Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine Translation for Machines: the Sentiment Classification Use Case   We propose a neural machine translation (NMT) approach that, instead of
pursuing adequacy and fluency ("human-oriented" quality criteria), aims to
generate translations that are best suited as input to a natural language
processing component designed for a specific downstream task (a
"machine-oriented" criterion). Towards this objective, we present a
reinforcement learning technique based on a new candidate sampling strategy,
which exploits the results obtained on the downstream task as weak feedback.
Experiments in sentiment classification of Twitter data in German and Italian
show that feeding an English classifier with machine-oriented translations
significantly improves its performance. Classification results outperform those
obtained with translations produced by general-purpose NMT models as well as by
an approach based on reinforcement learning. Moreover, our results on both
languages approximate the classification accuracy computed on gold standard
English tweets.

34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th
32
The categories of this paper are: atom-ph