  While Transformer-based neural machine translation (NMT) is very effective in
high-resource settings, many languages lack the necessary large parallel
corpora to benefit from it. In the context of low-resource (LR) MT between two
closely-related languages, a natural intuition is to seek benefits from
structural "shortcuts", such as copying subwords from the source to the target,
given that such language pairs often share a considerable number of identical
words, cognates, and borrowings. We test Pointer-Generator Networks for this
purpose for six language pairs over a variety of resource ranges, and find weak
improvements for most settings. However, analysis shows that the model does not
show greater improvements for closely-related vs. more distant language pairs,
or for lower resource ranges, and that the models do not exhibit the expected
usage of the mechanism for shared subwords. Our discussion of the reasons for
this behaviour highlights several general challenges for LR NMT, such as modern
tokenization strategies, noisy real-world conditions, and linguistic
complexities. We call for better scrutiny of linguistically motivated
improvements to NMT given the blackbox nature of Transformer models, as well as
for a focus on the above problems in the field.
