Code-mixing is a well-studied linguistic phenomenon when two or more languages are mixed in text or speech. Several datasets have been build with the goal of training computational models for code-mixing. Although it is very common to observe code-mixing with multiple languages, most datasets available contain code-mixed between only two languages. In this paper, we introduce SentMix-3L, a novel dataset for sentiment analysis containing code-mixed data between three languages Bangla, English, and Hindi. We carry out a comprehensive evaluation using SentMix-3L. We show that zero-shot prompting with GPT-3.5 outperforms all transformer-based models on SentMix-3L.

翻译：代码混合是一种被广泛研究的语言现象，指文本或语音中混合使用两种或更多语言。为了训练代码混合的计算模型，研究者已构建了多个数据集。尽管多语言代码混合现象十分常见，但现有数据集大多仅包含两种语言的混合数据。本文介绍了SentMix-3L，一个新颖的情感分析数据集，包含孟加拉语、英语和印地语三种语言的代码混合数据。我们利用SentMix-3L进行了全面评估，结果表明基于GPT-3.5的零样本提示方法性能优于所有Transformer模型。