ALBERT is an open-source, lite version of BERT that improves state-of-the-art performance on 12 NLP tasks with significantly fewer parameters.
ALBERT (A Lite BERT) is an upgrade to the BERT model for self-supervised learning of language representations. It achieves state-of-the-art performance on 12 Natural Language Processing (NLP) tasks, including SQuAD v2.0 and the RACE benchmark, while significantly reducing the number of model parameters. This efficiency is gained through two main design changes: factorization of the embedding parametrization and parameter-sharing across layers. The factorization of embedding parametrization splits the embedding matrix, using lower dimensions for input-level embeddings and higher dimensions for hidden-layer embeddings. This alone reduces parameters by 80% in the projection block with a minor performance drop. Parameter sharing across layers eliminates redundancy by applying the same layer repeatedly, leading to a 90% parameter reduction for the attention-feedforward block. Combined, these changes result in an ALBERT-base model with 89% fewer parameters than BERT-base, while maintaining respectable performance. The reduced parameter size allows for scaling up the hidden-layer embeddings. The ALBERT-xxlarge configuration, with a hidden-size of 4096, achieves a 30% parameter reduction compared to BERT-large and significant performance gains, such as +4.2 on SQuAD2.0 and +8.5 on RACE. When trained on larger datasets, ALBERT-xxlarge sets a new state-of-the-art score of 89.4 on the RACE challenge, demonstrating its capability for robust, high-capacity contextual representations.
Integrations: TensorFlow
Platforms: Web
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