Improving multilingual language models capabilities in low-resource languages is generally difficult due to the scarcity of large-scale data in those languages. In this paper, we relax the reliance on texts in low-resource languages by using multilingual lexicons in pretraining to enhance multilingual capabilities. Specifically, we focus on zero-shot sentiment analysis tasks across 34 languages, including 6 high/medium-resource languages, 25 low-resource languages, and 3 code-switching datasets. We demonstrate that pretraining using multilingual lexicons, without using any sentence-level sentiment data, achieves superior zero-shot performance compared to models fine-tuned on English sentiment datasets, and large language models like GPT--3.5, BLOOMZ, and XGLM. These findings are observable for unseen low-resource languages to code-mixed scenarios involving high-resource languages.
@article{arxiv.2402.02113,
title = {Zero-shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment Lexicon},
author = {Fajri Koto and Tilman Beck and Zeerak Talat and Iryna Gurevych and Timothy Baldwin},
journal= {arXiv preprint arXiv:2402.02113},
year = {2024}
}