This paper presents a rule-based automated system for generating both Arabic verb conjugations and their corresponding Urdu translations. The system processes triliteral, non-weak Arabic roots across key tenses Past Simple, Past Simple Negative, Present Simple, and Present Simple Negative. Addressing the challenges posed by Arabic morphology, our rule-based approach applies patterns and morphological rules to accurately produce verb conjugations, capturing essential grammatical variations in gender, number, and person. Simultaneously, the system generates Urdu translations using predefined patterns that is aligned with the grammatical nuances of Arabic, ensuring semantic consistency. As the first system of its kind, it uniquely provides a cross-lingual resource that bridges two linguistically similar but distinct languages. By focusing on rule based precision and dual-language outputs, it addresses critical gaps in NLP resources, serving as a valuable tool for linguists, educators, and NLP researchers in academic and religious contexts where Arabic and Urdu coexist.

Euphemisms are a linguistic device used to soften discussions of sensitive or uncomfortable topics, with death being a prominent example. In this paper, we present a study on the detection of death-related euphemisms in historical literary texts from a corpus containing Danish and Norwegian novels from the late 19th century. We introduce an annotated dataset of euphemistic and literal references to death, including both common and rare euphemisms, ranging from well-established terms to more culturally nuanced expressions. We evaluate the performances of state-of-the-art pre-trained language models fine-tuned for euphemism detection. Our findings show that fixed, literal expressions of death became less frequent over time, while metaphorical euphemisms grew in prevalence. Additionally, euphemistic language was more common in historical novels, whereas contemporary novels tended to refer to death more literally, reflecting the rise of secularism. These results shed light on the shifting discourse on death during a period when the concept of death as final became prominent.

This paper investigates the use of large language models (LLMs) for financial causality detection in the FinCausal 2025 shared task, focusing on generative and multilingual question answering (QA) tasks. Our study employed both generative and discriminative approaches, utilizing GPT-4o for generative QA and BERT-base-multilingual-cased, XLM-RoBerta-large, and XLM-RoBerta-base for multilingual QA across English and Spanish datasets. The datasets consist of financial disclosures where questions reflect causal relationships, paired with extractive answers derived directly from the text. Evaluation was conducted using Semantic Answer Similarity (SAS) and Exact Match (EM) metrics. While the discriminative XLM-RoBerta-large model achieved the best overall performance, ranking 5th in English (SAS: 0.9598, EM: 0.7615) and 4th in Spanish (SAS: 0.9756, EM: 0.8084) among 11 team submissions, our results also highlight the effectiveness of the generative GPT-4o approach. Notably, GPT-4o achieved promising results in few-shot settings, with SAS scores approaching those of fine-tuned discriminative models, demonstrating that the generative approach can provide competitive performance despite lacking task-specific fine-tuning. This comparison underscores the potential of generative LLMs as robust, versatile alternatives for complex QA tasks like financial causality detection.

While pre-trained language models have made significant progress in different classification tasks, little attention has been given to the reliability of their confidence scores. Calibration, how well model confidence aligns with actual accuracy, is essential for real-world applications where decisions rely on probabilistic outputs. This study addresses this gap in Arabic dialect identification by assessing the calibration of eight pre-trained language models, ensuring their predictions are not only accurate but also reliable for practical applications. We analyze two datasets: one with over 1 million text samples and the Nuanced Arabic Dialect Identification dataset(NADI-2023). Using Expected Calibration Error (ECE) as a metric, we reveal substantial variation in model calibration across dialects in both datasets, showing that prediction confidence can vary significantly depending on regional data. This research has implications for improving the reliability of Arabic dialect models in applications like sentiment analysis and social media monitoring.

We present a framework for detecting and categorizing noise in literary texts, demonstrated through its application to Danish and Norwegian literature from the late 19-th century. Noise, understood as “aberrant sonic behaviour,” is not only an auditory phenomenon but also a cultural construct tied to the processes of civilization and urbanization.We begin by utilizing topic modeling techniques to identify noise-related documents, followed by fine-tuning BERT-based language models trained on Danish and Norwegian texts to analyze a corpus of over 800 novels.We identify and track the prevalence of noise in these texts, offering insights into the literary perceptions of noise during the Scandinavian “Modern Breakthrough” period (1870-1899). Our contributions include the development of a comprehensive dataset annotated for noise-related segments and their categorization into human-made, non-human-made, and musical noises. This study illustrates the framework’s potential for enhancing the understanding of the relationship between noise and its literary representations, providing a deeper appreciation of the auditory elements in literary works, including as sources for cultural history.

We develop and evaluate the first pre-trained language models specifically tailored for historical Danish and Norwegian texts. Three models are trained on a corpus of 19th-century Danish and Norwegian literature: two directly on the corpus with no prior pre-training, and one with continued pre-training. To evaluate the models, we utilize an existing sentiment classification dataset, and additionally introduce a new annotated word sense disambiguation dataset focusing on the concept of fate. Our assessment reveals that the model employing continued pre-training outperforms the others in two downstream NLP tasks on historical texts. Specifically, we observe substantial improvement in sentiment classification and word sense disambiguation compared to models trained on contemporary texts. These results highlight the effectiveness of continued pre-training for enhancing performance across various NLP tasks in historical text analysis.