# FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow

arXiv: https://arxiv.org/abs/1909.02480 · HTML: https://mlsanity.com/paper/1909.02480/

## Key facts

- Cited by 89 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2019.

## Built on by (most cited first, 30 of 89)

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- [Transformer-based Conditional Variational Autoencoder for Controllable Story Generation](https://mlsanity.com/paper/2101.00828/) (2021)
- [Directed Acyclic Transformer for Non-Autoregressive Machine Translation](https://mlsanity.com/paper/2205.07459/) (2022)
- [FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition](https://mlsanity.com/paper/2105.03842/) (2021)
- [Non-Autoregressive Text Generation with Pre-trained Language Models](https://mlsanity.com/paper/2102.08220/) (2021)
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- [A Survey on Parallel Text Generation: From Parallel Decoding to Diffusion Language Models](https://mlsanity.com/paper/2508.08712/) (2025)
- [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)
- [Controllable Text Generation via Probability Density Estimation in the Latent Space](https://mlsanity.com/paper/2212.08307/) (2022)
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- [AligNART: Non-autoregressive Neural Machine Translation by Jointly Learning to Estimate Alignment and Translate](https://mlsanity.com/paper/2109.06481/) (2021)
- [textitlatent-GLAT: Glancing at Latent Variables for Parallel Text Generation](https://mlsanity.com/paper/2204.02030/) (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)
- [Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog](https://mlsanity.com/paper/2104.04923/) (2021)
- [Thinking Clearly, Talking Fast: Concept-Guided Non-Autoregressive Generation for Open-Domain Dialogue Systems](https://mlsanity.com/paper/2109.04084/) (2021)
- [Non-Monotonic Latent Alignments for CTC-Based Non-Autoregressive Machine Translation](https://mlsanity.com/paper/2210.03953/) (2022)
- [Discovering Non-monotonic Autoregressive Orderings with Variational Inference](https://mlsanity.com/paper/2110.15797/) (2021)
- [Recent Advances in Neural Text Generation: A Task-Agnostic Survey](https://mlsanity.com/paper/2203.03047/) (2022)
- [Universal Multimodal Representation for Language Understanding](https://mlsanity.com/paper/2301.03344/) (2023)
- [Enriching Non-Autoregressive Transformer with Syntactic and SemanticStructures for Neural Machine Translation](https://mlsanity.com/paper/2101.08942/) (2021)

## Abstract (arXiv)

Most sequence-to-sequence (seq2seq) models are autoregressive; they generate each token by conditioning on previously generated tokens. In contrast, non-autoregressive seq2seq models generate all tokens in one pass, which leads to increased efficiency through parallel processing on hardware such as GPUs. However, directly modeling the joint distribution of all tokens simultaneously is challenging, and even with increasingly complex model structures accuracy lags significantly behind autoregressive models. In this paper, we propose a simple, efficient, and effective model for non-autoregressive sequence generation using latent variable models. Specifically, we turn to generative flow, an elegant technique to model complex distributions using neural networks, and design several layers of flow tailored for modeling the conditional density of sequential latent variables. We evaluate this model on three neural machine translation (NMT) benchmark datasets, achieving comparable performance with state-of-the-art non-autoregressive NMT models and almost constant decoding time w.r.t the sequence length.