ALBERT

ALBERT is an upgrade to BERT that has made significant advancements in the field of natural language processing (NLP). Since the emergence of BERT, NLP research has adopted a new paradigm, leveraging large amounts of text for self-supervised model pretraining without the need for data annotation. ALBERT aims to improve upon this approach by understanding the factors that contribute to language-understanding performance. The key to ALBERT's design is to allocate the model's capacity more efficiently. It achieves this through factorization of the embedding parametrization, which splits the embedding matrix between input-level embeddings with a relatively low dimension and hidden-layer embeddings with higher dimensionalities. This results in an 80% reduction in the parameters of the projection block with only a minor drop in performance. Another critical design decision is to eliminate redundancy by parameter-sharing across layers. This approach slightly reduces accuracy but leads to a more compact model size. Implementing these two design changes together yields an ALBERT-base model with a significant parameter reduction while still achieving respectable performance. Moreover, by scaling up the size of the hidden-layer embeddings, ALBERT can achieve even better results. The ALBERT-xxlarge configuration demonstrates significant performance gains on benchmarks like SQuAD2.0 and RACE. The success of ALBERT highlights the importance of identifying and focusing on the aspects of a model that give rise to powerful contextual representations, which can greatly improve the model's efficiency and performance on a wide range of NLP tasks. Google is open-sourcing ALBERT to the research community to facilitate further advancements in the field.