Sentence Similarity

Sentence similarity research aims to computationally measure the semantic relatedness between sentences, crucial for various natural language processing applications. Current research focuses on improving the accuracy and robustness of sentence embedding models, often employing transformer architectures like BERT, enhanced with techniques such as contrastive learning, mixture of experts, and optimal transport methods, to capture nuanced semantic relationships. These advancements are driving progress in tasks like information retrieval, text summarization, and machine translation, by enabling more accurate and efficient processing of textual data. Furthermore, research is actively exploring methods for improving interpretability and addressing challenges like handling typos and domain-specific language.