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 positive, negative, and neutral sentiments using the Slovene dataset. The system is based on a trilingual BERT-based model trained in three languages: English, Slovene, Croatian. The paper analyses different setups 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.
@article{arxiv.2212.07160,
title = {Multi-task Learning for Cross-Lingual Sentiment Analysis},
author = {Gaurish Thakkar and Nives Mikelic Preradovic and Marko Tadic},
journal= {arXiv preprint arXiv:2212.07160},
year = {2022}
}