  For nearly three decades, language models derived from the $n$-gram
assumption held the state of the art on the task. The key to their success lay
in the application of various smoothing techniques that served to combat
overfitting. However, when neural language models toppled $n$-gram models as
the best performers, $n$-gram smoothing techniques became less relevant.
Indeed, it would hardly be an understatement to suggest that the line of
inquiry into $n$-gram smoothing techniques became dormant. This paper re-opens
the role classical $n$-gram smoothing techniques may play in the age of neural
language models. First, we draw a formal equivalence between label smoothing, a
popular regularization technique for neural language models, and add-$\lambda$
smoothing. Second, we derive a generalized framework for converting any
$n$-gram smoothing technique into a regularizer compatible with neural language
models. Our empirical results find that our novel regularizers are comparable
to and, indeed, sometimes outperform label smoothing on language modeling and
machine translation.
