Simplifying Multilingual News Clustering Through Shared Space Projection

Priberam Labs

Abstract: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, 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.Bio:João Santos is a Research Scientist at Priberam currently working on applications of Natural Language Processing for fact-checking. He previously obtained his MSc degree in Computer Science and Engineering at Instituto Superior Técnico, and his thesis was focused on multi-agent conversational systems. João&#x27;s research interests include dialogue systems and narrative extraction from texts.<a href="https://www.priberam.com/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">www.priberam.com</a>

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Simplifying Multilingual News Clustering Through Shared Space Projection

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Simplifying Multilingual News Clustering Through Shared Space Projection

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