TL;DR
This paper introduces a large-scale synthetic multilingual dataset for multi-label emotion classification across 23 languages, and evaluates transformer models, achieving high performance and multilingual support.
Contribution
It creates and uses a synthetic dataset covering 23 languages for emotion classification, enabling multilingual training and evaluation at scale.
Findings
XLM-R-Large achieves 0.868 F1-micro on in-domain test set.
Models perform competitively on zero-shot English benchmarks.
The best base model is publicly available at the provided URL.
Abstract
Emotion classification in multilingual settings remains constrained by the scarcity of annotated data: existing corpora are predominantly English, single-label, and cover few languages. We address this gap by constructing a large-scale synthetic training corpus of over 1M multi-label samples (50k per language) across 23 languages: Arabic, Bengali, Dutch, English, French, German, Hindi, Indonesian, Italian, Japanese, Korean, Mandarin, Polish, Portuguese, Punjabi, Russian, Spanish, Swahili, Tamil, Turkish, Ukrainian, Urdu, and Vietnamese, covering 11 emotion categories using culturally-adapted generation and programmatic quality filtering. We train and compare six multilingual transformer encoders, from DistilBERT (135M parameters) to XLM-R-Large (560M parameters), under identical conditions. On our in-domain test set, XLM-R-Large achieves 0.868 F1-micro and 0.987 AUC-micro. To validate…
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