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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.02113 |
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| _version_ | 1866910317871628288 |
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| author | Koto, Fajri Beck, Tilman Talat, Zeerak Gurevych, Iryna Baldwin, Timothy |
| author_facet | Koto, Fajri Beck, Tilman Talat, Zeerak Gurevych, Iryna Baldwin, Timothy |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_02113 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Zero-shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment Lexicon Koto, Fajri Beck, Tilman Talat, Zeerak Gurevych, Iryna Baldwin, Timothy Computation and Language 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. |
| title | Zero-shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment Lexicon |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2402.02113 |