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

Boshko Koloski, Senja Pollak, Blaž Škrlj, Matej Martinc

Abstract
Keyword extraction is the task of retrieving words that are essential to the content of a given document. Researchers proposed various approaches to tackle this problem. At the top-most level, approaches are divided into ones that require training - supervised and ones that do not - unsupervised. In this study, we are interested in settings, where for a language under investigation, no training data is available. More specifically, we explore whether pretrained multilingual language models can be employed for zero-shot cross-lingual keyword extraction on low-resource languages with limited or no available labeled training data and whether they outperform state-of-the-art unsupervised keyword extractors. The comparison is conducted on six news article datasets covering two high-resource languages, English and Russian, and four low-resource languages, Croatian, Estonian, Latvian, and Slovenian. We find that the pretrained models fine-tuned on a multilingual corpus covering languages that do not appear in the test set (i.e. in a zero-shot setting), consistently outscore unsupervised models in all six languages.
Anthology ID:
2022.lrec-1.42
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
400–409
Language:
URL:
https://aclanthology.org/2022.lrec-1.42
DOI:
Bibkey:
Cite (ACL):
Boshko Koloski, Senja Pollak, Blaž Škrlj, and Matej Martinc. 2022. Out of Thin Air: Is Zero-Shot Cross-Lingual Keyword Detection Better Than Unsupervised?. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 400–409, Marseille, France. European Language Resources Association.
Cite (Informal):
Out of Thin Air: Is Zero-Shot Cross-Lingual Keyword Detection Better Than Unsupervised? (Koloski et al., LREC 2022)
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PDF:
https://preview.aclanthology.org/nschneid-patch-1/2022.lrec-1.42.pdf
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