Applying convolutional attention mechanisms and Human Memory Search for effective English-Urdu translation

In recent decades, machine translation has become a prominent area of research, with the primary objective of overcoming language barriers. Early approaches primarily centered on word-for-word translation between source and target languages. However, there has been a shift towards data-driven models, such as neural machine translation and statistical methods , driven by advancements in Artificial Intelligence (AI), communication and computing technologies. This paper introduces a novel Convolutional Attention Mechanism-based Human Memory Search (CAM-HMS) algorithm for translating English into Urdu, to achieve high-quality and effective machine translation. The proposed model consists of several key phases, including pre-processing, sentence padding, word embedding , encoding, decoding, and target text generation. A new spider web-based search strategy is also incorporated to enhance the translation process in neural machine translation. The performance is evaluated using a UMC005 English-Urdu dataset, Parallel corpus for English & Urdu language, and the Roman Urdu to English Translation Dataset. Various automatic evaluation metrics such as the Bilingual Evaluation Understudy (BLEU), National Institute of Standards and Technology (NIST), Word Error Rate (WER), Accuracy, Precision, Recall, and F-Measure are used to assess the model's efficiency, and its output is compared to that of Google Translate. The proposed model achieves an average BLEU score of 82.14 %, NIST of 79.51 %, WER of 2.77 %, Accuracy of 98.99 %, Precision of 98.95 %, Recall of 98.90 %, and F-Measure of 98.92 %, demonstrating its effectiveness in producing high-quality machine translation results. • Introduces CAM-HMS for high-quality English-to-Urdu machine translation. • Incorporates a spider web-based search strategy to enhance translation accuracy. • Achieves 98.99 % accuracy and outperforms Google Translate in multiple metrics. • Evaluated on UMC005, Parallel Corpus, and Roman Urdu to English datasets. • Demonstrates superior BLEU, NIST, WER, precision, recall, and F-measure scores.