Sequence-to-Sequence Models

Sequence-to-sequence models are a type of machine learning system designed to transform one sequence of data into another. For example, they can take a sentence in one language and produce its translation in another language. These models use neural networks—computational systems inspired by the human brain—to understand context and patterns in the input sequence, then generate a corresponding output sequence. They’ve been instrumental in tasks like language translation, speech recognition, and text summarization, enabling machines to handle complex, variable-length data effectively and adaptively.