LMML at SemEval-2020 Task 7: Siamese Transformers for Rating Humor in Edited News Headlines

Abstract

This paper contains a description of my solution to the problem statement of SemEval 2020: Assessing the Funniness of Edited News Headlines. I propose a Siamese Transformer based approach, coupled with a Global Attention mechanism that makes use of contextual embeddings and focus words, to generate important features that are fed to a 2 layer perceptron to rate the funniness of the edited headline. I detail various experiments to show the performance of the system. The proposed approach outperforms a baseline Bi-LSTM architecture and finished 5th (out of 49 teams) in sub-task 1 and 4th (out of 32 teams) in sub-task 2 of the competition and was the best non-ensemble model in both tasks.