  Generating text from structured data is challenging because it requires
bridging the gap between (i) structure and natural language (NL) and (ii)
semantically underspecified input and fully specified NL output. Multilingual
generation brings in an additional challenge: that of generating into languages
with varied word order and morphological properties. In this work, we focus on
Abstract Meaning Representations (AMRs) as structured input, where previous
research has overwhelmingly focused on generating only into English. We
leverage advances in cross-lingual embeddings, pretraining, and multilingual
models to create multilingual AMR-to-text models that generate in twenty one
different languages. For eighteen languages, based on automatic metrics, our
multilingual models surpass baselines that generate into a single language. We
analyse the ability of our multilingual models to accurately capture morphology
and word order using human evaluation, and find that native speakers judge our
generations to be fluent.
