Non Autoregressive

Non-autoregressive (NAR) models represent a significant shift in sequence generation, aiming to improve the speed of tasks like machine translation and speech recognition by generating entire sequences in parallel, unlike the sequential approach of autoregressive models. Current research focuses on enhancing the accuracy of NAR models, often through techniques like knowledge distillation from autoregressive counterparts, incorporating adversarial learning, and developing novel architectures such as multi-layer draft heads and specialized decoders. This research is impactful because NAR models offer substantial speed improvements, crucial for real-time applications, while striving to maintain accuracy comparable to slower autoregressive methods.