The Transformer network, introduced in the paper "Attention Is All You Need" by Vaswani et al. (2017), represented a significant shift in how sequence-to-sequence tasks were approached in natural language processing. Here are several reasons why Transformer networks exhibit advantages over traditional Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs):
Parallelization:
- Unlike RNNs, which inherently process sequences step by step, Transformers process entire sequences simultaneously. This allows for greater parallelization during training and inference, leading to significant speedups, especially on modern hardware designed for parallel computations, such as GPUs and TPUs.
Long-range dependencies:
- Transformers utilize a self-attention mechanism that directly computes relationships between all tokens in a sequence, regardless of their distance from each other. This enables the model to capture long-range dependencies more effectively than RNNs, which may struggle with such dependencies due to vanishing and exploding gradient problems.
Scalability:
- The scalability of Transformers is superior because they avoid sequential computation. This is beneficial not only for training but also for handling large-scale datasets and long input sequences.
Reduced computation complexity for long sequences:
- While CNNs can process input in parallel, they typically require stacking many layers or using large dilation factors to aggregate information from distant parts of the input, which can become computationally expensive. Transformers can understand the full context of the input in a single layer with their attention mechanism.
Flexibility and adaptability:
- The self-attention mechanism is simpler and more flexible than the recurrent structure of RNNs and the local receptive fields of CNNs. This allows Transformers to adapt to various types of data and tasks more effectively.
Dynamic attention:
- The attention mechanism in Transformers can weigh different parts of the input differently, allowing the model to focus more on relevant parts of the data for a particular task. This dynamic weighting is less natural in CNNs and RNNs.
Better gradient flow:
- Because Transformers do not rely on recurrent connections, they avoid issues like vanishing and exploding gradients that can make RNNs difficult to train on long sequences. Additionally, Transformers often use residual connections and layer normalization to further improve training stability and efficiency.
However, it is important to remember that better is often contextual in machine learning. While Transformers offer many advantages, there are still scenarios where CNNs or RNNs might be preferred due to their inductive biases or constraints of the problem. For example:
- CNN strengths: local pattern recognition (such as in image processing), translation invariance, and efficient computation when dealing with fixed-size inputs.
- RNN strengths: strong performance on smaller datasets or tasks where precise modeling of sequential data with tight temporal dependencies is crucial.
Despite their benefits, Transformers also have drawbacks, such as large memory requirements due to the self-attention mechanism, which can make them impractical for very long sequences without modifications like sparse attention patterns. However, for many NLP tasks and even for some tasks outside of NLP, Transformers have quickly become the architecture of choice, as evidenced by their dominance in recent models and benchmarks.