Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Revisiting Low-Resource Neural Machine Translation: A Case Study   It has been shown that the performance of neural machine translation (NMT)
drops starkly in low-resource conditions, underperforming phrase-based
statistical machine translation (PBSMT) and requiring large amounts of
auxiliary data to achieve competitive results. In this paper, we re-assess the
validity of these results, arguing that they are the result of lack of system
adaptation to low-resource settings. We discuss some pitfalls to be aware of
when training low-resource NMT systems, and recent techniques that have shown
to be especially helpful in low-resource settings, resulting in a set of best
practices for low-resource NMT. In our experiments on German--English with
different amounts of IWSLT14 training data, we show that, without the use of
any auxiliary monolingual or multilingual data, an optimized NMT system can
outperform PBSMT with far less data than previously claimed. We also apply
these techniques to a low-resource Korean-English dataset, surpassing
previously reported results by 4 BLEU.

7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
16
The categories of this paper are: stat
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an