Abstract
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 Naïve 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).
Cite
CITATION STYLE
Khwaileh, E., & Al-As’ad, M. (2020). ELMo-NB at SemEval-2020 Task 7: Assessing Sense of Humor in Edited News Headlines Using ELMo and NB. In 14th International Workshops on Semantic Evaluation, SemEval 2020 - co-located 28th International Conference on Computational Linguistics, COLING 2020, Proceedings (pp. 1001–1007). International Committee for Computational Linguistics. https://doi.org/10.18653/v1/2020.semeval-1.130
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