  Despite constant improvements in machine translation quality, automatic
poetry translation remains a challenging problem due to the lack of
open-sourced parallel poetic corpora, and to the intrinsic complexities
involved in preserving the semantics, style, and figurative nature of poetry.
We present an empirical investigation for poetry translation along several
dimensions: 1) size and style of training data (poetic vs. non-poetic),
including a zero-shot setup; 2) bilingual vs. multilingual learning; and 3)
language-family-specific models vs. mixed-multilingual models. To accomplish
this, we contribute a parallel dataset of poetry translations for several
language pairs. Our results show that multilingual fine-tuning on poetic text
significantly outperforms multilingual fine-tuning on non-poetic text that is
35X larger in size, both in terms of automatic metrics (BLEU, BERTScore) and
human evaluation metrics such as faithfulness (meaning and poetic style).
Moreover, multilingual fine-tuning on poetic data outperforms \emph{bilingual}
fine-tuning on poetic data.
