In this paper, we present our submission for SemEval-2020 competition subtask 1 in Task 7 (Hossain et al., 2020a): Assessing Humor in Edited News Headlines. 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 Naive 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).
展开▼
