  The task of organizing and clustering multilingual news articles for media
monitoring is essential to follow news stories in real time. Most approaches to
this task focus on high-resource languages (mostly English), with low-resource
languages being disregarded. With that in mind, we present a much simpler
online system that is able to cluster an incoming stream of documents without
depending on language-specific features. We empirically demonstrate that the
use of multilingual contextual embeddings as the document representation
significantly improves clustering quality. We challenge previous crosslingual
approaches by removing the precondition of building monolingual clusters. We
model the clustering process as a set of linear classifiers to aggregate
similar documents, and correct closely-related multilingual clusters through
merging in an online fashion. Our system achieves state-of-the-art results on a
multilingual news stream clustering dataset, and we introduce a new evaluation
for zero-shot news clustering in multiple languages. We make our code available
as open-source.
