articles. The persuasion technique in Arabic texts was detected using a training loop with XLM-RoBERTa, a language-agnostic text representation model. This approach proved to be potent, leveraging fine-tuning of a multilingual language model. In our evaluation of the test set, we achieved a micro F1 score of 0.64 for subtask A of the competition.

This paper describes our submissions for the Social Media Mining for Health (SMM4H) 2022 shared tasks. We participated in 2 tasks: a) Task 4: Classification of Tweets self-reporting exact age and b) Task 9: Classification of Reddit posts self-reporting exact age. We evaluated the two( BERT and RoBERTa) transformer based models for both tasks. For Task 4 RoBERTa-Large achieved an F1 score of 0.846 on the test set and BERT-Large achieved an F1 score of 0.865 on the test set for Task 9.