Contextual embeddings address the limitation of traditional word embeddings (e.g., Word2Vec, GloVe) by creating word representations that vary depending on the context in which the word appears. In models like BERT (Bidirectional Encoder Representations from Transformers), each word or token is represented as a multi-dimensional vector, which is dynamically adjusted according to the words around it. This allows for deeper understanding of polysemy (words with multiple meanings) and improves performance in tasks like language modeling, translation, and question-answering. Unlike static embeddings, contextual embeddings enable models to capture nuances of meaning, such as the difference between "bank" in "river bank" and "financial bank" based on surrounding words. They are foundational to state-of-the-art natural language processing (NLP) systems, providing more accurate and human-like language comprehension.
The concept of contextual embeddings gained prominence in 2018 with the introduction of models like ELMo (Embeddings from Language Models) and BERT. While word embeddings had existed since early 2010s, the shift to contextual embeddings represented a major leap in NLP, enabling more sophisticated language models.
Key contributors include the teams behind ELMo (developed by AllenNLP researchers Matthew Peters et al. in 2018) and BERT (developed by Jacob Devlin and colleagues at Google in 2018). Their work significantly advanced the use of contextual embeddings in NLP.