Cybercrime is a serious and growing threat affecting millions of people worldwide. Detecting cybercrimes from text messages is challenging, as it requires understanding the linguistic and cultural nuances of different languages and regions. Roman Urdu is a widely used language in Pakistan and other South Asian countries, however, it lacks sufficient resources and tools for natural language processing and cybercrime detection. To address this problem, we make three main contributions in this paper. (1) We create and release CRU, a benchmark dataset for text-based cybercrime detection in Roman Urdu, which covers a number of cybercrimes as defined by the Prevention of Electronic Crimes Act (PECA) of Pakistan. This dataset is annotated by experts following a standardized procedure based on Pakistan’s legal framework. (2) We perform experiments on four pre-trained language models (PLMs) for cybercrime text classification in Roman Urdu. Our results show that xlm-roberta-base is the best model for this task, achieving the highest performance on all metrics. (3) We explore the utility of prompt engineering techniques, namely prefix and cloze prompts, for enhancing the performance of PLMs for low-resource languages such as Roman Urdu. We analyze the impact of different prompt shapes and k-shot settings on the performance of xlm-roberta-base and bert-base-multilingual-cased. We find that prefix prompts are more effective than cloze prompts for Roman Urdu classification tasks, as they provide more contextually relevant completions for the models. Our work provides useful insights and resources for future research on cybercrime detection and text classification in low-resource languages.

In this era of multimedia dominance, the surge of multimodal content on social media has transformed our methods of communication and information exchange. With the widespread use of multimedia content, the ability to effectively summarize this multimodal content is crucial for enhancing consumption, searchability, and retrieval. The scarcity of such training datasets has been a barrier to research in this area, especially for low-resource languages like Urdu. To address this gap, this paper introduces “UrduMASD”, a video-based Urdu multimodal abstractive text summarization dataset. The dataset contains 15,374 collections of videos, audio, titles, transcripts, and corresponding text summaries. To ensure the quality of the dataset, intrinsic evaluation metrics such as Abstractivity, Compression, Redundancy, and Semantic coherence have been employed. It was observed that our dataset surpasses existing datasets on numerous key quality metrics. Additionally, we present baseline results achieved using both text-based and state-of-the-art multimodal summarization models. On adding visual information, an improvement of 2.6% was observed in the ROUGE scores, highlighting the efficacy of utilizing multimodal inputs for summarization. To the best of our knowledge, this is the first dataset in Urdu that provides video-based multimodal data for abstractive text summarization, making it a valuable resource for advancing research in this field.

Recent advancements in natural language processing have demonstrated the efficacy of pre-trained language models for various downstream tasks through prompt-based fine-tuning. In contrast to standard fine-tuning, which relies solely on labeled examples, prompt-based fine-tuning combines a few labeled examples (few shot) with guidance through prompts tailored for the specific language and task. For low-resource languages, where labeled examples are limited, prompt-based fine-tuning appears to be a promising alternative. In this paper, we compare prompt-based and standard fine-tuning for the popular task of text classification in Urdu and Roman Urdu languages. We conduct experiments using five datasets, covering different domains, and pre-trained multilingual transformers. The results reveal that significant improvement of up to 13% in accuracy is achieved by prompt-based fine-tuning over standard fine-tuning approaches. This suggests the potential of prompt-based fine-tuning as a valuable approach for low-resource languages with limited labeled data.