  The RDF-to-text task has recently gained substantial attention due to
continuous growth of Linked Data. In contrast to traditional pipeline models,
recent studies have focused on neural models, which are now able to convert a
set of RDF triples into text in an end-to-end style with promising results.
However, English is the only language widely targeted. We address this research
gap by presenting NABU, a multilingual graph-based neural model that verbalizes
RDF data to German, Russian, and English. NABU is based on an encoder-decoder
architecture, uses an encoder inspired by Graph Attention Networks and a
Transformer as decoder. Our approach relies on the fact that knowledge graphs
are language-agnostic and they hence can be used to generate multilingual text.
We evaluate NABU in monolingual and multilingual settings on standard
benchmarking WebNLG datasets. Our results show that NABU outperforms
state-of-the-art approaches on English with 66.21 BLEU, and achieves consistent
results across all languages on the multilingual scenario with 56.04 BLEU.
