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Detecting and Understanding Generalization Barriers for Neural Machine Translation
[article]
2020
arXiv
pre-print
Generalization to unseen instances is our eternal pursuit for all data-driven models. However, for realistic task like machine translation, the traditional approach measuring generalization in an average sense provides poor understanding for the fine-grained generalization ability. As a remedy, this paper attempts to identify and understand generalization barrier words within an unseen input sentence that cause the degradation of fine-grained generalization. We propose a principled definition
arXiv:2004.02181v1
fatcat:ik7mnqrg35crhdhn3piw3itira