Sequence-to-Sequence Model (Seq2Seq)

A sequence-to-sequence (Seq2Seq) model is a type of neural network architecture designed to convert one sequence into another. It typically consists of two components: an encoder, which processes the input sequence and compresses it into a fixed-size representation, and a decoder, which generates the output sequence step by step.

‍

Background
Seq2Seq models became popular in natural language processing around 2014. They were initially based on recurrent neural networks (RNNs) and later enhanced with attention mechanisms, allowing the decoder to “focus” on relevant parts of the input sequence. These innovations led to state-of-the-art results in machine translation and laid the foundation for today’s transformer architectures like GPT or BERT.

‍

Applications

• Machine translation (English → French, Chinese → English, etc.).
• Text summarization for condensing long documents.
• Conversational AI and chatbots.
• Speech recognition and transcription.
• Image captioning, where a sequence of words describes an image.

‍

Challenges

• Requires large labeled datasets to perform well.
• Struggles with very long sequences without advanced mechanisms like attention or transformers.

‍

Seq2Seq models marked a turning point in neural sequence modeling, as they offered a general framework to map variable-length inputs to variable-length outputs. Unlike traditional models that required fixed-size input/output, Seq2Seq systems enabled tasks like translation, summarization, and dialogue to be approached in a unified way.

‍

The breakthrough came with the introduction of the attention mechanism, which allowed decoders to look back at the full input sequence instead of relying solely on a compressed fixed vector. This innovation not only improved translation quality but also paved the way for transformers, which rely entirely on attention without recurrent connections.

‍

Although modern NLP now relies heavily on transformer architectures, Seq2Seq models remain a conceptual foundation. They are still used in constrained domains or as building blocks in hybrid systems, and their encoder–decoder design continues to inspire architectures across text, audio, and vision.

‍

📚 Further Reading

• Sutskever, I., Vinyals, O., Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks.
• Bahdanau, D., Cho, K., Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate.