10 papers
Supercharging Agenda Setting Research: The ParlaCAP Dataset of 28 European Parliaments and a Scalable Multilingual LLM-Based Classification
Taja Kuzman PungerÅ¡ek, Peter Rupnik, Daniela Å iriniÄ +1
This paper introduces ParlaCAP, a large-scale dataset for analyzing parliamentary agenda setting across Europe, and proposes a cost-effective method for building domain-specific po…
ParlaSpeech 3.0: Richly Annotated Spoken Parliamentary Corpora of Croatian, Czech, Polish, and Serbian
Nikola LjubeÅ¡iÄ, Peter Rupnik, Ivan Porupski +1
ParlaSpeech is a collection of spoken parliamentary corpora currently spanning four Slavic languages - Croatian, Czech, Polish and Serbian - all together 6 thousand hours in size.…
The Growing Gains and Pains of Iterative Web Corpora Crawling: Insights from South Slavic CLASSLA-web 2.0 Corpora
Taja Kuzman PungerÅ¡ek, Peter Rupnik, VÃt Suchomel +1
Crawling national top-level domains has proven to be highly effective for collecting texts in less-resourced languages. This approach has been recently used for South Slavic langua…
State of the Art in Text Classification for South Slavic Languages: Fine-Tuning or Prompting?
Taja Kuzman Pungeršek, Peter Rupnik, Ivan Porupski +2
Until recently, fine-tuned BERT-like models provided state-of-the-art performance on text classification tasks. With the rise of instruction-tuned decoder-only models, commonly kno…
MiÄi Princ -- A Little Boy Teaching Speech Technologies the Chakavian Dialect
Nikola LjubeÅ¡iÄ, Peter Rupnik, Tea PerinÄiÄ
This paper documents our efforts in releasing the printed and audio book of the translation of the famous novel The Little Prince into the Chakavian dialect, as a computer-readable…
LLM Teacher-Student Framework for Text Classification With No Manually Annotated Data: A Case Study in IPTC News Topic Classification
Taja Kuzman, Nikola LjubeÅ¡iÄ
With the ever-increasing number of news stories available online, classifying them by topic, regardless of the language they are written in, has become crucial for enhancing reader…