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：代码混合是一种在文本或语音中混合两种或多种语言的已被充分研究的语言现象。为训练处理代码混合的计算模型已构建了多个数据集。尽管多语言混合现象十分普遍，但现有大多数数据集仅包含两种语言之间的代码混合数据。本文介绍了SentMix-3L，一个面向情感分析的新型数据集，包含孟加拉语、英语和印地语三种语言之间的代码混合数据。我们利用SentMix-3L进行了全面评估，结果表明基于GPT-3.5的零样本提示方法在SentMix-3L上优于所有基于Transformer的模型。