Cross-Lingual Transfer Learning in RNNs for Enhancing Linguistic Diversity in Natural Language Processing

In addition to removing biases, these findings reveal a comprehensive method for improving cross-lingual transfer learning in NLP, which in turn makes language more diverse. This approach was created as a result of this effort. In order to construct the system utilizing these methodologies, five fundamental procedures are followed. One approach that falls under this category is MTI, which stands for Multilingual Embedding Adaptation and is used for Machine Translation-based Initialization. Ethical Bias Mitigation, Linguistic Feature Alignment, and Recurrent Neural Network Adaptation are some supplementary methods. Developing each strategy to address the wide variety of issues that may arise during cross-lingual transfer learning required considerable deliberation. Starting with translated data, the MTI algorithm aligns the source and target language embeddings, the RNA algorithm aligns RNN structures, the LFA algorithm aligns linguistic characteristics, and the EBM method lowers biases fairly. The desired outcomes are attained by employing all these techniques. The outcomes of ablation research demonstrated the significance of each approach and their interdependence in ensuring the system's functionality. The framework provides a comprehensive strategy for increasing the linguistic diversity of natural language processing (NLP) models to accommodate individuals from varying cultural backgrounds and language abilities. Issues of social justice and equity receive substantial consideration within the framework.