Authors - Soumen Halder, Subhamoy Bhaduri, Binayak Mukherjee Abstract - Paraphrasing is significant in applications that require controlled lexical variation to original text with semantic equivalence, especially in educational assessment systems where student answers should be scored on more than surface level matching. Recent transformer-based paraphrasing models do not exhibit regulated structural changes but instead generate uncontrolled changes, are costly in terms of computation, and are not feasible in low-resource or real-time implementations. These limitations are overcome by this work with a lightweight synonymreplacement paraphrasing framework on the basis of exclusive embedding clustering. The proposed EEC-SRP model groups semantically similar words into local embedding clouds and limits the search of synonyms to the tiny areas, which lowers the complexity of search considerably. An embedding augmentation algorithm involves perturbation to form embedding clusters and a neural network is trained to output contextually favorable synonym embeddings in those clusters. Strict semantic fidelity and controlled lexical substitution is ensured by the model by maintaining word count and sentence structure. Experimental analysis of standard paraphrasing tasks show that the suggested methodology attains high levels of semantic similarity, competitive levels of BLEU and ROUGE, and significantly quicker inference than conventional embedding-based and transformer-based models. The proposed model can be effectively implemented in automated assessment systems, controlled text rewriting and resource-constrained applications of natural language processing due to its low memory footprint and computational efficiency.
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