  Digital news platforms use news recommenders as the main instrument to cater
to the individual information needs of readers. Despite an increasingly
language-diverse online community, in which many Internet users consume news in
multiple languages, the majority of news recommendation focuses on major,
resource-rich languages, and English in particular. Moreover, nearly all news
recommendation efforts assume monolingual news consumption, whereas more and
more users tend to consume information in at least two languages. Accordingly,
the existing body of work on news recommendation suffers from a lack of
publicly available multilingual benchmarks that would catalyze development of
news recommenders effective in multilingual settings and for low-resource
languages. Aiming to fill this gap, we introduce xMIND, an open, multilingual
news recommendation dataset derived from the English MIND dataset using machine
translation, covering a set of 14 linguistically and geographically diverse
languages, with digital footprints of varying sizes. Using xMIND, we
systematically benchmark several state-of-the-art content-based neural news
recommenders (NNRs) in both zero-shot (ZS-XLT) and few-shot (FS-XLT)
cross-lingual transfer scenarios, considering both monolingual and bilingual
news consumption patterns. Our findings reveal that (i) current NNRs, even when
based on a multilingual language model, suffer from substantial performance
losses under ZS-XLT and that (ii) inclusion of target-language data in FS-XLT
training has limited benefits, particularly when combined with a bilingual news
consumption. Our findings thus warrant a broader research effort in
multilingual and cross-lingual news recommendation. The xMIND dataset is
available at https://github.com/andreeaiana/xMIND.
