This paper presents our bootstrapping efforts of producing the first large freely available Croatian automatic speech recognition (ASR) dataset, 1,816 hours in size, obtained from parliamentary transcripts and recordings from the ParlaMint corpus. The bootstrapping approach to the dataset building relies on a commercial ASR system for initial data alignment, and building a multilingual-transformer-based ASR system from the initial data for full data alignment. Experiments on the resulting dataset show that the difference between the spoken content and the parliamentary transcripts is present in ~4-5% of words, which is also the word error rate of our best-performing ASR system. Interestingly, fine-tuning transformer models on either normalized or original data does not show a difference in performance. Models pre-trained on a subset of raw speech data consisting of Slavic languages only show to perform better than those pre-trained on a wider set of languages. With our public release of data, models and code, we are paving the way forward for the preparation of the multi-modal corpus of Croatian parliamentary proceedings, as well as for the development of similar free datasets, models and corpora for other under-resourced languages.

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 650,000 words. Each document was manually annotated for genre with a new annotation schema that builds upon existing schemata, having primarily clarity of labels and inter-annotator agreement in mind. The dataset consists of various challenges related to web-based data, such as machine translated content, encoding errors, multiple contents presented in one document etc., enabling evaluation of classifiers in realistic conditions. The initial machine learning experiments on the dataset show that (1) pre-Transformer models are drastically less able to model the phenomena, with macro F1 metrics ranging around 0.22, while Transformer-based models achieve scores of around 0.58, and (2) multilingual Transformer models work as well on the task as the monolingual models that were previously proven to be superior to multilingual models on standard NLP tasks.

We introduce the project “MaCoCu: Massive collection and curation of monolingual and bilingual data: focus on under-resourced languages”, funded by the Connecting Europe Facility, which is aimed at building monolingual and parallel corpora for under-resourced European languages. The approach followed consists of crawling large amounts of textual data from carefully selected top-level domains of the Internet, and then applying a curation and enrichment pipeline. In addition to corpora, the project will release successive versions of the free/open-source web crawling and curation software used.