用多语言预训练变形器量化文本的效价和觉醒

Gonccalo Azevedo Mendes, Bruno Martins
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引用次数: 2

摘要

对文本中表达的情感的分析有许多应用。分类分析侧重于根据一组预定义的常见类别对情绪进行分类,与之相反,维度方法可以提供一种更细微的方法来区分不同的情绪。然而,文献中对量纲方法的研究较少。考虑到一个价-觉醒维度空间,本工作评估了使用预训练的变形金刚在连续尺度上预测这两个维度,并使用来自多种语言和领域的输入文本。我们特别结合了来自先前研究的多个带注释的数据集,对应于情感词典或短文本文档,并评估了多种大小的模型,并在不同的设置下进行了训练。我们的研究结果表明,模型的大小可以对预测的质量产生重大影响,并且通过微调一个大模型,我们可以自信地预测多种语言的效价和唤醒。我们使代码、模型和支持数据可用。
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Quantifying Valence and Arousal in Text with Multilingual Pre-trained Transformers
The analysis of emotions expressed in text has numerous applications. In contrast to categorical analysis, focused on classifying emotions according to a pre-defined set of common classes, dimensional approaches can offer a more nuanced way to distinguish between different emotions. Still, dimensional methods have been less studied in the literature. Considering a valence-arousal dimensional space, this work assesses the use of pre-trained Transformers to predict these two dimensions on a continuous scale, with input texts from multiple languages and domains. We specifically combined multiple annotated datasets from previous studies, corresponding to either emotional lexica or short text documents, and evaluated models of multiple sizes and trained under different settings. Our results show that model size can have a significant impact on the quality of predictions, and that by fine-tuning a large model we can confidently predict valence and arousal in multiple languages. We make available the code, models, and supporting data.
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