Sentence Encoders

Sentence encoders are algorithms that transform sentences into numerical representations (embeddings) capturing their semantic meaning, enabling efficient comparison and analysis of text. Current research focuses on improving encoder robustness against adversarial attacks, developing multilingual and domain-specific encoders, and exploring novel training methods like contrastive learning and multi-modal approaches to enhance accuracy and efficiency. These advancements are crucial for various NLP applications, including information retrieval, question answering, and text classification, particularly in low-resource languages and specialized domains. The development of more robust and efficient sentence encoders is driving progress in numerous downstream tasks.