Contextual Word Embeddings: A Review

Abstract

Contextual word embeddings have transformed natural language processing challenges by annexing the contextual meaning of words within sentences. This research paper provides a comprehensive reviewand analysis of contextual word embeddings, delving into their underlying principles, architectures, training methods, applications, and evaluation metrics. The paper discusses the evolution of contextual word embeddings from traditional word embeddings and delves into various prominent models, such as ELMo, GPT, BERT, and Transformer-XL. Additionally, the paper presents a critical analysis of the strengths and limitations of contextual word embeddings and highlights potential future directions for research in this field.