# Non-Autoregressive Machine Translation with Latent Alignments

arXiv: https://arxiv.org/abs/2004.07437 · HTML: https://mlsanity.com/paper/2004.07437/

## Key facts

- Cited by 96 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2020.

## Built on by (most cited first, 30 of 96)

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- [Learning to Efficiently Sample from Diffusion Probabilistic Models](https://mlsanity.com/paper/2106.03802/) (2021)
- [Accelerating Transformer Inference for Translation via Parallel Decoding](https://mlsanity.com/paper/2305.10427/) (2023)
- [MaskViT: Masked Visual Pre-Training for Video Prediction](https://mlsanity.com/paper/2206.11894/) (2022)
- [Step-unrolled Denoising Autoencoders for Text Generation](https://mlsanity.com/paper/2112.06749/) (2021)
- [Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes](https://mlsanity.com/paper/2111.12701/) (2021)
- [A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyond](https://mlsanity.com/paper/2204.09269/) (2022)
- [Order-Agnostic Cross Entropy for Non-Autoregressive Machine Translation](https://mlsanity.com/paper/2106.05093/) (2021)
- [Directed Acyclic Transformer for Non-Autoregressive Machine Translation](https://mlsanity.com/paper/2205.07459/) (2022)
- [DiffusER: Discrete Diffusion via Edit-based Reconstruction](https://mlsanity.com/paper/2210.16886/) (2022)
- [Non-Autoregressive Translation with Layer-Wise Prediction and Deep Supervision](https://mlsanity.com/paper/2110.07515/) (2021)
- [Lookahead: An Inference Acceleration Framework for Large Language Model with Lossless Generation Accuracy](https://mlsanity.com/paper/2312.12728/) (2023)
- [A Comparative Study on Non-Autoregressive Modelings for Speech-to-Text Generation](https://mlsanity.com/paper/2110.05249/) (2021)
- [On the Learning of Non-Autoregressive Transformers](https://mlsanity.com/paper/2206.05975/) (2022)
- [Revisiting End-to-End Speech-to-Text Translation From Scratch](https://mlsanity.com/paper/2206.04571/) (2022)
- [Progressive Multi-Granularity Training for Non-Autoregressive Translation](https://mlsanity.com/paper/2106.05546/) (2021)
- [CTC Alignments Improve Autoregressive Translation](https://mlsanity.com/paper/2210.05200/) (2022)
- [AligNART: Non-autoregressive Neural Machine Translation by Jointly Learning to Estimate Alignment and Translate](https://mlsanity.com/paper/2109.06481/) (2021)
- [Investigating the Reordering Capability in CTC-based Non-Autoregressive End-to-End Speech Translation](https://mlsanity.com/paper/2105.04840/) (2021)
- [A Cheaper and Better Diffusion Language Model with Soft-Masked Noise](https://mlsanity.com/paper/2304.04746/) (2023)
- [StreamSpeech: Simultaneous Speech-to-Speech Translation with Multi-task Learning](https://mlsanity.com/paper/2406.03049/) (2024)
- [An Imitation Learning Curriculum for Text Editing with Non-Autoregressive Models](https://mlsanity.com/paper/2203.09486/) (2022)
- [One Reference Is Not Enough: Diverse Distillation with Reference Selection for Non-Autoregressive Translation](https://mlsanity.com/paper/2205.14333/) (2022)
- [Sequence-Level Training for Non-Autoregressive Neural Machine Translation](https://mlsanity.com/paper/2106.08122/) (2021)
- [Lossless Acceleration for Seq2seq Generation with Aggressive Decoding](https://mlsanity.com/paper/2205.10350/) (2022)
- [Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog](https://mlsanity.com/paper/2104.04923/) (2021)

## Abstract (arXiv)

This paper presents two strong methods, CTC and Imputer, for non-autoregressive machine translation that model latent alignments with dynamic programming. We revisit CTC for machine translation and demonstrate that a simple CTC model can achieve state-of-the-art for single-step non-autoregressive machine translation, contrary to what prior work indicates. In addition, we adapt the Imputer model for non-autoregressive machine translation and demonstrate that Imputer with just 4 generation steps can match the performance of an autoregressive Transformer baseline. Our latent alignment models are simpler than many existing non-autoregressive translation baselines; for example, we do not require target length prediction or re-scoring with an autoregressive model. On the competitive WMT'14 EnrightarrowDe task, our CTC model achieves 25.7 BLEU with a single generation step, while Imputer achieves 27.5 BLEU with 2 generation steps, and 28.0 BLEU with 4 generation steps. This compares favourably to the autoregressive Transformer baseline at 27.8 BLEU.