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Computer Science > Computation and Language

arXiv:2204.13418 (cs)
[Submitted on 28 Apr 2022]

Title:Simplifying Multilingual News Clustering Through Projection From a Shared Space

Authors:João Santos, Afonso Mendes, Sebastião Miranda
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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 (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.
Comments: 10 pages, 1 figure
Subjects: Computation and Language (cs.CL); Information Retrieval (cs.IR)
Cite as: arXiv:2204.13418 [cs.CL]
  (or arXiv:2204.13418v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2204.13418
Journal reference: Proceedings of Text2Story - Fifth Workshop on Narrative Extraction From Texts held in conjunction with the 44th European Conference on Information Retrieval (ECIR 2022) Stavanger, Norway, April 10, 2022 (pp. 015-024)

Submission history

From: João Santos [view email]
[v1] Thu, 28 Apr 2022 11:32:49 UTC (191 KB)
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