Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Revisiting the Weaknesses of Reinforcement Learning for Neural Machine
  Translation   Policy gradient algorithms have found wide adoption in NLP, but have recently
become subject to criticism, doubting their suitability for NMT. Choshen et al.
(2020) identify multiple weaknesses and suspect that their success is
determined by the shape of output distributions rather than the reward. In this
paper, we revisit these claims and study them under a wider range of
configurations. Our experiments on in-domain and cross-domain adaptation reveal
the importance of exploration and reward scaling, and provide empirical
counter-evidence to these claims.

16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph