  Sequence model learning algorithms typically maximize log-likelihood minus
the norm of the model (or minimize Hamming loss + norm). In cross-lingual
part-of-speech (POS) tagging, our target language training data consists of
sequences of sentences with word-by-word labels projected from translations in
$k$ languages for which we have labeled data, via word alignments. Our training
data is therefore very noisy, and if Rademacher complexity is high, learning
algorithms are prone to overfit. Norm-based regularization assumes a constant
width and zero mean prior. We instead propose to use the $k$ source language
models to estimate the parameters of a Gaussian prior for learning new POS
taggers. This leads to significantly better performance in multi-source
transfer set-ups. We also present a drop-out version that injects (empirical)
Gaussian noise during online learning. Finally, we note that using empirical
Gaussian priors leads to much lower Rademacher complexity, and is superior to
optimally weighted model interpolation.
