Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-supervised and Supervised Joint Training for Resource-rich Machine
  Translation   Self-supervised pre-training of text representations has been successfully
applied to low-resource Neural Machine Translation (NMT). However, it usually
fails to achieve notable gains on resource-rich NMT. In this paper, we propose
a joint training approach, $F_2$-XEnDec, to combine self-supervised and
supervised learning to optimize NMT models. To exploit complementary
self-supervised signals for supervised learning, NMT models are trained on
examples that are interbred from monolingual and parallel sentences through a
new process called crossover encoder-decoder. Experiments on two resource-rich
translation benchmarks, WMT'14 English-German and WMT'14 English-French,
demonstrate that our approach achieves substantial improvements over several
strong baseline methods and obtains a new state of the art of 46.19 BLEU on
English-French when incorporating back translation. Results also show that our
approach is capable of improving model robustness to input perturbations such
as code-switching noise which frequently appears on social media.

4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
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The categories of this paper are: gr-qc
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The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph