Maximum-a-posteriori (MAP) decoding is the most widely used decoding strategy for neural machine translation (NMT) models. The underlying assumption is that model probability correlates well with human judgment, with better translations getting assigned a higher score by the model. However, research has shown that this assumption does not always hold, and generation quality can be improved by decoding to optimize a utility function backed by a metric or quality-estimation signal, as is done by Minimum Bayes Risk (MBR) or Quality-Aware decoding. The main disadvantage of these approaches is that they require an additional model to calculate the utility function during decoding, significantly increasing the computational cost. In this paper, we propose to make the NMT models themselves quality-aware by training them to estimate the quality of their own output. Using this approach for MBR decoding we can drastically reduce the size of the candidate list, resulting in a speed-up of two-orders of magnitude. When applying our method to MAP decoding we obtain quality gains similar or even superior to quality reranking approaches, but with the efficiency of single pass decoding.

翻译：最大后验（MAP）解码是神经机器翻译（NMT）模型中最广泛使用的解码策略。其基本假设是模型概率与人工判断高度相关，即更好的翻译会被模型赋予更高分数。然而研究表明，这一假设并非始终成立，通过解码以优化由评估指标或质量评估信号支持的效用函数（如最小贝叶斯风险（MBR）或质量感知解码），可以提升生成质量。这些方法的主要缺陷在于需要额外模型在解码过程中计算效用函数，从而显著增加计算成本。本文提出通过训练NMT模型自身评估其输出质量，使其具备质量感知能力。将本方法应用于MBR解码时，候选列表规模可大幅缩减，从而实现两个数量级的加速。当将本方法应用于MAP解码时，可获得与质量重排序方法相当甚至更优的质量提升，同时保持单遍解码的高效性。