NABU $\mathrm{-}$ Multilingual Graph-based Neural RDF Verbalizer
This addresses the problem of limited language support in RDF-to-text tasks for researchers and practitioners working with multilingual Linked Data, representing an incremental advancement by extending existing neural methods to new languages.
The paper tackles the lack of multilingual models for converting RDF triples to text by presenting NABU, a graph-based neural model that verbalizes RDF data to German, Russian, and English, achieving state-of-the-art results with 66.21 BLEU in English and 56.04 BLEU in multilingual settings.
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.