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Multi-task Learning for Cross-Lingual Sentiment Analysis

2022-12-14 11:29:03
Gaurish Thakkar, Nives Mikelic Preradovic, Marko Tadic

Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2212.07160

PDF

https://arxiv.org/pdf/2212.07160.pdf


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