To improve natural language understanding in multilingual contexts, computational linguistics can leverage deep learning models like BERT, GPT, and mBERT, which generate ...
To improve natural language understanding in multilingual contexts, computational linguistics can leverage deep learning models like BERT, GPT, and mBERT, which generate contextual embeddings for multiple languages. These models can capture semantic nuances and relationships across different languages, improving tasks like translation, sentiment analysis, and entity recognition.
To address linguistic diversity, researchers can focus on creating and fine-tuning models for low-resource languages by incorporating multilingual datasets and transfer learning techniques. As for bias, steps like bias detection, dataset balancing, and fairness constraints during model training can mitigate the perpetuation of social biases. Moreover, using domain-specific lexicons and incorporating diverse cultural contexts into training data can further reduce bias and enhance inclusivity, ensuring that natural language processing tools are more equitable across languages and cultures.