基于提示符的微调,使用多语言转换器进行语言独立的情绪分析.
Faizad Ullah1, Safiullah Faizullah2, Imdad Ullah Khan1
1Department of Computer Science, LUMS, Lahore, Pakistan.
Scientific reports
|July 2, 2025
概括
基于提示符的微调与变压器模型使语言独立的情绪分析. 这种使用XLM-RoBERTa的方法,通过最小的数据实现了高精度,在各种语言中表现优于传统方法.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 全球数字通信需要多语言的用户情绪分析.
- 现有的方法在论挖掘和社交媒体监控中与语言独立性作斗争.
- 变压器模型为文本理解提供了先进的功能.
研究的目的:
- 开发和评估一种语言独立的情感分析框架.
- 将基于提示的微调与经典和深度学习方法进行比较.
- 评估多语言变压器模型在不同语言中的性能.
主要方法:
- 实现了经典机器学习 (SVM,物流回归) 与TF-IDF.
- 开发了一种混合深度学习模型,将LSTM和CNN结合起来.
- 微调的多语言变压器模型 (BERT-base-multilingual,XLM-RoBERTa) 使用前和cloze式提示符进行语言独立的情绪分类.
主要成果:
- 基于提示的微调的XLM-RoBERTa显著优于经典和深度学习方法.
- 前提示实现了与标准微调相比的性能,每类仅使用32个训练示例.
- 统一框架在八种不同类型的语言中表现出有效性.
结论:
- 基于提示的微调是一种高度有效的策略,用于可扩展的,语言独立的情绪分析.
- 变压器模型,特别是XLM-RoBERTa,对跨语言情绪分类有很大的前景.
- 这种方法减少了训练有效的多语言情绪分析模型的数据需求.
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