Sentence Encoder

Sentence encoders are algorithms that transform sentences into numerical representations (embeddings) capturing their semantic meaning, enabling computers to understand and process text. Current research focuses on improving the accuracy and efficiency of these encoders, exploring architectures like Transformer-based models and contrastive learning methods, often incorporating multilingual capabilities and leveraging large datasets. This work is crucial for advancing numerous natural language processing applications, including semantic similarity tasks, information retrieval, and out-of-distribution detection, by providing more robust and nuanced semantic understanding. Ongoing efforts also address limitations in capturing subtle semantic properties and improving generalizability across diverse languages and datasets.