Non-autoregressive translation (NAT) model achieves a much faster inference speed than the autoregressive translation (AT) model because it can simultaneously predict all tokens during inference. However, its translation quality suffers from degradation compared to AT. And existing NAT methods only focus on improving the NAT model's performance but do not fully utilize it. In this paper, we propose a simple but effective method called "Candidate Soups," which can obtain high-quality translations while maintaining the inference speed of NAT models. Unlike previous approaches that pick the individual result and discard the remainders, Candidate Soups (CDS) can fully use the valuable information in the different candidate translations through model uncertainty. Extensive experiments on two benchmarks (WMT'14 EN-DE and WMT'16 EN-RO) demonstrate the effectiveness and generality of our proposed method, which can significantly improve the translation quality of various base models. More notably, our best variant outperforms the AT model on three translation tasks with 7.6 times speedup.

翻译：非自回归翻译（NAT）模型因能在推理时同时预测所有词元，其推理速度远快于自回归翻译（AT）模型，但其翻译质量相比AT有所下降。现有NAT方法仅聚焦于提升模型性能，并未充分利用模型潜力。本文提出一种名为“候选混汤”（Candidate Soups）的简洁有效方法，可在保持NAT模型推理速度的同时获得高质量翻译。不同于以往仅选取单个结果而丢弃其余候选的做法，候选混汤（CDS）通过模型不确定性充分挖掘不同候选翻译中的有价值信息。在两个基准任务（WMT'14英德翻译与WMT'16英罗翻译）上的大量实验表明，该方法具有有效性和通用性，能显著提升多种基础模型的翻译质量。特别地，我们的最佳变体在三个翻译任务上以7.6倍加速比超越了AT模型。