低资源和中资源语言中的情感分类
Emotion Classification in Low and Moderate Resource Languages
AI总结：
提出跨语言情感分类器，用英语训练并迁移到低/中资源语言，对比两种迁移方法，在6种语言上优于随机基线，直接迁移效果更好，并为4种语言创建了标注资源。
摘要：
能够分析全球各地人们的情绪状态非常重要。全世界有7100多种活跃使用的语言，为每种语言构建情感分类系统需要大量的人力。特别是对于低资源和濒危语言，构建情感分类系统可能相当具有挑战性。我们提出了一种跨语言情感分类器，在该分类器中，我们使用资源丰富的语言（即我们工作中的英语）训练情感分类器，并将学习迁移到低资源和中资源语言。我们比较和对比了从高资源语言到低资源或中资源语言的两种迁移学习方法。一种方法将高资源语言的标注通过平行语料库投影到低资源和中资源语言，另一种方法则直接从高资源语言迁移到其他语言。我们在6种语言上展示了我们方法的有效性：波斯语、阿拉伯语、西班牙语、伊洛卡诺语、奥里亚语和阿塞拜疆语。我们的结果表明，我们的方法优于随机基线，并成功地在语言间迁移情感。对于所有语言，直接跨语言情感迁移产生了更好的结果。我们还为四种语言创建了带情感标注的资源：波斯语、阿塞拜疆语、伊洛卡诺语和奥里亚语。
英文摘要：
It is important to be able to analyze the emotional state of people around the globe. There are 7100+ active languages spoken around the world and building emotion classification for each language is labor intensive. Particularly for low-resource and endangered languages, building emotion classification can be quite challenging. We present a cross-lingual emotion classifier, where we train an emotion classifier with resource-rich languages (i.e. \textit{English} in our work) and transfer the learning to low and moderate resource languages. We compare and contrast two approaches of transfer learning from a high-resource language to a low or moderate-resource language. One approach projects the annotation from a high-resource language to low and moderate-resource language in parallel corpora and the other one uses direct transfer from high-resource language to the other languages. We show the efficacy of our approaches on 6 languages: Farsi, Arabic, Spanish, Ilocano, Odia, and Azerbaijani. Our results indicate that our approaches outperform random baselines and transfer emotions across languages successfully. For all languages, the direct cross-lingual transfer of emotion yields better results. We also create annotated emotion-labeled resources for four languages: Farsi, Azerbaijani, Ilocano and Odia.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
