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.

通过使用多语种词典进行预训练，本文在低资源语言中增强了多语种语言模型的功能，在34种语言中进行了零样本情感分析任务，包括6种高/中资源语言，25种低资源语言和3个代码混合数据集。结果显示，使用多语种词典进行预训练能够实现更好的零样本性能，而不使用句级情感数据，相比于基于英文情感数据集和大语言模型如GPT-3.5，BLOOMZ和XGLM的微调模型。这些发现适用于涉及高资源语言的未知低资源语言到代码混合场景。

低资源语言中使用多语情感词典的零样本情感分析