Iztok Kosem


2024

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SENTA: Sentence Simplification System for Slovene
Aleš Žagar | Matej Klemen | Marko Robnik-Šikonja | Iztok Kosem
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Ensuring universal access to written content, regardless of users’ language proficiency and cognitive abilities, is of paramount importance. Sentence simplification, which involves converting complex sentences into more accessible forms while preserving their meaning, plays a crucial role in enhancing text accessibility. This paper introduces SENTA, a system for sentence simplification in Slovene. The system consists of two components. First, a neural classifier identifies sentences that require simplification, and second, a large Slovene language model based on T5 architecture is fine-tuned to transform complex texts into a simpler form, achieving an excellent SARI score of 41. Both automatic and qualitative evaluations provide important insights into the problem, highlighting areas for future research in multilingual applications, and fluency maintenance. Finally, SENTA is integrated into a freely accessible, user-friendly user interface, offering a valuable service to less-fluent Slovene users.

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Towards an Ideal Tool for Learner Error Annotation
Špela Arhar Holdt | Tomaž Erjavec | Iztok Kosem | Elena Volodina
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Annotation and analysis of corrections in learner corpora have always presented technical challenges, mainly on account of the fact that until now there has not been any standard tool available, and that original and corrected versions of texts have been mostly stored together rather than treated as individual texts. In this paper, we present CJVT Svala 1.0, the Slovene version of the SVALA tool, which was originally used for the annotation of Swedish learner language. The localisation into Slovene resulted in the development of several new features in SVALA such as the support for multiple annotation systems, localisation into other languages, and the support for more complex annotation systems. Adopting the parallel aligned approach to text visualisation and annotation, as well as storing the data, combined with the tool supporting this, i.e. SVALA, are proposed as new standards in Learner Corpus Research.

2020

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Creating Expert Knowledge by Relying on Language Learners: a Generic Approach for Mass-Producing Language Resources by Combining Implicit Crowdsourcing and Language Learning
Lionel Nicolas | Verena Lyding | Claudia Borg | Corina Forascu | Karën Fort | Katerina Zdravkova | Iztok Kosem | Jaka Čibej | Špela Arhar Holdt | Alice Millour | Alexander König | Christos Rodosthenous | Federico Sangati | Umair ul Hassan | Anisia Katinskaia | Anabela Barreiro | Lavinia Aparaschivei | Yaakov HaCohen-Kerner
Proceedings of the Twelfth Language Resources and Evaluation Conference

We introduce in this paper a generic approach to combine implicit crowdsourcing and language learning in order to mass-produce language resources (LRs) for any language for which a crowd of language learners can be involved. We present the approach by explaining its core paradigm that consists in pairing specific types of LRs with specific exercises, by detailing both its strengths and challenges, and by discussing how much these challenges have been addressed at present. Accordingly, we also report on on-going proof-of-concept efforts aiming at developing the first prototypical implementation of the approach in order to correct and extend an LR called ConceptNet based on the input crowdsourced from language learners. We then present an international network called the European Network for Combining Language Learning with Crowdsourcing Techniques (enetCollect) that provides the context to accelerate the implementation of this generic approach. Finally, we exemplify how it can be used in several language learning scenarios to produce a multitude of NLP resources and how it can therefore alleviate the long-standing NLP issue of the lack of LRs.

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Gigafida 2.0: The Reference Corpus of Written Standard Slovene
Simon Krek | Špela Arhar Holdt | Tomaž Erjavec | Jaka Čibej | Andraz Repar | Polona Gantar | Nikola Ljubešić | Iztok Kosem | Kaja Dobrovoljc
Proceedings of the Twelfth Language Resources and Evaluation Conference

We describe a new version of the Gigafida reference corpus of Slovene. In addition to updating the corpus with new material and annotating it with better tools, the focus of the upgrade was also on its transformation from a general reference corpus, which contains all language variants including non-standard language, to the corpus of standard (written) Slovene. This decision could be implemented as new corpora dedicated specifically to non-standard language emerged recently. In the new version, the whole Gigafida corpus was deduplicated for the first time, which facilitates automatic extraction of data for the purposes of compilation of new lexicographic resources such as the collocations dictionary and the thesaurus of Slovene.