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Unsupervised Embeddings with Graph Auto-Encoders for Multi-domain and Multilingual Hate Speech Detection
Hate speech detection is a prominent and challenging task, since hate messages are often expressed in subtle ways and with characteristics that may vary depending on the author.
Vicinal Risk Minimization for Few-Shot Cross-lingual Transfer in Abusive Language Detection
In this work, we resort to data augmentation and continual pre-training for domain adaptation to improve cross-lingual abusive language detection.
Zero and Few-shot Learning for Author Profiling
We find that entailment-based models out-perform supervised text classifiers based on roberta-XLM and that we can reach 80% of the accuracy of previous approaches using less than 50\% of the training data on average.
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