MahaParaphrase: A Marathi Paraphrase Detection Corpus and BERT-based Models
This work addresses the problem of limited NLP resources for Marathi speakers and researchers, though it is incremental as it applies existing methods to a new dataset.
The authors tackled the lack of annotated data for paraphrase detection in Marathi, a low-resource Indic language, by creating a high-quality corpus of 8,000 sentence pairs and evaluating BERT-based models on it.
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