What are sequence-to-sequence models?

Sequence-to-sequence (seq2seq) models are a type of model architecture used in machine learning, or natural language processing (NLP) tasks. In this Answer, we will learn everything about seq2seq models, including the components, architecture, and working.

Let's dive in!

What are seq2seq models?

Seq2seq models, inspired by recurrent neural networks (RNNs), translate varying input lengths into different output lengths. RNNs, known for their "memory" that retains past insights, can handle sequences but often falter with long ones due to technical issues like the vanishing gradient problem. This problem is addressed by advanced versions such as LSTM or GRU. However, there is another problem that is discussed below.

Consider translating a French greeting "Bonjour, comment ça va?" into Japanese as "こんにちは、お元気ですか？." A typical LSTM might struggle here, as the input has four words, while the output consists of nine symbols. That's where seq2seq shines. It overcomes LSTM limitations, making it an essential tool for tasks like language translation, where input and output lengths can differ significantly.

By building on RNN (or advanced versions like LSTM or GRU) strengths and mitigating their weaknesses, seq2seq models offer a more effective solution for translating varying sequence lengths.

Working of seq2seq models

Let's understand the working of seq2seq models with the following illustration:

1. Input sequence: The input sequence is fed into the encoder one item at a time. This could be a sequence of words, characters, or even images.

2. Encoder: The input sequence is processed by the encoder, and is converted to a context vector.

3. Context vector: The context vector acts like a condensed version of the input sequence, serving as the starting hidden state for the decoder.

4. Decoder: The decoder is another RNN that takes the context vector and produces the output sequence. The output sequence could be in the same domain as the input sequence, or it could be in a completely different domain.

5. Output sequence: The output sequence is the final result produced by the seq2seq model.

Components of seq2seq models

Now that we have discussed the working of Seq2Seq models, we can understand that the seq2seq model mainly consists of two components: an encoder and a decoder.

Here's the representation of the encoder-decoder model:

Now let's try to learn these components in more detail:

Encoder

The encoder in a seq2seq model is responsible for processing the input sequence and compressing it into a context vector. The encoder is typically a recurrent neural network (RNN), such as a long short-term memory (LSTM) or a gated recurrent unit (GRU).

It uses an RNN equation to update its hidden state $h_t$​ at each time step $t$:

Decoder

The decoder in a seq2seq model generates the output sequence from the context vector generated by the encoder. Like the encoder, the decoder is typically a type of RNN.

The hidden state $h_t$ is given as:

The output $y_t$ is calculated using the above-hidden state given as:

Applications

Having understood the working and components of seq2seq models, let's look at its applications. Seq2seq models have a variety of applications. A few of these are given below:

Conclusion

Seq2seq models are a useful tool in machine learning and NLP. They excel in translating languages, summarizing text, and explaining images. Despite looking complex, the core idea is straightforward: change input into a fixed context vector (or thought vector), then turn it into output. With proper training, seq2seq models get good at making accurate predictions.

Test your knowledge

Let's test the knowledge that you've learned so far. You have to match the option from the left column to the correct option from the right column.