Zero-shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment Lexicon

Authors

Fajri Koto, Mohamed bin Zayed University of Artificial IntelligenceFollow
Tilman Beck, Technische Universität Darmstadt
Zeerak Talat, Mohamed bin Zayed University of Artificial IntelligenceFollow
Iryna Gurevych, Mohamed bin Zayed University of Artificial IntelligenceFollow
Timothy Baldwin, Mohamed bin Zayed University of Artificial IntelligenceFollow

Document Type

Conference Proceeding

Publication Title

EACL 2024 - 18th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference

Abstract

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.

First Page

298

Last Page

320

Publication Date

1-1-2024

Recommended Citation

F. Koto et al., "Zero-shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment Lexicon," EACL 2024 - 18th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference, vol. 1, pp. 298 - 320, Jan 2024.