Embedding Models: A Comprehensive Review with Task-Oriented Assessment

Sentence embedding is a very important technique in most natural language processing (NLP) tasks, such as answer generation, semantic similarity detection, text classification and information retrieval. This technique aims to transform the semantic meaning of a sentence into a fixed-dimensional vector, allowing machines to understand human language. Sentence embedding has moved in recent years from simple word vector averaging methods to the development of more sophisticated models, particularly those based on transformer structures such as the BERT model and its variants. However, systematic reviews that critical, analyze and compare the performance of these models are still limited, particularly the selection of the appropriate embedding model for a specific NLP task. This study aims to address this gap by a comprehensive review for sentence embedding models and a systematic evaluation of their performance on NLP tasks, such as semantic similarity, clustering, and retrieval. The study enabled us to identify the appropriate embedding model for each task, identify the main challenges faced by embedding models, and propose effective solutions to improve the performance and efficiency of sentence embedding.