izvorni znanstveni rad
This paper presents a cross-lingual sentiment analysis of news articles using zero-shot and few-shot learning. The study aims to classify the Croatian news articles with the positive, negative, and neu- tral sentiment using the Slovene dataset. The system is based on a trilin- gual BERT-based model trained in three languages: English, Slovene, Croatian. The paper analyses different setups of using datasets in two languages and proposes a simple multi-task model to perform sentiment classification. The evaluation is performed using the few-shot and zero- shot scenarios in single-task and multi-task experiments for Croatian and Slovene.
sentiment analysis ; cross-lingual ; transfer learning ; multi-task learning ; news sentiment ; under-resourced languages
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Ovaj rad istražuje i uspoređuje mogućnosti prosljeđivanja grafičkih kartica na Hyper-V i KVM hipervizorima prema virtualnim strojevima, detaljno opisujući metode, postupke i razlike u arhitekturi koje utječu na…
The ever-growing usage and adaptation of HTTP/3 in web traffic leverages Quick UDP Internet Connections (QUIC) as a transport protocol. QUIC is widely recognized for reduced latency, enhanced…
