L3Cube-MahaNews: Largest Marathi News Classification Dataset with 12 Diverse Categories

Core Concepts

L3Cube-MahaNews is the largest supervised Marathi text classification dataset, containing over 1.05 million records across 12 diverse categories, designed for short text, long documents, and medium paragraphs.

Abstract

The L3Cube-MahaNews dataset is a comprehensive Marathi text classification corpus consisting of three sub-datasets: Short Headlines Classification (SHC): This dataset contains news article headlines with their corresponding categorical labels. Long Paragraph Classification (LPC): This dataset includes news article paragraphs with their categorical labels. Long Document Classification (LDC): This dataset comprises full news articles with their categorical labels. The dataset covers 12 diverse categories, including Auto, Bhakti, Crime, Education, Fashion, Health, International, Manoranjan, Politics, Sports, Tech, and Travel. This is the largest supervised Marathi text classification dataset available, with over 1.05 million records, significantly larger than the existing Marathi datasets. The authors provide baseline results using state-of-the-art pre-trained BERT models, including MahaBERT, IndicBERT, and MuRIL. The monolingual MahaBERT model outperforms the multilingual models on all three sub-datasets. The results demonstrate the need for diverse, high-quality Marathi datasets to support the development of advanced NLP models for this low-resource language.

Stats

The L3Cube-MahaNews dataset contains a total of 1,08,643 records derived from 27,525 news articles. The SHC and LDC datasets have 27,525 records each, while the LPC dataset has 53,593 records. The average word count per record is 12 for SHC, 150 for LPC, and 350 for LDC.

Quotes

"The availability of text or topic classification datasets in the low-resource Marathi language is limited, typically consisting of fewer than 4 target labels, with some achieving nearly perfect accuracy." "The monolingual MahaBERT model outperforms all others on every dataset."

Key Insights Distilled From

L3Cube-MahaNews: News-based Short Text and Long Document Classification Datasets in Marathi

by Saloni Mitta... at arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.18216.pdf

Deeper Inquiries

How can the L3Cube-MahaNews dataset be used to develop specialized Marathi language models for specific domains or applications

The L3Cube-MahaNews dataset can be instrumental in developing specialized Marathi language models for specific domains or applications by providing a robust foundation for training and fine-tuning. Researchers and developers can leverage the diverse range of 12 categories in the dataset to create domain-specific models tailored to areas such as technology, sports, politics, health, and more. By training models on this dataset, they can capture the nuances and intricacies of language usage within these domains, leading to more accurate and contextually relevant predictions. Furthermore, the availability of three sub-datasets - Short Headlines Classification (SHC), Long Document Classification (LDC), and Long Paragraph Classification (LPC) - allows for the development of models optimized for different text lengths. This flexibility enables the creation of specialized models that excel in processing short, medium, or long-form content, catering to specific application requirements. For instance, a model trained on the LPC dataset may be more suitable for applications that involve analyzing lengthy documents or articles, while a model trained on the SHC dataset may be better suited for processing news headlines or short texts. Overall, the L3Cube-MahaNews dataset serves as a valuable resource for building specialized Marathi language models that can enhance performance and accuracy in specific domains or applications through targeted training and fine-tuning.

What are the potential challenges in scaling the dataset to include more diverse content or languages beyond Marathi

Scaling the L3Cube-MahaNews dataset to include more diverse content or languages beyond Marathi poses several potential challenges that need to be addressed: Data Collection and Annotation: Expanding the dataset to include more diverse content or languages requires extensive data collection and annotation efforts. Curating high-quality data in multiple languages or domains can be time-consuming and resource-intensive. Cross-Linguistic Variability: Incorporating languages beyond Marathi introduces cross-linguistic variability, including differences in syntax, semantics, and linguistic structures. Ensuring the dataset captures these variations accurately is crucial for developing effective multilingual models. Domain Adaptation: Scaling the dataset to cover diverse domains necessitates domain adaptation techniques to ensure models generalize well across different subject areas. Domain-specific nuances and vocabulary need to be adequately represented in the dataset. Model Complexity: Handling a larger and more diverse dataset may increase the complexity of model training and evaluation. Ensuring scalability and efficiency in processing vast amounts of data is essential for practical implementation. Resource Constraints: Scaling the dataset may require significant computational resources for training and fine-tuning models on a larger and more diverse corpus. Managing these resources effectively is vital for maintaining model performance. Addressing these challenges involves a comprehensive approach that includes robust data collection strategies, effective annotation processes, domain-specific adaptation techniques, and efficient utilization of resources to scale the dataset successfully.

How can the insights from the comparative analysis of monolingual and multilingual BERT models be leveraged to improve Marathi NLP capabilities in the long run

The insights from the comparative analysis of monolingual and multilingual BERT models can be leveraged to improve Marathi NLP capabilities in the long run through the following strategies: Model Selection: Based on the comparative analysis results, researchers can identify the most effective model for Marathi NLP tasks. By understanding the performance differences between monolingual and multilingual models, they can choose the model that best suits their specific requirements. Fine-Tuning Strategies: Leveraging the findings, researchers can optimize fine-tuning strategies for Marathi language models. Understanding how different models perform on specific datasets can guide the fine-tuning process to enhance model accuracy and efficiency. Transfer Learning: Insights from the analysis can inform transfer learning approaches for Marathi NLP. By understanding the strengths and weaknesses of monolingual and multilingual models, researchers can design transfer learning pipelines that maximize the benefits of pre-trained models for Marathi language tasks. Dataset Augmentation: Researchers can use the comparative analysis results to guide dataset augmentation strategies. By incorporating diverse data sources and leveraging the strengths of different models, they can enhance the robustness and generalization capabilities of Marathi language models. Continuous Evaluation: Continuous evaluation of monolingual and multilingual models on Marathi NLP tasks is essential for ongoing improvement. By monitoring performance metrics and adapting strategies based on real-world performance, researchers can iteratively enhance Marathi NLP capabilities over time. By applying these insights strategically, the Marathi NLP community can optimize model selection, fine-tuning processes, transfer learning approaches, dataset augmentation strategies, and continuous evaluation to advance the state-of-the-art in Marathi language processing.