Non Autoregressive Sequence to Sequence

Non-autoregressive sequence-to-sequence models aim to improve the speed and efficiency of sequence-to-sequence tasks by generating output sequences in parallel, rather than sequentially as in autoregressive models. Current research focuses on adapting these models to various applications, including vision-language tasks, road network extraction, and lip-to-speech synthesis, often employing techniques like Query-CTC loss or incorporating self-supervised learning for improved performance. This approach offers significant advantages in inference speed, making it attractive for real-time applications while achieving comparable or even superior accuracy to autoregressive methods in certain domains. The development of effective training strategies, such as imitation learning curricula, is crucial for overcoming challenges and realizing the full potential of these models.