sequencesto

Sequencesto is a term used in discussions of sequence transformation tasks within computer science and data processing. It denotes problems where an input sequence is transformed into an output sequence according to a model or rule. The term is not tied to a single formal definition and has appeared in diverse contexts, from natural language processing to symbolic computation, as a convenient shorthand for general sequence-to-sequence mapping.

In a sequencesto problem, one specifies an input alphabet X and an output alphabet Y, along with a (potentially probabilistic) mapping f: X* -> Y*. Practically, these tasks are often implemented with encoder-decoder architectures that convert the input representation into a latent form and then generate the target sequence token by token.

Common approaches rely on transformer-based models with attention, trained with cross-entropy and teacher forcing or its variants. Evaluation uses metrics like BLEU or ROUGE for language tasks, and task-specific measures for other domains, such as edit distance or accuracy of predicted sequences.

Sequencesto encompasses a range of applications, including machine translation, text summarization, code generation, speech-to-text, and forecasting where the output is a sequence. Research emphasizes robust generalization, alignment between source and target, and handling variable-length sequences.

The term remains informal; it gained traction in academic and blog discussions in the 2010s and 2020s as models grew capable of handling diverse sequence outputs. It is often used to contrast sequence-to-sequence tasks with classification or regression on single outputs.