Investigating Cyclic Translation for Urdu Text Reuse Detection using Deep and Transfer Learning Methods

Abstract

This research study addresses the complex problem of generating text reuse corpora using Machine Translation (MT), effectively detecting text reuse in paraphrased text through a cyclic translation approach and investigating the behavior of newly proposed deep learning and transfer learning methods on various Urdu text reuse corpora. Prior research endeavors addressing the challenge of text reuse paraphrase detection through the application of deep learning methodologies have predominantly focused on the English language. However, a comprehensive investigation into the problem of text reuse detection in the Urdu language, specifically utilizing a cyclic translation approach in the processing of data for training and testing purposes, alongside the utilization of deep learning methods, remains relatively limited. To tackle these challenges, a translated dataset is constructed by employing the Python Google Translate API on the Counter Dataset. Two variations of deep neural networks, namely Siamese Bidirectional LSTM (BiLSTM) and Manhattan LSTM (MaLSTM), are employed to achieve optimal accuracy in the multi-classification task. The training process incorporates two well-known optimizers, RMSProp (Root Mean Square Propagation) and Adam (Adaptive Moment Estimation). The experimental results substantiate the effectiveness of the Bidirectional LSTM, which achieves an accuracy of 77.22%, and the MALSTM, which attains an accuracy of 76.20%. These findings provide valuable insights for researchers and practitioners, paving the way for further advancements in the study of text reuse and its implications in diverse linguistic contexts