Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Weakly-Supervised Deep Learning for Domain Invariant Sentiment
  Classification   The task of learning a sentiment classification model that adapts well to any
target domain, different from the source domain, is a challenging problem.
Majority of the existing approaches focus on learning a common representation
by leveraging both source and target data during training. In this paper, we
introduce a two-stage training procedure that leverages weakly supervised
datasets for developing simple lift-and-shift-based predictive models without
being exposed to the target domain during the training phase. Experimental
results show that transfer with weak supervision from a source domain to
various target domains provides performance very close to that obtained via
supervised training on the target domain itself.

6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph