Simplifying Multilingual News Clustering Through Projection From a Shared Space
This addresses the challenge of real-time media monitoring across languages, particularly benefiting low-resource languages often disregarded in existing approaches.
The paper tackles the problem of clustering multilingual news articles by proposing a simpler online system that uses multilingual contextual embeddings, achieving state-of-the-art results on a multilingual news stream clustering dataset and introducing a new evaluation for zero-shot news clustering.
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.