Neural machine translation (NMT) models usually suffer from catastrophic forgetting during continual training where the models tend to gradually forget previously learned knowledge and swing to fit the newly added data which may have a different distribution, e.g. a different domain. Although many methods have been proposed to solve this problem, we cannot get to know what causes this phenomenon yet. Under the background of domain adaptation, we investigate the cause of catastrophic forgetting from the perspectives of modules and parameters (neurons). The investigation on the modules of the NMT model shows that some modules have tight relation with the general-domain knowledge while some other modules are more essential in the domain adaptation. And the investigation on the parameters shows that some parameters are important for both the general-domain and in-domain translation and the great change of them during continual training brings about the performance decline in general-domain. We conduct experiments across different language pairs and domains to ensure the validity and reliability of our findings.

在域自适应背景下，我们从模块和参数（神经元）的角度探究了神经机器翻译（NMT）模型的灾难性遗忘原因，发现一些模块与通用领域知识有紧密联系，而其他一些模块在域自适应中更为重要，一些参数对通用领域和领域内翻译都很重要，而在持续训练期间它们的大幅变化导致了通用领域性能下降。

探究神经机器翻译的连续训练期间的灾难性遗忘