BERT
DeBERTa is a language model that builds upon the success of the Transformer architecture, specifically BERT (Bidirectional Encoder Representations from Transformers). DeBERTa stands for “Decoding-enhanced BERT with Disentangled Attention,” and it was developed by researchers at Microsoft Research Asia.
The main innovation of DeBERTa lies in its attention mechanism, which addresses some of the limitations of the original BERT model. BERT uses the self-attention mechanism, which allows the model to attend to different positions in the input sequence when encoding representations. However, the attention weights are shared across all layers and attention heads in BERT, which limits its ability to capture fine-grained dependencies
In contrast, DeBERTa introduces disentangled attention, where each layer and attention head has its own attention weights. This enables DeBERTa to model diverse types of dependencies and capture more fine-grained information within the input sequence. The disentangled attention mechanism allows the model to attend to different positions and capture different types of relationships in a more flexible and nuanced manner.
Furthermore, DeBERTa also incorporates two additional enhancements: cross-layer parameter sharing and masked disentangled self-attention. Cross-layer parameter sharing helps improve parameter efficiency by allowing lower layers to share parameters with higher layers, reducing the overall model size. Masked disentangled self-attention improves the model’s ability to handle tasks that involve partial information by allowing attention to be restricted to certain positions in the input.
Overall, DeBERTa achieves state-of-the-art performance on a wide range of natural language processing (NLP) tasks, including text classification, named entity recognition, question answering, and more. Its disentangled attention mechanism enables it to capture more intricate dependencies in text, leading to better representation learning and improved task performance.