Authorship attribution is a critical task in natural language processing with applications ranging from forensic linguistics to plagiarism detection. While well-studied in high-resource languages, it remains challenging for low-resource languages like Arabic and Urdu. In this paper, we present our participation in the AbjadNLP shared task, where we systematically evaluate three distinct approaches: traditional machine learning using SVM with TF-IDF features, fine-tuned transformer-based models (AraBERT), and LLMs. We demonstrate that while fine-tuned AraBERT excels in Arabic, traditional lexical models (SVM) prove more robust for Urdu, outperforming both BERT-based and LLM approaches. We also show that few-shot prompting with LLMs, when operated as a reranker over top candidates, significantly outperforms zero-shot baselines. Our final systems achieved competitive performance, ranking 6th and 1st in the Arabic and Urdu tasks respectively.

Arabic, like other highly inflected languages, encodes a large amount of information in its morphology and word structure. In this work, we propose two embedding strategies that modify the tokenization phase of traditional word embedding models (Word2Vec) and contextual word embedding models (BERT) to take into account Arabic’s relatively complex morphology. In Word2Vec, we segment words into subwords during training time and then compose word-level representations from the subwords during test time. We train our embeddings on Arabic Wikipedia and show that they perform better than a Word2Vec model on multiple Arabic natural language processing datasets while being approximately 60% smaller in size. Moreover, we showcase our embeddings’ ability to produce accurate representations of some out-of-vocabulary words that were not encountered before. In BERT, we modify the tokenization layer of Google’s pretrained multilingual BERT model by incorporating information on morphology. By doing so, we achieve state of the art performance on two Arabic NLP datasets without pretraining.