Non-autoregressive models have been widely studied in the Complete Information Scenario (CIS), in which the input has complete information of corresponding output. However, their explorations in the Incomplete Information Scenario (IIS) are extremely limited. Our analyses reveal that the IIS's incomplete input information will augment the inherent limitations of existing non-autoregressive models trained under Maximum Likelihood Estimation. In this paper, we propose for the IIS an Adversarial Non-autoregressive Transformer (ANT) which has two features: 1) Position-Aware Self-Modulation to provide more reasonable hidden representations, and 2) Dependency Feed Forward Network to strengthen its capacity in dependency modeling. We compare ANT with other mainstream models in the IIS and demonstrate that ANT can achieve comparable performance with much fewer decoding iterations. Furthermore, we show its great potential in various applications like latent interpolation and semi-supervised learning.

翻译：非自回归模型已在完全信息场景中得到广泛研究，其中输入包含对应输出的完整信息。然而，它们在非完全信息场景中的探索极为有限。我们的分析表明，非完全信息场景中输入信息的不完整性会放大现有基于最大似然估计训练的非自回归模型固有的局限性。本文提出了一种面向非完全信息场景的对抗性非自回归Transformer（ANT），该模型具有两个特点：1）位置感知自调制机制，能够提供更合理的隐层表示；2）依赖前馈网络，增强其依赖建模能力。我们将ANT与非完全信息场景中的其他主流模型进行对比，结果表明ANT能够在显著减少解码迭代次数的同时达到可比的性能。此外，我们展示了该模型在潜在插值与半监督学习等多种应用中的巨大潜力。