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Label smoothing has been shown to be an effective regularization strategy inclassification, that prevents overfitting and helps in label de-noising.However, extending such methods directly to seq2seq settings, such as MachineTranslation, is challenging: the large target output space of such problemsmakes it intractable to apply label smoothing over all possible outputs. Mostexisting approaches for seq2seq settings either do token level smoothing, orsmooth over sequences generated by randomly substituting tokens in the targetsequence. Unlike these works, in this paper, we propose a technique thatsmooths over \emph{well formed} relevant sequences that not only havesufficient n-gram overlap with the target sequence, but are also\emph{semantically similar}. Our method shows a consistent and significantimprovement over the state-of-the-art techniques on different datasets.

Semantic Label Smoothing for Sequence to Sequence Problems