Emotion Classification in Low and Moderate Resource Languages
CoRR(2024)
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. 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.
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