  Detecting humor is a challenging task since words might share multiple
valences and, depending on the context, the same words can be even used in
offensive expressions. Neural network architectures based on Transformer obtain
state-of-the-art results on several Natural Language Processing tasks,
especially text classification. Adversarial learning, combined with other
techniques such as multi-task learning, aids neural models learn the intrinsic
properties of data. In this work, we describe our adversarial multi-task
network, AMTL-Humor, used to detect and rate humor and offensive texts from
Task 7 at SemEval-2021. Each branch from the model is focused on solving a
related task, and consists of a BiLSTM layer followed by Capsule layers, on top
of BERTweet used for generating contextualized embeddings. Our best model
consists 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 0.6200
and 0.5318 for Tasks 1b and 2, respectively.
