A sequence-to-sequence (seq2seq) model is a type of neural network architecture designed to convert input sequences into output sequences. It is commonly used for tasks where both the input and output are variable-length sequences, such as machine translation, text summarization, or chatbot responses. The model consists of two main components: an encoder and a decoder. The encoder processes the input sequence (e.g., an English sentence) and compresses it into a fixed-length “context vector” that captures its meaning. The decoder then uses this vector to generate the output sequence (e.g., the translated French sentence). Originally, these models relied on recurrent neural networks (RNNs) like LSTMs or GRUs for processing sequential data, but modern implementations often use Transformers due to their ability to handle long-range dependencies more effectively.
One key challenge seq2seq models address is handling variable-length input and output sequences. Traditional neural networks require fixed-size inputs, making them unsuitable for tasks like translation where sentence lengths vary. The encoder-decoder structure solves this by first mapping the entire input sequence to a context vector, which the decoder uses step-by-step to produce the output. However, early versions faced limitations when processing long sequences because the fixed-size context vector struggled to retain all information. This led to the introduction of attention mechanisms, which allow the decoder to dynamically focus on specific parts of the input during each output step. For example, when translating “The cat sat on the mat” to French, the decoder might prioritize “cat” and “mat” when generating the corresponding French words, improving accuracy and coherence.
Seq2seq models are widely applied in real-world scenarios. In machine translation, tools like Google Translate use these models to convert text between languages. For text summarization, a seq2seq model can condense a lengthy article into a concise summary by identifying key sentences. In dialogue systems, they power chatbots that generate contextually relevant responses based on user input. Another example is speech-to-text systems, where audio waveforms (processed as time-series sequences) are converted into transcribed text. While early implementations relied on RNN-based architectures, Transformers have become the standard due to their parallel processing capabilities and scalability. Training these models requires large paired datasets (e.g., English-French sentence pairs) and significant computational resources, but pre-trained models like BERT or T5 have made fine-tuning easier for specific tasks.