Demo: https://colab.research.google.com/drive/1Ha8UNpOwx87BrGCgtlZmO09tjnwkRv3N
The paper "Attention Is All You Need" introduces the Transformer model, a neural network architecture based entirely on attention mechanisms. The authors propose the Transformer as a replacement for recurrent neural networks (RNNs) and convolutional neural networks (CNNs) in sequence transduction tasks, such as machine translation.
The Transformer model utilizes self-attention mechanisms to capture global dependencies between words in a sentence. This allows the model to attend to different parts of the input sequence when generating the output. The self-attention mechanism computes attention weights for each word based on its relationship with other words in the sequence, enabling the model to focus on relevant information.
The authors also introduce multi-head attention, which allows the model to capture different types of information and learn different representation subspaces. By applying attention mechanisms multiple times in parallel, the model can capture diverse dependencies and improve its performance.
To incorporate sequential information, the paper proposes the use of positional encoding. Positional encoding provides the model with information about the order of words in the sequence, allowing it to consider the positional context when generating the output.
The authors demonstrate the effectiveness of the Transformer model on machine translation tasks, achieving state-of-the-art results on various language pairs. The model's attention-based approach offers parallelizable computations, enabling efficient training and inference on modern hardware.
Since its publication, the Transformer model has had a significant impact on the field of natural language processing (NLP). It has become the foundation for many state-of-the-art models in NLP tasks, such as language understanding, text generation, and sentiment analysis. The paper's contributions have spurred further research and advancements in attention-based models and have opened up new avenues for exploring the potential of attention mechanisms in neural networks.