Subword regularizations use multiple subword segmentations during training to
improve the robustness of neural machine translation models. In previous
subword regularizations, we use multiple segmentations in the training process
but use only one segmentation in the inference. In this study, we propose an
inference strategy to address this discrepancy. The proposed strategy
approximates the marginalized likelihood by using multiple segmentations
including the most plausible segmentation and several sampled segmentations.
Because the proposed strategy aggregates predictions from several
segmentations, we can regard it as a single model ensemble that does not
require any additional cost for training. Experimental results show that the
proposed strategy improves the performance of models trained with subword
regularization in low-resource machine translation tasks.

探讨使用多个子词部分划分提高神经机器翻译模型的鲁棒性，提出了一个推理策略，通过多种划分途径，包括最可信的划分和多个样本划分，来逼近边缘分布，并进行预测聚合，将其视为单个模型集成，在低资源机器翻译任务中改善了基于子词规则化训练的模型的性能。