用大模型生成混合语种数据，提升情感分析效果
Leveraging Large Language Models for Code-Mixed Data Augmentation in Sentiment Analysis
- 用大模型生成自然流畅的语码混杂文本
- 西英混合语中F1提升9.32%，马来语英混合需低基线时才有效
- 低成本生成真实感强的合成数据，适合资源少的语言
语码混杂（CM）在多语言社会中普遍存在，但因其复杂性和数据稀缺，给自然语言处理带来挑战。本文提出利用大语言模型生成合成的语码混杂数据，并用于提升特定任务模型在语码混杂情感分析中的性能。实验显示，在西班牙语-英语场景中，合成数据使F1分数提升9.32%，优于以往增强方法；而在马拉雅拉姆语-英语场景中，仅当基线表现较差时合成数据才有帮助；若已有较强自然数据，则额外合成数据收益有限。人工评估证实该方法能高效生成自然流畅的语码混杂句子，是一种简单且成本低廉的数据增强方式。研究结果表明，对大模型进行少样本提示是语码混杂数据增强的有前景方法，对提升情感分析性能具有重要意义，有助于社会影响力系统的建设。
原文摘要 · Abstract (English)
Code-mixing (CM), where speakers blend languages within a single expression, is prevalent in multilingual societies but poses challenges for natural language processing due to its complexity and limited data. We propose using a large language model to generate synthetic CM data, which is then used to enhance the performance of task-specific models for CM sentiment analysis. Our results show that in Spanish-English, synthetic data improved the F1 score by 9.32%, outperforming previous augmentation techniques. However, in Malayalam-English, synthetic data only helped when the baseline was low; with strong natural data, additional synthetic data offered little benefit. Human evaluation confirmed that this approach is a simple, cost-effective way to generate natural-sounding CM sentences, particularly beneficial for low baselines. Our findings suggest that few-shot prompting of large language models is a promising method for CM data augmentation and has significant impact on improving sentiment analysis, an important element in the development of social influence systems.
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