Non-Autoregressive Translation (NAT) is a machine translation approach that generates all target tokens simultaneously in a single forward pass — eliminating the sequential dependency of autoregressive translation for dramatically faster decoding, at the potential cost of some translation quality.

NAT Approaches

• Fertility-Based: Predict the number of target tokens per source token (fertility), then generate all target tokens in parallel.
• CTC (Connectionist Temporal Classification): Generate a longer sequence with blanks, collapse repeated tokens.
• Iterative Refinement: Generate all tokens at once, then refine with multiple iterations — mask-predict, CMLM.
• Glancing Training: During training, selectively mask tokens based on the model's current performance — curriculum-based.

Why It Matters

• Speed: 10-15× faster decoding than autoregressive translation — critical for low-latency applications.
• Multi-Modality Problem: NAT struggles with the multi-modality of translation — multiple valid translations exist.
• Gap Narrowing: Modern NAT methods have significantly closed the quality gap with autoregressive models.

Non-Autoregressive Translation is all-at-once translation — generating the complete translation simultaneously for dramatically faster machine translation decoding.