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Detecting humor is a challenging task since words might share multiplevalences and, depending on the context, the same words can be even used inoffensive expressions. Neural network architectures based on Transformer obtainstate-of-the-art results on several Natural Language Processing tasks,especially text classification. Adversarial learning, combined with othertechniques such as multi-task learning, aids neural models learn the intrinsicproperties of data. In this work, we describe our adversarial multi-tasknetwork, AMTL-Humor, used to detect and rate humor and offensive texts fromTask 7 at SemEval-2021. Each branch from the model is focused on solving arelated task, and consists of a BiLSTM layer followed by Capsule layers, on topof BERTweet used for generating contextualized embeddings. Our best modelconsists of an ensemble of all tested configurations, and achieves a 95.66%F1-score and 94.70% accuracy for Task 1a, while obtaining RMSE scores of 0.6200and 0.5318 for Tasks 1b and 2, respectively.

UPB at SemEval-2021 Task 7: Adversarial Multi-Task Learning for Detecting and Rating Humor and Offense