sequenzzusequenz

SequenzzuSequenz is a term used in German-language discourse to describe the general class of methods that map input sequences to output sequences across domains. In practice it corresponds to what English-language literature calls sequence-to-sequence, or Seq2Seq, modeling. The term emphasizes cross-domain transformation and end-to-end learning from paired input-output sequences.

The expression appears in academic writings and textbooks beginning in the 2010s and 2020s, as researchers discussed the evolution of encoder–decoder architectures from recurrent neural networks to Transformer-based models. The compound form reflects a direct gloss on the English concept, while being used in German-language explanations and tutorials.

Core architecture consists of an encoder that encodes an input sequence into a latent representation, and a decoder that generates an output sequence from that representation. Attention mechanisms, teacher forcing during training, and variants based on recurrent networks or transformers are common. Cross-domain Seq2Seq applications often employ shared embeddings or modular encoders to accommodate different data modalities such as natural language, speech, or symbolic sequences.

Applications include machine translation, speech recognition, text summarization, code translation, and music or symbolic sequence generation. In genomics or bioinformatics, sequence-to-sequence ideas have been used to model mappings between nucleotide sequences and functional annotations, illustrating the cross-domain scope implied by SequenzzuSequenz.

Evaluation typically relies on task-specific metrics such as BLEU for translation or edit distance for edit-based tasks, and model limitations include high data and compute requirements, exposure bias, and challenges with long sequences or domain shifts. The term SequenzzuSequenz remains mainly descriptive, with many practitioners favoring the more precise English term Seq2Seq in technical discussion.