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

Fajri Koto, Tilman Beck, Zeerak Talat, Iryna Gurevych, Timothy Baldwin


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.
Anthology ID:
2024.eacl-long.18
Volume:
Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
March
Year:
2024
Address:
St. Julian’s, Malta
Editors:
Yvette Graham, Matthew Purver
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
298–320
Language:
URL:
https://aclanthology.org/2024.eacl-long.18
DOI:
Bibkey:
Cite (ACL):
Fajri Koto, Tilman Beck, Zeerak Talat, Iryna Gurevych, and Timothy Baldwin. 2024. Zero-shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment Lexicon. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 298–320, St. Julian’s, Malta. Association for Computational Linguistics.
Cite (Informal):
Zero-shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment Lexicon (Koto et al., EACL 2024)
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PDF:
https://aclanthology.org/2024.eacl-long.18.pdf
Video:
 https://aclanthology.org/2024.eacl-long.18.mp4