Poet Attribution of Urdu Ghazals using Deep Learning

Poet attribution focuses on determining ownership of a piece of poetry by insights obtained from analyzing his existing poetry. Its significance is immense including in detection of plagiarism and characterization of poetry of a poet. Urdu, Pakistan's lingua franca with the richest poetic tradition, has been a subject of misinformation and misattribution. This paper presents a novel approach to poet attribution in Urdu Ghazals through the application of machine and deep learning models. Our aim is to establish an accurate and comprehensive characterization of ghazals that captures the unique writing style of each poet. To achieve this, we trained and tested a range of machine learning, deep learning, and transformer-based classification models on a dataset containing 17,609 couplets of 15 notable ghazal poets. We used classifiers such as SVM and logistic regression to obtain preliminary results, achieving an accuracy of 64% with SVM. However, to achieve even better results, we employed deep learning models such as MLP, CNNs, and GRUs, with LSTMs resulting in the highest accuracy of 59.96%. We then used transformer-based models, including roBERTa and BERT, which achieved an outstanding accuracy of approximately 80% in classifying 15 poets. This work represents a significant contribution to the field of computational poetry analysis, as it is the first to explore poet attribution in Urdu Ghazals using deep learning and transformer-based models. Our analytical approach enables us to examine and analyze each model's capabilities in capturing the writing style of Urdu Ghazal poets, leading to a more comprehensive and accurate characterization of these works.