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Despite the tremendous success of Neural Machine Translation (NMT), itsperformance on low-resource language pairs still remains subpar, partly due tothe limited ability to handle previously unseen inputs, i.e., generalization.In this paper, we propose a method called Joint Dropout, that addresses thechallenge of low-resource neural machine translation by substituting phraseswith variables, resulting in significant enhancement of compositionality, whichis a key aspect of generalization. We observe a substantial improvement intranslation quality for language pairs with minimal resources, as seen in BLEUand Direct Assessment scores. Furthermore, we conduct an error analysis, andfind Joint Dropout to also enhance generalizability of low-resource NMT interms of robustness and adaptability across different domains

Joint Dropout: Improving Generalizability in Low-Resource Neural Machine Translation through Phrase Pair Variables