Sequence Transduction

Sequence transduction focuses on efficiently transforming input sequences into output sequences, addressing challenges like varying sequence lengths and computational costs. Current research emphasizes developing faster and more efficient models, including adaptations of Transformers and RNN-Transducers, often incorporating techniques like dynamic compression, edit operation prediction, and joint token-duration prediction to improve speed and accuracy. These advancements are impacting various fields, from speech recognition and translation to image classification, by enabling faster inference, improved accuracy, and enhanced explainability in complex sequence-to-sequence tasks.