  We propose a novel interpretable framework for cross-lingual content
flagging, which significantly outperforms prior work both in terms of
predictive performance and average inference time. The framework is based on a
nearest-neighbour architecture and is interpretable by design. Moreover, it can
easily adapt to new instances without the need to retrain it from scratch.
Unlike prior work, (i) we encode not only the texts, but also the labels in the
neighbourhood space (which yields better accuracy), and (ii) we use a
bi-encoder instead of a cross-encoder (which saves computation time). Our
evaluation results on ten different datasets for abusive language detection in
eight languages shows sizable improvements over the state of the art, as well
as a speed-up at inference time.
