What is it about?
It is about applying a transfer learning approach to fine-tune pretrained language models in Nepali language to improve the accuracy of sentiment analysis in the Nepali language.
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Why is it important?
Improving sentiment analysis is very important because this is a classic text classification problem. Natural Language Processing for the Nepali language has not developed so much. We aim to bridge this gap of getting proper embeddings for a Nepali word by evaluating the performance of different pretrained language models in the Nepali language.
Perspectives
Evaluating the performance of pretrained language models for the Nepali language is very important because these pre-trained models can be used in different downstream NLP tasks.
Shushanta Pudasaini
Technological University Dublin
Read the Original
This page is a summary of: Application of Nepali Large Language Models to Improve Sentiment Analysis, January 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3647782.3647804.
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