Multilingual Sentence

Multilingual sentence representation research focuses on creating computational models that understand and compare sentences across multiple languages, aiming to bridge linguistic barriers in tasks like machine translation and cross-lingual information retrieval. Current efforts concentrate on improving model architectures like multilingual BERT and Sentence-BERT, often employing techniques such as contrastive learning and cross-lingual consistency regularization to enhance the quality and robustness of these representations. This field is crucial for advancing natural language processing applications in diverse languages, particularly those with limited resources, and for mitigating biases that can arise from skewed training data.