Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bridging the domain gap in cross-lingual document classification   The scarcity of labeled training data often prohibits the
internationalization of NLP models to multiple languages. Recent developments
in cross-lingual understanding (XLU) has made progress in this area, trying to
bridge the language barrier using language universal representations. However,
even if the language problem was resolved, models trained in one language would
not transfer to another language perfectly due to the natural domain drift
across languages and cultures. We consider the setting of semi-supervised
cross-lingual understanding, where labeled data is available in a source
language (English), but only unlabeled data is available in the target
language. We combine state-of-the-art cross-lingual methods with recently
proposed methods for weakly supervised learning such as unsupervised
pre-training and unsupervised data augmentation to simultaneously close both
the language gap and the domain gap in XLU. We show that addressing the domain
gap is crucial. We improve over strong baselines and achieve a new
state-of-the-art for cross-lingual document classification.

20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
16
The categories of this paper are: stat
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
25
The categories of this paper are: patt-sol
2
The categories of this paper are: math