Emotion Classification in Low and Moderate Resource Languages
<|reference_start|>Emotion Classification in Low and Moderate Resource Languages: 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.<|reference_end|>
@article{tafreshi2024emotion,
    title={Emotion Classification in Low and Moderate Resource Languages},
    author={Shabnam Tafreshi, Shubham Vatsal, Mona Diab},
    journal={arXiv preprint arXiv:2402.18424},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.18424},
    primaryClass={cs.CL cs.AI cs.LG}
}