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.

通过跨语言情感分类器，我们通过在资源丰富的语言（英语）上进行训练，并将其应用于资源匮乏的和濒危的语言，成功地实现了情感的跨语言传递和分析。