machine learning neural networks NLP multilingual processing transformer models sentiment analysis

The field of machine learning and natural language processing (NLP) has seen rapid advancements, yet challenges remain in developing robust models that can handle multilingual datasets effectively. This study aims to enhance neural network architectures to improve the performance of multilingual NLP tasks. We employed a combination of transformer-based models and recursive neural networks to develop a hybrid architecture. This approach was tested using a diverse set of linguistic corpora from five major languages, focusing on tasks such as sentiment analysis and language translation. Our findings indicate a significant improvement in accuracy and computational efficiency, outperforming existing models by an average of 12%. The hybrid model demonstrated an enhanced ability to generalize across languages, thereby reducing the need for language-specific preprocessing. In conclusion, this research provides a novel approach to multilingual NLP, potentially serving as a foundational framework for future developments in global language processing technologies.

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