3 citations · 6 across the 8 of their papers we have counts for
6 papers · 2 filters
Long Range Named Entity Recognition for Marathi Documents
Pranita Deshmukh, Nikita Kulkarni, Sanhita Kulkarni +3
The demand for sophisticated natural language processing (NLP) methods, particularly Named Entity Recognition (NER), has increased due to the exponential growth of Marathi-language…
L3Cube-MahaSum: A Comprehensive Dataset and BART Models for Abstractive Text Summarization in Marathi
Pranita Deshmukh, Nikita Kulkarni, Sanhita Kulkarni +2
We present the MahaSUM dataset, a large-scale collection of diverse news articles in Marathi, designed to facilitate the training and evaluation of models for abstractive summariza…
A Data Selection Approach for Enhancing Low Resource Machine Translation Using Cross-Lingual Sentence Representations
Nidhi Kowtal, Tejas Deshpande, Raviraj Joshi
Machine translation in low-resource language pairs faces significant challenges due to the scarcity of parallel corpora and linguistic resources. This study focuses on the case of…
Chain-of-Translation Prompting (CoTR): A Novel Prompting Technique for Low Resource Languages
Tejas Deshpande, Nidhi Kowtal, Raviraj Joshi
This paper introduces Chain of Translation Prompting (CoTR), a novel strategy designed to enhance the performance of language models in low-resource languages. CoTR restructures pr…
Leveraging Parameter Efficient Training Methods for Low Resource Text Classification: A Case Study in Marathi
Pranita Deshmukh, Nikita Kulkarni, Sanhita Kulkarni +2
With the surge in digital content in low-resource languages, there is an escalating demand for advanced Natural Language Processing (NLP) techniques tailored to these languages. BE…
Curating Stopwords in Marathi: A TF-IDF Approach for Improved Text Analysis and Information Retrieval
Rohan Chavan, Gaurav Patil, Vishal Madle +1
Stopwords are commonly used words in a language that are often considered to be of little value in determining the meaning or significance of a document. These words occur frequent…