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Keyword extraction is the task of retrieving words that are essential to thecontent of a given document. Researchers proposed various approaches to tacklethis problem. At the top-most level, approaches are divided into ones thatrequire training - supervised and ones that do not - unsupervised. In thisstudy, we are interested in settings, where for a language under investigation,no training data is available. More specifically, we explore whether pretrainedmultilingual language models can be employed for zero-shot cross-lingualkeyword extraction on low-resource languages with limited or no availablelabeled training data and whether they outperform state-of-the-art unsupervisedkeyword extractors. The comparison is conducted on six news article datasetscovering two high-resource languages, English and Russian, and fourlow-resource languages, Croatian, Estonian, Latvian, and Slovenian. We findthat the pretrained models fine-tuned on a multilingual corpus coveringlanguages that do not appear in the test set (i.e. in a zero-shot setting),consistently outscore unsupervised models in all six languages.

Out of Thin Air: Is Zero-Shot Cross-Lingual Keyword Detection Better Than Unsupervised?