Understanding the Encoder-Decoder Transformer: A Deep Dive

The encoder-decoder transformer is one of the most influential architectures in natural language processing (NLP) and various machine learning applications. It revolutionized tasks such as machine translation, text generation, and even tasks beyond NLP like image processing and reinforcement learning.

In this article, we’ll break down the encoder-decoder transformer in detail, explore its components, and understand why it’s so powerful and widely used.

What is the Transformer Architecture?

The transformer architecture was introduced in the 2017 paper "Attention is All You Need" by Vaswani et al., and it replaced previous sequence-based models like RNNs (Recurrent Neural Networks) and LSTMs (Long Short-Term Memory networks) for many tasks. The architecture is built on self-attention mechanisms, which allow it to model relationships between all elements in a sequence in a parallelized manner, overcoming the bottlenecks of sequential processing.

The transformer has two major components:

1. Encoder: Processes the input data and captures its representations.
2. Decoder: Uses the encoder’s output to generate a new sequence (such as a translated sentence in machine translation).

Encoder-Decoder Transformer Overview

In tasks like machine translation, the goal is to convert a sequence (e.g., an English sentence) into another sequence (e.g., a French sentence). The encoder-decoder transformer is designed to handle such tasks efficiently and accurately. Here's how the flow works at a high level:

1. Input Sequence: The encoder receives the input sequence (English sentence).
2. Encoding: The encoder processes the input and creates a set of representations (encodings) that summarize the input sequence's information.
3. Decoding: The decoder takes these encoded representations and uses them to generate the target sequence (French sentence), one word (token) at a time.

Each stage of this process relies heavily on the attention mechanism, which helps the model focus on relevant parts of the input during encoding and decoding.

Detailed Breakdown of the Encoder-Decoder Transformer

Let’s dive into the components and operations in more detail.

1. Encoder

The encoder's job is to take the input sequence and transform it into a continuous representation. The encoder is composed of N layers (typically 6), and each layer has two main components:

• Multi-Head Self-Attention Mechanism
• Feed-Forward Neural Network

a. Multi-Head Self-Attention Mechanism

The self-attention mechanism allows the model to look at every word in the input sequence when processing any given word. For instance, when processing a word like “bank” in the sentence “I went to the bank,” the model can attend to other words in the sentence to understand if “bank” refers to a financial institution or the side of a river.

The process is as follows:

• Each word (or token) in the input is transformed into queries, keys, and values.
• The attention scores are calculated by taking the dot product of the queries and keys. These scores determine how much focus each word should have on every other word.
• The attention scores are used to weight the values.
• In the multi-head part, this process is repeated multiple times (with different learned projections), and the results are combined. This allows the model to capture different types of relationships between words.

b. Feed-Forward Neural Network

After self-attention, each word’s representation is passed through a fully connected feed-forward network, which applies transformations to help the model learn more complex features. This is done independently for each word (token).

2. Decoder

The decoder’s role is to generate the target sequence, using both the input sequence’s representation (from the encoder) and the previous words generated in the output sequence. Like the encoder, the decoder is also composed of N layers (typically 6), but each layer includes an additional mechanism:

• Masked Multi-Head Self-Attention
• Multi-Head Attention Over Encoder Outputs
• Feed-Forward Neural Network

a. Masked Multi-Head Self-Attention

In the decoder, the model needs to generate the target sequence one token at a time, so it uses masked self-attention to ensure that each word only looks at the previous words, not the ones yet to be predicted. This prevents "cheating" by looking ahead in the sequence.

b. Multi-Head Attention Over Encoder Outputs

This layer helps the decoder attend to the relevant parts of the input sequence. It works just like the attention mechanism in the encoder, but instead of focusing on the decoder’s input, it attends to the encoder's output. This allows the model to gather important information from the encoded input sequence, like which words in the English sentence to focus on when generating the French translation.

c. Feed-Forward Neural Network

Just like in the encoder, the decoder applies a feed-forward neural network to the output of the attention layers to learn more complex representations.

How Does the Attention Mechanism Work?

The heart of the transformer model is the attention mechanism, which allows it to learn long-range dependencies between words in a sequence. It helps the model decide which words to focus on when encoding or decoding.

Attention Calculation:

For each word (token) in the sequence, we calculate:

• Query (Q): A vector representing the current word.
• Key (K): A vector representing each word in the sequence.
• Value (V): A vector carrying the information for each word.

The attention score for a given word is computed by taking the dot product of its query vector with all the keys in the sequence. This tells the model how much focus (attention) each word should have relative to the others.

Formally:

Where dkd_kdk is the dimension of the key vectors, and softmax ensures that the attention scores sum to 1.

The output is a weighted sum of the values, where the weights are the attention scores.

Positional Encoding

Since transformers don't have any inherent sense of word order (unlike RNNs), they need a way to capture the position of words in a sequence. This is where positional encoding comes in. Positional encodings are added to the word embeddings, allowing the model to understand the relative positions of words in the input sequence.

The positional encodings are based on sine and cosine functions of different frequencies, which help the model distinguish between different positions in the sequence.

Why Transformers Are So Powerful

1. Parallelization: Unlike RNNs, which process one word at a time, transformers process all words in parallel. This makes them much faster, especially for long sequences.
2. Handling Long-Range Dependencies: Self-attention enables the model to capture relationships between distant words effectively, something RNNs struggle with due to their sequential nature.
3. Scalability: Transformers scale well with larger datasets and deeper architectures, making them ideal for large-scale tasks like language modeling, machine translation, and text generation.
4. Flexibility: The encoder-decoder transformer architecture can be applied to a variety of tasks, including:

Applications of Encoder-Decoder Transformers

• Google Translate: Modern machine translation systems use transformers for their ability to capture long-range dependencies between words in different languages.
• BERT and GPT: These are examples of transformer models applied to tasks like question-answering, text classification, and even open-domain conversation.
• Speech Recognition and Image Processing: The attention mechanism in transformers has been adapted for tasks in vision and speech, where capturing contextual information over a sequence of images or audio frames is critical.