Sequence-to-Sequence Models: The Building Blocks of Modern AI Systems

#ai #beginners

If you are trying to learn about modern AI systems or reading research papers, you will likely encounter sequence-to-sequence (seq2seq) models, which act as foundational components in many real-world AI applications.

Let's try learning them piece by piece.

What is a sequence-to-sequence model?

You can think of it as a model that takes a sequence of items as input and produces another sequence of items as output.

One common use case is neural machine translation.
Here, the input sequence is a series of words processed one after another.

The output is also a series of words.

What does a sequence-to-sequence model contain?

A sequence-to-sequence model is composed of two main parts:

an encoder
a decoder

Encoder
The encoder takes each item in the input sequence and compiles the information into a vector (called the context).

Decoder
After the input is processed, the encoder sends the context to the decoder.

The decoder then produces the output sequence one item at a time.

What is a context?

In machine translation, each word in the input sentence is first converted into a vector using a word embedding algorithm. These embeddings capture semantic meaning from individual words.

As the encoder processes the sequence word by word, it combines this information into a single fixed-length vector, commonly called the context vector in early sequence-to-sequence models. This vector summarizes the meaning of the whole input sentence.

The encoder and decoder in these early sequence-to-sequence models are typically implemented using recurrent neural networks (RNNs).

At each time step, the encoder RNN takes two inputs:

The current word from the input sequence, converted into a vector using word embeddings
The previous hidden state, which carries information from earlier words in the sequence

The encoder processes the entire input sequence in this way, updating its hidden state at each step, and eventually produces the context vector that summarizes the whole sequence.

In the next article, we’ll dive deeper into Recurrent Neural Networks (RNNs) themselves and understand how they process sequences step by step.

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