Abstract
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