Sequencetosequence

Sequencetosequence, commonly abbreviated seq2seq, denotes a family of models and learning frameworks designed to map an input sequence to an output sequence. The input and output sequences can differ in length, and the approach is used across natural language processing, speech, and other sequential data tasks.

A typical sequencetosequence architecture consists of an encoder and a decoder. The encoder reads the input sequence and produces a representation of its information, which may be a fixed-length vector or a stack of hidden states. The decoder then generates the output sequence from this representation, one element at a time. Early implementations used recurrent neural networks such as LSTMs or GRUs; later models widely adopted attention mechanisms to align input and output positions. The advent of Transformer architectures further improved performance by relying on self-attention and parallelizable computations, often replacing recurrent components.

Training is performed end-to-end by maximizing the probability of the target sequence given the input, usually via cross-entropy loss. Teacher forcing—a technique where the model is fed the true previous token during training—helps learning but can create exposure bias. Inference typically uses greedy decoding or beam search to generate the most likely output sequence.

Sequencetosequence models underpin many applications, including machine translation, text summarization, speech recognition, question answering, and image captioning. Evaluation uses metrics such as BLEU, ROUGE, or METEOR to compare generated sequences against reference targets. The approach remains a central paradigm in sequence modeling, with ongoing research aimed at improving efficiency, interpretability, and handling of long sequences.