Paraphrases are a vital tool to assist language understanding tasks such as question answering, style transfer, semantic parsing, and data augmentation tasks. Indic languages are complex in natural language processing (NLP) due to their rich morphological and syntactic variations, diverse scripts, and limited availability of annotated data. In this work, we present the L3Cube-MahaParaphrase Dataset, a high-quality paraphrase corpus for Marathi, a low resource Indic language, consisting of 8,000 sentence pairs, each annotated by human experts as either Paraphrase (P) or Non-paraphrase (NP). We also present the results of standard transformer-based BERT models on these datasets. The dataset and model are publicly shared at https://github.com/l3cube-pune/MarathiNLP
@article{arxiv.2508.17444,
title = {MahaParaphrase: A Marathi Paraphrase Detection Corpus and BERT-based Models},
author = {Suramya Jadhav and Abhay Shanbhag and Amogh Thakurdesai and Ridhima Sinare and Ananya Joshi and Raviraj Joshi},
journal= {arXiv preprint arXiv:2508.17444},
year = {2025}
}