Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On Learning Text Style Transfer with Direct Rewards   In most cases, the lack of parallel corpora makes it impossible to directly
train supervised models for the text style transfer task. In this paper, we
explore training algorithms that instead optimize reward functions that
explicitly consider different aspects of the style-transferred outputs. In
particular, we leverage semantic similarity metrics originally used for
fine-tuning neural machine translation models to explicitly assess the
preservation of content between system outputs and input texts. We also
investigate the potential weaknesses of the existing automatic metrics and
propose efficient strategies of using these metrics for training. The
experimental results show that our model provides significant gains in both
automatic and human evaluation over strong baselines, indicating the
effectiveness of our proposed methods and training strategies.

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The categories of this paper are: hep-th
10
The categories of this paper are: q-bio
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The categories of this paper are: nlin
16
The categories of this paper are: stat
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol