  Back translation is one of the most widely used methods for improving the
performance of neural machine translation systems. Recent research has sought
to enhance the effectiveness of this method by increasing the 'diversity' of
the generated translations. We argue that the definitions and metrics used to
quantify 'diversity' in previous work have been insufficient. This work puts
forward a more nuanced framework for understanding diversity in training data,
splitting it into lexical diversity and syntactic diversity. We present novel
metrics for measuring these different aspects of diversity and carry out
empirical analysis into the effect of these types of diversity on final neural
machine translation model performance for low-resource
English$\leftrightarrow$Turkish and mid-resource
English$\leftrightarrow$Icelandic. Our findings show that generating back
translation using nucleus sampling results in higher final model performance,
and that this method of generation has high levels of both lexical and
syntactic diversity. We also find evidence that lexical diversity is more
important than syntactic for back translation performance.
