Non-autoregressive (NAR) generation, which is first proposed in neural machine translation (NMT) to speed up inference, has attracted much attention in both machine learning and natural language processing communities. While NAR generation can significantly accelerate inference speed for machine translation, the speedup comes at the cost of sacrificed translation accuracy compared to its counterpart, autoregressive (AR) generation. In recent years, many new models and algorithms have been designed/proposed to bridge the accuracy gap between NAR generation and AR generation. In this paper, we conduct a systematic survey with comparisons and discussions of various non-autoregressive translation (NAT) models from different aspects. Specifically, we categorize the efforts of NAT into several groups, including data manipulation, modeling methods, training criterion, decoding algorithms, and the benefit from pre-trained models. Furthermore, we briefly review other applications of NAR models beyond machine translation, such as grammatical error correction, text summarization, text style transfer, dialogue, semantic parsing, automatic speech recognition, and so on. In addition, we also discuss potential directions for future exploration, including releasing the dependency of KD, reasonable training objectives, pre-training for NAR, and wider applications, etc. We hope this survey can help researchers capture the latest progress in NAR generation, inspire the design of advanced NAR models and algorithms, and enable industry practitioners to choose appropriate solutions for their applications. The web page of this survey is at \url{https://github.com/LitterBrother-Xiao/Overview-of-Non-autoregressive-Applications}.

翻译：非自回归（NAR）生成最初在神经机器翻译（NMT）中提出以加速推理，近年来在机器学习和自然语言处理领域引起了广泛关注。尽管NAR生成能显著提升机器翻译的推理速度，但相较于自回归（AR）生成，这种加速以牺牲翻译准确性为代价。近年来，研究人员设计并提出了许多新模型与算法，旨在缩小NAR生成与AR生成之间的准确性差距。本文从不同角度对各类非自回归翻译（NAT）模型进行了系统比较与讨论的综述。具体而言，我们将NAT的相关研究分为若干类别，包括数据操纵、建模方法、训练准则、解码算法以及从预训练模型中受益等方面。此外，我们还简要回顾了NAR模型在机器翻译之外的其他应用，例如语法错误纠正、文本摘要、文本风格迁移、对话、语义解析、自动语音识别等。同时，本文还讨论了未来探索的潜在方向，包括解除对知识蒸馏（KD）的依赖、合理的训练目标、面向NAR的预训练以及更广泛的应用等。我们期望本综述能帮助研究人员了解NAR生成的最新进展，启发先进NAR模型与算法的设计，并为工业实践者选择适合其应用场景的解决方案提供参考。本综述的网页见\url{https://github.com/LitterBrother-Xiao/Overview-of-Non-autoregressive-Applications}。