Assessing Sense of Humor in Edited News Headlines Using ELMo and NB https://drive.google.com/file/d/1bZJmr1YGQauAn6kXqQnHmFkOIptgoGgR/view
The task consists of estimating the hilariousness of
news headlines that have been modified manually by humans using micro-edit changes to make them funny. Our approach is constructed to improve on a couple of aspects; preprocessing with an emphasis on humor sense detection, using embeddings from state-of-the-art language model (ELMo), and ensembling the results came up with using machine learning model Na ̈ıve Bayes (NB) with a deep learning pretrained models. ELMo-NB participation has scored (0.5642) on the competition leader board, where results were measured by Root Mean Squared Error (RMSE).