Abstract/Summary

Humor detection is an interesting but difficult task in NLP. Humor might not be obvious in text because it may be embedded into context, hide behind the literal meaning of the phrase and require prior knowledge to understand. We explored different shallow and deep methods to create a humour detection classifier for task 7-1a. Models like Logistic Regression, LSTM, MLP, CNN were used, and pre-trained models like DistilBert were introduced to generate accurate vector representation for textual data. We focused on applying a multi-scale strategy on modelling, and compared different models. Our best model is the DistilBert+MultiScale CNN which used different sizes of CNN kernel to get multiple scales of features. This method achieved 93.7% F1-score and 92.1% accuracy on the test set.