Abstract
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 this https URL
Abstract (translated)
paraphrases 是一种重要的工具，用于支持如问答、文体转换、语义解析以及数据增强等语言理解任务。由于其丰富的形态和句法变化、多样化的书写系统以及注释数据的有限性，印地语系语言在自然语言处理（NLP）中尤为复杂。在这项工作中，我们介绍了 L3Cube-MahaParaphrase 数据集，这是一个针对马拉地语（一种资源稀缺的印地语系语言）的高质量 paraphrase 语料库，包含8,000对句子，并由人工专家标注为“paraphrase”或“非 paraphrase”。此外，我们还展示了基于标准 transformer 的 BERT 模型在这些数据集上的表现。该数据集和模型已公开分享在此链接中（原链接用此URL代替）。
URL
https://arxiv.org/abs/2508.17444
