Sentential Cross-lingual Paraphrase Detection for English-Urdu Language Pair

Abstract

Due to vast digital data collections and paraphrasing tools, researchers have shown growing interest in Cross-lingual Paraphrase Detection (CLPD). Open-access data and tools make paraphrasing easier and detection more challenging. Translation tools further exacerbate the issue by enabling effortless text translation across languages, leading to increased cross-lingual paraphrasing. Most existing CLPD studies focus on European languages, particularly English, while the English-Urdu language pair remains underexplored due to limited standard approaches and benchmark corpora.This study addresses this gap by developing the CLPD Corpus for English-Urdu (CLPD-EU), a gold-standard benchmark corpus at the sentence level. The corpus includes 5,801 sentence pairs, comprising 3,900 paraphrased and 1,901 non-paraphrased instances. Additionally, the study implements classical machine learning methods based on bilingual dictionaries, cross-lingual word embeddings, and transfer learning using sentence transformers.The research further incorporates state-of-the-art Large Language Models (LLMs) such as Mistral and LLaMA, significantly improving detection accuracy. Our proposed Feature Fusion Approach, ‘Comb-ST+BD,’ demonstrates strong performance with an F1 score of 0.739 for the CLPD task. The CLPD-EU corpus will be publicly available to encourage further research in CLPD, especially for under-resourced languages like Urdu.