The Transformers architecture, introduced in the paper “Attention is All You Need” by Vaswani et al. (2017), revolutionized the field of natural language processing. It consists of two main components: the encoder and the decoder. In this technical description, I’ll cover both the encoding and decoding processes, detailing their functionalities and the mechanisms involved, citing sources where applicable.
The encoder’s primary role is to convert input sequences (tokens) into meaningful representations. This process involves several key components:
1. Input Embedding: The input tokens (words or subwords) are first converted into dense vectors using an embedding layer. This allows for each token to be represented in a high-dimensional space.
1. Positional Encoding: Since Transformers do not inherently encode the positional information of tokens within sequences (unlike RNNs), positional encoding is added to incorporate this information. This is achieved by adding a unique vector to each token’s embedding, representing its position in the sequence. The positional encoding vectors are designed to ensure that the model can distinguish between different positions. This is mathematically derived using sine and cosine functions of different frequencies.
1. Multi-Head Attention: The core mechanism of the Transformer is the multi-head attention mechanism. Let’s break it down: – Self-Attention: Each token in the input sequence attends to every other token via a weight mechanism. This creates a weighted sum of all tokens, focusing on relevant parts of the input sequence. Formally, for each token, we compute three vectors: Query (Q), Key (K), and Value (V). Using these, attention scores are calculated as: [ \text{Attention}(Q, K, V) = \text{softmax} \left( \frac{QK^T}{\sqrt{d\_k}} \right) V ] where (d\_k) is the dimension of the key vectors. – Multi-Head Mechanism: Instead of performing a single attention function, multiple sets (heads) of Q, K, and V are created, and the attention operation is applied in parallel. The outputs are then concatenated and linearly transformed.
1. Feed-Forward Neural Networks (FFN): After the multi-head attention, each token goes through a position-wise feed-forward network. The FFN consists of two linear transformations with a ReLU activation in between, applied to each position separately and identically.
1. Residual Connections and Layer Normalization: Both the self-attention layer and the feed-forward network layer have residual connections around them (i.e., the input to the layer is added to the output of the layer). Layer normalization is applied to the summed outputs to stabilize training.
These steps are repeated multiple times (layers) to build deep encoder representations.
The decoder is responsible for generating the output sequence, typically used in sequence-to-sequence tasks like machine translation. The process closely mirrors the encoder with some additional steps:
1. Output Embedding and Positional Encoding: Like the encoder, the output tokens (e.g., previously generated words in the sequence) are embedded and receive positional encoding.
1. Masked Multi-Head Self-Attention: The decoder also uses multi-head self-attention, but with a crucial difference: it’s masked to prevent attending to future tokens. This ensures the autoregressive property of the model (i.e., the prediction for each position depends only on the known outputs before it).
1. Encoder-Decoder Attention: In addition to self-attending to the output sequence, the decoder attends to the encoder’s output states. This is achieved through another multi-head attention layer, where the queries come from the previous decoder layer, and the keys and values come from the encoder’s output representations.
1. Feed-Forward Neural Networks (FFN): The position-wise feed-forward network in the decoder is identical to that in the encoder.
1. Residual Connections and Layer Normalization: Again, residual connections and layer normalization are applied in the same way as the encoder.
The final output is then passed through a linear layer and a softmax function to produce probability distributions over the target vocabulary.
1. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., … & Polosukhin, I. (2017). “Attention is All You Need”. Advances in Neural Information Processing Systems, 30, 5998-6008.
2. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding”. arXiv preprint arXiv:1810.04805.
In conclusion, the Transformer model’s sophisticated design, characterized by the use of multi-head self-attention, positional encoding, and residual connections, enables it to handle complex sequence-to-sequence tasks efficiently. It bypasses the sequential bottleneck of RNNs and captures global dependencies and contextual information more effectively.
