Investigating Catastrophic Forgetting During Continual Training for Neural Machine Translation (2020.coling-main)
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| Challenge: | Neural machine translation models suffer from catastrophic forgetting during continual training . models tend to overfit to frequent observations in the in-domain data but forget previously learned knowledge. |
| Approach: | They investigated the causes of catastrophic forgetting in NMT models by examining their parameters and modules. |
| Outcome: | The proposed model forgets previously learned knowledge and swings to fit new data . the results show 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. |
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| Challenge: | Currently, continuous learning methods suffer from catastrophic forgetting problem, causing model to forget previous knowledge while learning new knowledge. |
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