论文标题
多任务学习用于跨语言分析
Multi-task Learning for Cross-Lingual Sentiment Analysis
论文作者
论文摘要
本文介绍了使用零拍的新闻文章的跨语性情感分析。该研究旨在使用Slovene数据集对克罗地亚新闻文章进行积极，负和中性情绪进行分类。该系统基于一种基于三语的BERT模型，该模型采用三种语言培训：英语，斯洛文尼亚，克罗地亚语。本文使用两种语言的数据集分析了不同的设置，并提出了一个简单的多任务模型来执行情感分类。评估是使用克罗地亚和斯洛文尼亚的单个任务和多任务实验中的几次射击和零击场景进行的。
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.
