Non Autoregressive Machine Translation

Non-autoregressive machine translation (NAT) aims to accelerate machine translation by generating target sentences in parallel, unlike the sequential approach of autoregressive models. Current research focuses on improving NAT's accuracy, which lags behind autoregressive methods, through techniques like iterative refinement, incorporating grammatical structures (e.g., using probabilistic context-free grammars), and modifying training objectives (e.g., order-agnostic cross-entropy). These advancements address the inherent challenges of parallel decoding, such as the multi-modality problem and the difficulty of capturing long-range dependencies, leading to more efficient and potentially cost-effective translation systems.