Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Non-autoregressive Neural Machine Translation with Monolingual
  Data   Non-autoregressive (NAR) neural machine translation is usually done via
knowledge distillation from an autoregressive (AR) model. Under this framework,
we leverage large monolingual corpora to improve the NAR model's performance,
with the goal of transferring the AR model's generalization ability while
preventing overfitting. On top of a strong NAR baseline, our experimental
results on the WMT14 En-De and WMT16 En-Ro news translation tasks confirm that
monolingual data augmentation consistently improves the performance of the NAR
model to approach the teacher AR model's performance, yields comparable or
better results than the best non-iterative NAR methods in the literature and
helps reduce overfitting in the training process.

18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin