Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Empirical Gaussian priors for cross-lingual transfer learning   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.

28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio
6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex