Investigating Catastrophic Forgetting During Continual Training for Neural Machine Translation
This work addresses the problem of catastrophic forgetting in neural machine translation for researchers, but it is incremental as it builds on existing methods to analyze causes rather than propose a new solution.
The paper investigates the causes of catastrophic forgetting in neural machine translation during continual training, finding that specific modules and parameters are crucial for retaining general-domain knowledge and that their alteration leads to performance decline.
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.