Emotion Classification in Low and Moderate Resource Languages
Shabnam Tafreshi Shubham Vatsal Mona Diab
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.