UCAS-IIE-NLP at SemEval-2023 Task 12: Enhancing Generalization of Multilingual BERT for Low-resource Sentiment Analysis

Dou Hu, Lingwei Wei, Yaxin Liu, Wei Zhou, Songlin Hu
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引用次数: 1

Abstract

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.
任务12:在低资源情感分析中增强多语言BERT的泛化
本文描述了我们为SemEval-2023任务12设计的系统:非洲语言的情感分析。该任务面临的挑战是在低资源环境下标记数据和语言资源的稀缺性。为了缓解这些问题,我们提出了一个用于低资源语言情感分析的通用多语言系统SACL-XLMR。具体来说,我们设计了一个基于词典的多语言BERT来促进语言适应和情感感知表征学习。此外,我们还应用了一种监督对抗对比学习技术来学习情感传播的结构化表征,并增强了模型的泛化。我们的系统取得了有竞争力的结果,在多语言和零概率情绪分类子任务上都大大优于基线。值得注意的是,在官方排名中,该系统在零射击分类子任务上获得了第一名。大量的实验证明了该系统的有效性。
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