Review - ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

ALBERT, a lite version of the BERT model, offers a solution to memory and training time limitations faced by transformer-type models in Natural Language Processing. The paper proposes two parameter-reduction techniques - factorization of embedding parameters and cross-layer parameter sharing. Experiments show that ALBERT establishes new state-of-the-art results on various benchmarks, even with fewer parameters compared to BERT-large. Although ALBERT-xxlarge may have slower training speed due to its larger size, it still outperforms BERT-large when trained for the same amount of clock time. This research emphasizes that incrementing model size while reducing parameters can achieve state-of-the-art performance, offering a promising approach in limited GPU/TPU memory scenarios.