Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bi-Directional Neural Machine Translation with Synthetic Parallel Data   Despite impressive progress in high-resource settings, Neural Machine
Translation (NMT) still struggles in low-resource and out-of-domain scenarios,
often failing to match the quality of phrase-based translation. We propose a
novel technique that combines back-translation and multilingual NMT to improve
performance in these difficult cases. Our technique trains a single model for
both directions of a language pair, allowing us to back-translate source or
target monolingual data without requiring an auxiliary model. We then continue
training on the augmented parallel data, enabling a cycle of improvement for a
single model that can incorporate any source, target, or parallel data to
improve both translation directions. As a byproduct, these models can reduce
training and deployment costs significantly compared to uni-directional models.
Extensive experiments show that our technique outperforms standard
back-translation in low-resource scenarios, improves quality on cross-domain
tasks, and effectively reduces costs across the board.

29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin