A BERT-based Approach for Automatic Humor Detection and Scoring

In this paper we report our participation in the 2019 HAHA task where a corpus of crowd-annotated tweets is provided and required to tell if a tweet is a joke or not and predict a funniness score value for a tweet. Our approach utilizes BERT, a multi-layer bidirectional transformer encoder which can help learn deep bi-directional representations, and the pretrained model is fine-tuned on training data for HAHA task. The representation of a tweet is fed into an output layer for classification. To predict the funniness score, we apply another output layer to generate scores by using float labels and train it with the mean squad error between the prediction scores and the labels. Our best F-Score on the test set for Task 1 is 0.784 and RMSE for Task 2 is 0.910. We find that our approach is competitive and applicable to multilingual text classification tasks.