non-autoregressive translation

**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.