While RNNs, LSTMs, and GRUs process sequences element by element, many real-world problems require mapping an input sequence of one length to an output sequence of a potentially different length. Consider machine translation (translating a sentence from French to English) or text summarization (condensing a long article into a few sentences). The input and output lengths are often unrelated. A standard RNN architecture, which typically produces one output for each input, isn't directly suited for these tasks.

To address this, the Sequence-to-Sequence (seq2seq) framework was developed, primarily using recurrent architectures like LSTMs or GRUs. The core idea is to use two separate RNNs: one to process the input sequence (the Encoder) and another to generate the output sequence (the Decoder).

The Encoder-Decoder Architecture

The seq2seq model consists of two main components:

Encoder: This RNN reads the input sequence, token by token (e.g., words or subwords). Its goal is not to produce an output at each step but to compress the entire input sequence's information into a fixed-size vector representation. This vector is often called the "context vector" or "thought vector," typically represented by the final hidden state (and cell state, for LSTMs) of the encoder RNN.
Decoder: This RNN takes the context vector produced by the encoder as its initial hidden state. It then generates the output sequence, token by token. At each step $t$ , the decoder receives the context vector, its own previous hidden state $h_{t-1}$ , and the previously generated output token $y_{t-1}$ as input to produce the next output token $y_t$ and update its hidden state to $h_t$ . The generation process usually starts with a special start-of-sequence <SOS> token and continues until an end-of-sequence <EOS> token is produced or a maximum length is reached.

High-level structure of a Sequence-to-Sequence model using RNNs. The Encoder processes the input to create a context vector, which initializes the Decoder to generate the output sequence.

Information Flow and the Context Vector

The encoder processes the input sequence $X = (x_1, x_2, ..., x_n)$ and outputs a context vector $c$ . This vector $c$ aims to summarize the entire input sequence.

c = \text{Encoder}(x_1, x_2, ..., x_n)

Typically, for an LSTM, $c$ would be the final hidden state $h_n$ and cell state $C_n$ .

The decoder is initialized with this context (e.g., $h_0^{\text{dec}} = h_n^{\text{enc}}$ , $C_0^{\text{dec}} = C_n^{\text{enc}}$ ). It then generates the output sequence $Y = (y_1, y_2, ..., y_m)$ one token at a time. The probability of the next token $y_t$ depends on the context $c$ , the previous token $y_{t-1}$ , and the decoder's current hidden state $h_t^{\text{dec}}$ :

P(y_t | y_1, ..., y_{t-1}, c) = \text{Decoder}(y_{t-1}, h_{t-1}^{\text{dec}}, c)

The first input to the decoder is usually a special <SOS> token ( $y_0 = \text{<SOS>}$ ).

Implementation Sketch with PyTorch

Let's outline simplified Encoder and Decoder modules using PyTorch's nn.LSTM.

import torch
import torch.nn as nn

class EncoderRNN(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers=1):
        super(EncoderRNN, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        # Note: Assuming input_size is the embedding dimension
        self.embedding = nn.Embedding(input_size, hidden_size)
        self.lstm = nn.LSTM(
            hidden_size, hidden_size, num_layers, batch_first=True
        )

    def forward(self, input_seq):
        # input_seq shape: (batch_size, seq_length)
        embedded = self.embedding(input_seq)
        # embedded shape: (batch_size, seq_length, hidden_size)

        # Initialize hidden state (not shown for simplicity,
        # defaults to zeros)
        # hidden = self.init_hidden(batch_size)

        outputs, (hidden, cell) = self.lstm(embedded)
        # outputs shape: (batch_size, seq_length, hidden_size)
        # hidden shape: (num_layers, batch_size, hidden_size)
        # cell shape: (num_layers, batch_size, hidden_size)

        # We typically use the final hidden and cell states as context
        return hidden, cell

class DecoderRNN(nn.Module):
    def __init__(self, hidden_size, output_size, num_layers=1):
        super(DecoderRNN, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        # Note: output_size is the vocabulary size for the target
        # language
        self.embedding = nn.Embedding(output_size, hidden_size)
        self.lstm = nn.LSTM(
            hidden_size, hidden_size, num_layers, batch_first=True
        )
        self.out = nn.Linear(hidden_size, output_size)
        self.softmax = nn.LogSoftmax(dim=1) # Often used with NLLLoss

    def forward(self, input_token, hidden, cell):
        # input_token shape: (batch_size, 1) -> single token
        # hidden shape: (num_layers, batch_size, hidden_size)
        # cell shape: (num_layers, batch_size, hidden_size)

        embedded = self.embedding(input_token)
        # embedded shape: (batch_size, 1, hidden_size)

        # The context vector (encoder's final hidden/cell states)
        # is passed as the initial hidden/cell state here.
        output, (hidden, cell) = self.lstm(embedded, (hidden, cell))
        # output shape: (batch_size, 1, hidden_size)

        # Reshape output to be (batch_size, hidden_size) for the
        # linear layer
        output = output.squeeze(1)
        output = self.out(output)
        # output shape: (batch_size, output_size)

        # Optional: Apply softmax to get
        # probabilities/log-probabilities
        # output = self.softmax(output)

        return output, hidden, cell

# Example Usage
# input_vocab_size = 10000
# output_vocab_size = 12000
# hidden_dim = 256
# n_layers = 2
# batch_size = 32
# input_length = 50

# encoder = EncoderRNN(input_vocab_size, hidden_dim, n_layers)
# decoder = DecoderRNN(hidden_dim, output_vocab_size, n_layers)

# Example input batch (indices)
# input_tensor = torch.randint(
#     0, input_vocab_size, (batch_size, input_length)
# )

# Pass through encoder
# encoder_hidden, encoder_cell = encoder(input_tensor)

# Decoder input starts with <SOS> token (assume index 0)
# decoder_input = torch.full((batch_size, 1), 0, dtype=torch.long)
# decoder_hidden = encoder_hidden # Use encoder's final hidden state
# decoder_cell = encoder_cell   # Use encoder's final cell state

# Generate output sequence step-by-step (simplified loop)
# max_target_length = 60
# all_decoder_outputs = []
# for _ in range(max_target_length):
#     decoder_output, decoder_hidden, decoder_cell = decoder(
#         decoder_input, decoder_hidden, decoder_cell
#     )
#     all_decoder_outputs.append(decoder_output)
#
#     # Get the most likely next token (greedy decoding)
#     _, top_idx = decoder_output.topk(1)
#     # Use predicted token as next input
#     decoder_input = top_idx.detach()

Limitations and Next Steps

The standard Encoder-Decoder architecture using RNNs proved effective for many tasks. However, it relies on compressing the entire input sequence into a single, fixed-size context vector. This creates an information bottleneck, particularly problematic for long input sequences. It becomes difficult for the model to remember details from the beginning of a long input when generating the end of the output sequence.

This limitation was a significant motivation for the development of attention mechanisms. Attention allows the decoder to selectively focus on different parts of the input sequence at each step of the output generation process, rather than relying solely on the single context vector. This ability to look back at the relevant parts of the source input dramatically improved performance on tasks like machine translation and paved the way for the Transformer architecture, which we will explore in the next chapter.