Experimental Evaluation of Deep Learning models for Marathi Text Classification
This work addresses the need for NLP tools for Marathi speakers and researchers, but it is incremental as it applies existing methods to a new language context.
The paper tackles the problem of limited NLP research for Marathi text classification by evaluating deep learning models like CNN, LSTM, ULMFiT, and BERT on two public datasets, finding that basic CNN and LSTM models with FastText embeddings perform similarly to BERT models.
The Marathi language is one of the prominent languages used in India. It is predominantly spoken by the people of Maharashtra. Over the past decade, the usage of language on online platforms has tremendously increased. However, research on Natural Language Processing (NLP) approaches for Marathi text has not received much attention. Marathi is a morphologically rich language and uses a variant of the Devanagari script in the written form. This works aims to provide a comprehensive overview of available resources and models for Marathi text classification. We evaluate CNN, LSTM, ULMFiT, and BERT based models on two publicly available Marathi text classification datasets and present a comparative analysis. The pre-trained Marathi fast text word embeddings by Facebook and IndicNLP are used in conjunction with word-based models. We show that basic single layer models based on CNN and LSTM coupled with FastText embeddings perform on par with the BERT based models on the available datasets. We hope our paper aids focused research and experiments in the area of Marathi NLP.