3 citations · 8 across the 10 of their papers we have counts for
13 papers
The Growing Gains and Pains of Iterative Web Corpora Crawling: Insights from South Slavic CLASSLA-web 2.0 Corpora
Taja Kuzman Pungeršek, Peter Rupnik, Vít 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…
Mići Princ -- A Little Boy Teaching Speech Technologies the Chakavian Dialect
Nikola Ljubešić, Peter Rupnik, Tea Perinčić
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…
Supercharging Agenda Setting Research: The ParlaCAP Dataset of 28 European Parliaments and a Scalable Multilingual LLM-Based Classification
Taja Kuzman Pungeršek, Peter Rupnik, Daniela Širinić +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…
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…
ParlaSpeech 3.0: Richly Annotated Spoken Parliamentary Corpora of Croatian, Czech, Polish, and Serbian
Nikola Ljubešić, 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.…
Global PIQA: Evaluating Commonsense Reasoning Across 100+ Languages and Cultures
Tyler A. Chang, Catherine Arnett, Abdelrahman Sadallah +377
To date, there exist almost no culturally-specific evaluation benchmarks for large language models (LLMs) that cover a large number of languages and cultures. In this paper, we pre…
Identifying Primary Stress Across Related Languages and Dialects with Transformer-based Speech Encoder Models
Nikola Ljubešić, Ivan Porupski, Peter Rupnik
Automating primary stress identification has been an active research field due to the role of stress in encoding meaning and aiding speech comprehension. Previous studies relied ma…
The ParlaSpeech Collection of Automatically Generated Speech and Text Datasets from Parliamentary Proceedings
Nikola Ljubešić, Peter Rupnik, Danijel Koržinek
Recent significant improvements in speech and language technologies come both from self-supervised approaches over raw language data as well as various types of explicit supervisio…
Language Models on a Diet: Cost-Efficient Development of Encoders for Closely-Related Languages via Additional Pretraining
Nikola Ljubešić, Vít Suchomel, Peter Rupnik +2
The world of language models is going through turbulent times, better and ever larger models are coming out at an unprecedented speed. However, we argue that, especially for the sc…
Do Language Models Care About Text Quality? Evaluating Web-Crawled Corpora Across 11 Languages
Rik van Noord, Taja Kuzman, Peter Rupnik +4
Large, curated, web-crawled corpora play a vital role in training language models (LMs). They form the lion's share of the training data in virtually all recent LMs, such as the we…
The ParlaSent Multilingual Training Dataset for Sentiment Identification in Parliamentary Proceedings
Michal Mochtak, Peter Rupnik, Nikola Ljubešić
The paper presents a new training dataset of sentences in 7 languages, manually annotated for sentiment, which are used in a series of experiments focused on training a robust sent…
The ParlaSent-BCS dataset of sentiment-annotated parliamentary debates from Bosnia-Herzegovina, Croatia, and Serbia
Michal Mochtak, Peter Rupnik, Nikola Ljubešič
Expression of sentiment in parliamentary debates is deemed to be significantly different from that on social media or in product reviews. This paper adds to an emerging body of res…
The GINCO Training Dataset for Web Genre Identification of Documents Out in the Wild
Taja Kuzman, Peter Rupnik, Nikola Ljubešić
This paper presents a new training dataset for automatic genre identification GINCO, which is based on 1,125 crawled Slovenian web documents that consist of 650 thousand words. Eac…