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Revisiting Modularized Multilingual NMT to Meet Industrial Demands
[article]
2020
arXiv
pre-print
The complete sharing of parameters for multilingual translation (1-1) has been the mainstream approach in current research. However, degraded performance due to the capacity bottleneck and low maintainability hinders its extensive adoption in industries. In this study, we revisit the multilingual neural machine translation model that only share modules among the same languages (M2) as a practical alternative to 1-1 to satisfy industrial requirements. Through comprehensive experiments, we
arXiv:2010.09402v1
fatcat:diar2fvmsreidevpasfy6ewlqy