Sequence-to-Sequence

What is Sequence-to-Sequence (Seq2Seq)? Seq2Seq Explained

Sequence-to-Sequence (Seq2Seq) is a deep learning architecture used for tasks that involve transforming one sequence of data into another. It is particularly effective in tasks such as machine translation, text summarization, chatbot responses, and speech recognition.

The Seq2Seq model consists of two main components: an encoder and a decoder.

Encoder: The encoder takes the input sequence and processes it into a fixed-length vector, also known as the context vector or the latent representation. The encoder can be a recurrent neural network (RNN), such as Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU), or a more recent architecture like the Transformer. The encoder reads the input sequence step by step and captures the important information in the context vector.

Decoder: The decoder takes the context vector generated by the encoder and generates the output sequence step by step. Like the encoder, the decoder can be an RNN or Transformer architecture. It receives the context vector as the initial hidden state and produces the output sequence by predicting the next element at each time step. During training, the correct target sequence is provided as input to the decoder, while during inference or testing, the decoder generates the output sequence autonomously based on its previous predictions.

The Seq2Seq model is trained using a technique called teacher forcing, where the decoder is provided with the ground truth output at each time step during training. This helps in guiding the model to learn the correct sequence mapping. However, during inference, the model is typically used in an autoregressive manner, where the output at each time step becomes the input for the next time step.

Seq2Seq models can handle sequences of variable lengths and can capture complex dependencies between elements in the input and output sequences. They have been successfully applied to various sequence generation tasks and have shown promising results in generating coherent and contextually relevant outputs.

Extensions to the basic Seq2Seq architecture include the use of attention mechanisms, which allow the decoder to focus on different parts of the input sequence while generating each output element. Attention mechanisms improve the model’s ability to handle long sequences and improve translation quality.

Overall, Seq2Seq models have revolutionized the field of sequence generation and have become a fundamental building block for many natural language processing applications.