Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Universal Parent Model for Low-Resource Neural Machine Translation
  Transfer   Transfer learning from a high-resource language pair `parent' has been proven
to be an effective way to improve neural machine translation quality for
low-resource language pairs `children.' However, previous approaches build a
custom parent model or at least update an existing parent model's vocabulary
for each child language pair they wish to train, in an effort to align parent
and child vocabularies. This is not a practical solution. It is wasteful to
devote the majority of training time for new language pairs to optimizing
parameters on an unrelated data set. Further, this overhead reduces the utility
of neural machine translation for deployment in humanitarian assistance
scenarios, where extra time to deploy a new language pair can mean the
difference between life and death. In this work, we present a `universal'
pre-trained neural parent model with constant vocabulary that can be used as a
starting point for training practically any new low-resource language to a
fixed target language. We demonstrate that our approach, which leverages
orthography unification and a broad-coverage approach to subword
identification, generalizes well to several languages from a variety of
families, and that translation systems built with our approach can be built
more quickly than competing methods and with better quality as well.

31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
19
The categories of this paper are: eess
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics