This paper describes our system designed for SemEval-2023 Task 12: Sentiment analysis for African languages. The challenge faced by this task is the scarcity of labeled data and linguistic resources in low-resource settings. To alleviate these, we propose a generalized multilingual system SACL-XLMR for sentiment analysis on low-resource languages. Specifically, we design a lexicon-based multilingual BERT to facilitate language adaptation and sentiment-aware representation learning. Besides, we apply a supervised adversarial contrastive learning technique to learn sentiment-spread structured representations and enhance model generalization. Our system achieved competitive results, largely outperforming baselines on both multilingual and zero-shot sentiment classification subtasks. Notably, the system obtained the 1st rank on the zero-shot classification subtask in the official ranking. Extensive experiments demonstrate the effectiveness of our system.

翻译：本文描述了我们为SemEval-2023任务12：非洲语言情感分析所设计的系统。该任务面临的挑战是低资源环境下标注数据和语言资源的稀缺性。针对这些问题，我们提出了一种通用型多语言系统SACL-XLMR，用于低资源语言的情感分析。具体而言，我们设计了一种基于词典的多语言BERT，以促进语言适应和情感感知表示学习。此外，我们引入了监督对抗对比学习技术，学习情感扩散的层次化表示，从而提升模型泛化能力。我们的系统取得了竞争性结果，在多语言和零样本情感分类子任务上均大幅超越基线模型。值得注意的是，在官方排行榜中，该系统在零样本分类子任务上位列第一。大量实验验证了系统的有效性。