Email this article Transfer Learning for Low-Resource Natural Language Processing Tasks
Abstract
Natural Language Processing (NLP) models often require vast amounts of labeled data, posing challenges for low-resource languages and domains. This paper investigates the effectiveness of transfer learning in addressing these limitations. Pre-trained language models, such as BERT and GPT, are fine-tuned on low-resource datasets to perform tasks such as sentiment analysis, named entity recognition, and machine translation. Results demonstrate that transfer learning significantly improves performance, even with limited labeled data, highlighting its potential to democratize NLP advancements across diverse languages and applications.
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