MahaParaphrase: A Marathi Paraphrase Detection Corpus and BERT-based Models
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Abstract (arXiv)
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
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 15 of its 35 references that are in the map. First posted on arXiv 24 Aug 2025.
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References · 15
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- 2020Reformulating Unsupervised Style Transfer as Paraphrase Generationcited by 183
- 2021MuRIL: Multilingual Representations for Indian Languagescited by 179
- 2022L3Cube-MahaCorpus and MahaBERT: Marathi Monolingual Corpus, Marathi BERT Language Models, and Resourcescited by 51
- 2020AI4Bharat-IndicNLP Corpus: Monolingual Corpora and Word Embeddings for Indic Languagescited by 44
- 2020Paraphrase Augmented Task-Oriented Dialog Generationcited by 43
- 2022L3Cube-MahaNLP: Marathi Natural Language Processing Datasets, Models, and Librarycited by 31
- 2020Unsupervised Dual Paraphrasing for Two-stage Semantic Parsingcited by 20
- 2022BanglaParaphrase: A High-Quality Bangla Paraphrase Datasetcited by 15
- 2023L3Cube-IndicSBERT: A simple approach for learning cross-lingual sentence representations using multilingual BERTcited by 15
3 more
- 2024On Limitations of LLM as Annotator for Low Resource Languagescited by 12
- 2022L3Cube-MahaSBERT and HindSBERT: Sentence BERT Models and Benchmarking BERT Sentence Representations for Hindi and Marathicited by 10
- 2022ParaCotta: Synthetic Multilingual Paraphrase Corpora from the Most Diverse Translation Sample Paircited by 6
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