  Neural models have revolutionized the field of machine translation, but
creating parallel corpora is expensive and time-consuming. We investigate an
alternative to manual parallel corpora - hallucinated parallel corpora created
by generative language models. Although these models are themselves trained on
parallel data, they can leverage a multilingual vector space to create data,
and may be able to supplement small manually-procured corpora. Our experiments
highlight two key findings - despite a lack of diversity in their output, the
hallucinated data improves the translation signal, even when the domain clashes
with the original dataset.
